Most software engineers are seriously sleeping on how good LLM agents are right now, especially something like Claude Code.
Once you’ve got Claude Code set up, you can point it at your codebase, have it learn your conventions, pull in best practices, and refine everything until it’s basically operating like a super-powered teammate. The real unlock is building a solid set of reusable “skills” plus a few agents for the stuff you do all the time.
For example, we have a custom UI library, and Claude Code has a skill that explains exactly how to use it. Same for how we write Storybooks, how we structure APIs, and basically how we want everything done in our repo. So when it generates code, it already matches our patterns and standards out of the box.
We also had Claude Code create a bunch of ESLint automation, including custom ESLint rules and lint checks that catch and auto-handle a lot of stuff before it even hits review.
Then we take it further: we have a deep code review agent Claude Code runs after changes are made. And when a PR goes up, we have another Claude Code agent that does a full PR review, following a detailed markdown checklist we’ve written for it.
On top of that, we’ve got like five other Claude Code GitHub workflow agents that run on a schedule. One of them reads all commits from the last month and makes sure docs are still aligned. Another checks for gaps in end-to-end coverage. Stuff like that. A ton of maintenance and quality work is just… automated. It runs ridiculously smoothly.
We even use Claude Code for ticket triage. It reads the ticket, digs into the codebase, and leaves a comment with what it thinks should be done. So when an engineer picks it up, they’re basically starting halfway through already.
There is so much low-hanging fruit here that it honestly blows my mind people aren’t all over it. 2026 is going to be a wake-up call.
(used voice to text then had claude reword, I am lazy and not gonna hand write it all for yall sorry!)
I made a similar comment on a different thread, but I think it also fits here: I think the disconnect between engineers is due to their own context. If you work with frontend applications, specially React/React Native/HTML/Mobile, your experience with LLMs is completely different than the experience of someone working with OpenGL, io_uring, libev and other lower level stuff. Sure, Opus 4.5 can one shot Windows utilities and full stack apps, but can't implement a simple shadowing algorithm from a 2003 paper in C++, GLFW, GLAD: https://www.cse.chalmers.se/~uffe/soft_gfxhw2003.pdf
Codex/Claude Code are terrible with C++. It also can't do Rust really well, once you get to the meat of it. Not sure why that is, but they just spit out nonsense that creates more work than it helps me. It also can't one shot anything complete, even though I might feed him the entire paper that explains what the algorithm is supposed to do.
Try to do some OpenGL or Vulkan with it, without using WebGPU or three.js. Try it with real code, that all of us have to deal with every day. SDL, Vulkan RHI, NVRHI. Very frustrating.
Try it with boost, or cmake, or taskflow. It loses itself constantly, hallucinates which version it is working on and ignores you when you provide actual pointers to documentation on the repo.
I've also recently tried to get Opus 4.5 to move the Job system from Doom 3 BFG to the original codebase. Clean clone of dhewm3, pointed Opus to the BFG Job system codebase, and explained how it works. I have also fed it the Fabien Sanglard code review of the job system: https://fabiensanglard.net/doom3_bfg/threading.php
We are not sleeping on it, we are actually waiting for it to get actually useful. Sure, it can generate a full stack admin control panel in JS for my PostgreSQL tables, but is that really "not normal"? That's basic.
We have an in-house, Rust-based proxy server. Claude is unable to contribute to it meaningfully outside of grunt work like minor refactors across many files. It doesn't seem to understand proxying and how it works on both a protocol level and business logic level.
With some entirely novel work we're doing, it's actually a hindrance as it consistently tells us the approach isn't valid/won't work (it will) and then enters "absolutely right" loops when corrected.
I still believe those who rave about it are not writing anything I would consider "engineering". Or perhaps it's a skill issue and I'm using it wrong, but I haven't yet met someone I respect who tells me it's the future in the way those running AI-based companies tell me.
I've had Opus 4.5 hand rolling CUDA kernels and writing a custom event loop on io_uring lately and both were done really well. Need to set up the right feedback loops so it can test its work thoroughly but then it flies.
I'll second this. I'm making a fairly basic iOS/Swift app with an accompanying React-based site. I was able to vibe-code the React site (it isn't pretty, but it works and the code is fairly decent). But I've struggled to get the Swift code to be reliable.
Which makes sense. I'm sure there's lots of training data for React/HTML/CSS/etc. but much less with Swift, especially the newer versions.
I built an open to "game engine" entirely in Lua a many years ago, but relying on many third party libraries that I would bind to with FFI.
I thought I'd revive it, but this time with Vulkan and no third-party dependencies (except for Vulkan)
4.5 Sonet, Opus and Gemini 3.5 flash has helped me write image decoders for dds, png jpg, exr, a wayland window implementation, macOS window implementation, etc.
I find that Gemini 3.5 flash is really good at understanding 3d in general while sonnet might be lacking a little.
All these sota models seem to understand my bespoke Lua framework and the right level of abstraction. For example at the low level you have the generated Vulkan bindings, then after that you have objects around Vulkan types, then finally a high level pipeline builder and whatnot which does not mention Vulkan anywhere.
However with a larger C# codebase at work, they really struggle. My theory is that there are too many files and abstractions so that they cannot understand where to begin looking.
I'm a quite senior frontend using React and even I see Sonnet 4.5 struggle with basic things. Today it wrote my Zod validation incorrectly, mixing up versions, then just decided it wasn't working and attempted to replace the entire thing with a different library.
Have you experimented with all of these things on the latest models (e.g. Opus 4.5) since Nov 2025? They are significantly better at coding than earlier models.
I've found it to be pretty hit-or-miss with C++ in general, but it's really, REALLY bad at 3D graphics code. I've tried to use it to port an OpenGL project to SDL3_GPU, and it really struggled. It would confidently insist that the code it wrote worked, when all you had to do was run it and look at the output to see a blank screen.
I've had pretty good luck with LLM agents coding C. In this case a C compiler that supports a subset of C and targets a customizable microcoded state machine/processor. Then I had Gemini code up a simulator/debugger for the target machine in C++ and it did it in short order and quite successfully - lets you single step through the microcode and examine inputs (and set inputs), outputs & current state - did that in an afternoon and the resulting C++ code looks pretty decent.
I have not tried C++, but Codex did a good job with low-level C code, shaders as well as porting 32 bit to 64 bit assembly drawing routines.
I have also tried it with retro-computing programming with relative success.
From what I've seen, CC has troubles with the latest Swift too, partially because of it being latest and partially because it's so convoluted nowadays.
> It also can't do Rust really well, once you get to the meat of it. Not sure why that is
Because types are proofs and require global correctness, you can't just iterate, fix things locally, and wait until it breaks somewhere else that you also have to fix locally.
I really think a lof of people tried AI coding earlier, got frustrated at the errors and gave up. That's where the rejection of all these doomer predictions comes from.
And I get it. Coding with Claude Code really was prompting something, getting errors, and asking it to fix it. Which was still useful but I could see why a skilled coder adding a feature to a complex codebase would just give up
Opus 4.5 really is at a new tier however. It just...works. The errors are far fewer and often very minor - "careless" errors, not fundamental issues (like forgetting to add "use client" to a nextjs client component.
This was me. I was a huge AI coding detractor on here for a while (you can check my comment history). But, in order to stay informed and not just be that grouchy curmudgeon all the time, I kept up with the models and regularly tried them out. Opus 4.5 is so much better than anything I've tried before, I'm ready to change my mind about AI assistance.
I even gave -True Vibe Coding- a whirl. Yesterday, from a blank directory and text file list of requirements, I had Opus 4.5 build an Android TV video player that could read a directory over NFS, show a grid view of movie poster thumbnails, and play the selected video file on the TV. The result wasn't exactly full-featured Kodi, but it works in the emulator and actual device, it has no memory leaks, crashes, ANRs, no performance problems, no network latency bugs or anything. It was pretty astounding.
Oh, and I did this all without ever opening a single source file or even looking at the proposed code changes while Opus was doing its thing. I don't even know Kotlin and still don't know it.
This is what people are still doing wrong. Tools in a loop people, tools in a loop.
The agent has to have the tools to detect whatever it just created is producing errors during linting/testing/running. When it can do that, I can loop again, fix the error and again - use the tools to see whether it worked.
I _still_ encounter people who think "AI programming" is pasting stuff into ChatGPT on the browser and they complain it hallucinates functions and produces invalid code.
I have been out of the loop for a couple of months (vacation). I tried Claude Opus 4.5 at the end of November 2025 with the corporate Github Copilot subscription in Agent mode and it was awful: basically ignoring code and hallucinating.
My team is using it with Claude Code and say it works brilliantly, so I'll be giving it another go.
How much of the value comes from Opus 4.5, how much comes from Claude Code, and how much comes from the combination?
This was me. I have done a full 180 over the last 12 months or so, from "they're an interesting idea, and technically impressive, but not practically useful" to "holy shit I can have entire days/weeks where I don't write a single line of code".
my issue hasn't been for a long time now that the code they write works or doesn't work. My issues all stem from that it works, but does the wrong thing
> I really think a lof of people tried AI coding earlier, got frustrated at the errors and gave up. That's where the rejection of all these doomer predictions comes from.
It's not just the deficiencies of earlier versions, but the mismatch between the praise from AI enthusiasts and the reality.
I mean maybe it is really different now and I should definitely try uploading all of my employer's IP on Claude's cloud and see how well it works. But so many people were as hyped by GPT-4 as they are now, despite GPT-4 actually being underwhelming.
Too much hype for disappointing results leads to skepticism later on, even when the product has improved.
> Opus 4.5 really is at a new tier however. It just...works.
Literally tried it yesterday. I didn't see a single difference with whatever model Claude Code was using two months ago. Same crippled context window. Same "I'll read 10 irrelevant lines from a file", same random changes etc.
I know someone who is using a vibe coded or at least heavily assisted text editor, praising it daily, while also saying llms will never be productive. There is a lot of dissonance right now.
I teach at a university, and spend plenty of time programming for research and for fun. Like many others, I spent some time on the holidays trying to push the current generation of Cursor, Claude Code, and Codex as far as I could. (They're all very good.)
I had an idea for something that I wanted, and in five scattered hours, I got it good enough to use. I'm thinking about it in a few different ways:
1. I estimate I could have done it without AI with 2 weeks full-time effort. (Full-time defined as >> 40 hours / week.)
2. I have too many other things to do that are purportedly more important that programming. I really can't dedicate to two weeks full-time to a "nice to have" project. So, without AI, I wouldn't have done it at all.
3. I could hire someone to do it for me. At the university, those are students. From experience with lots of advising, a top-tier undergraduate student could have achieved the same thing, had they worked full tilt for a semester (before LLMs). This of course assumes that I'm meeting them every week.
How do you compare Claude Code to Cursor? I'm a Cursor user quietly watching the CC parade with curiosity. Personally, I haven't been able to give up the IDE experience.
This is where the LLM coding shines in my opinion, there's a list of things they are doing very well:
- single scripts. Anything which can be reduced to a single script.
- starting greenfield projects from scratch
- code maintenance (package upgrades, old code...)
- tasks which have a very clear and single definition. This isn't linked to complexity, some tasks can be both very complex but with a single definition.
If your work falls into this list they will do some amazing work (and yours clearly fits that), if it doesn't though, prepare yourself because it will be painful.
The crazy part is, once you have it setup and adapted your workflow, you start to notice all sorts of other "small" things:
claude can call ssh and do system admin tasks. It works amazingly well. I have 3 VM's, which depends on each other (proxmox with openwrt, adguard, unbound), and claude can prove to me that my dns chains works perfectly, my firewalls are perfect etc as claude can ssh into each. Setting up services, diagnosing issues, auditing configs... you name it. Just awesome.
claude can call other sh scripts on the machine, so over time, you can create a bunch of scripts that lets claude one shot certain tasks that would normally eat tokens. It works great. One script per intention - don't have a script do more than one thing.
claude can call the compiler, run the debug executable and read the debug logs.. in real time. So claude can read my android apps debug stream via adb.. or my C# debug console because claude calls the compiler, not me. Just ask it to do it and it will diagnose stuff really quickly.
It can also analyze your db tables (give it readonly sql access), look at the application code and queries, and diagnose performance issues.
The opportunities are endless here. People need to wake up to this.
Claude set up a Raspberry Pi with a display and conference audio device for me to use as an Alexa replacement tied to Home Assistant.
I gave it an ssh key and gave it root.
Then I told it what I wanted, and it did. It asked for me to confirm certain things, like what I could see on screen, whether I could hear the TTS etc. (it was a bit of a surprise when it was suddenly talking to me while I was minding my own business).
It configured everything, while keeping a meticulous log that I can point it at if I want to set up another device, and eventually turn into a runbook if I need to.
I have a /fix-ci-build slash command that instructs Claude how to use `gh` to get the latest build from that specific project's Github Actions and get the logs for the build
In addition there are instructions on how and where to push the possible fixes and how to check the results.
I've yet to encounter a build failure it couldn't fix automatically.
It makes me so exhausted trying to read them... my brain can tell immediately when there's so much redundant information that it just starts shutting itself off.
I still struggle with these things being _too_ good at generating code. They have a tendency to add abstractions, classes, wrappers, factories, builders to things that didn't really need all that. I find they spit out 6 files worth of code for something that really only needed 2-3 and I'm spending time going back through simplifying.
There are times those extra layers are worth it but it seems LLMs have a bias to add them prematurely and overcomplicate things. You then end up with extra complexity you didn't need.
I think we're entering a world where programmers as such won't really exist (except perhaps in certain niches). Being able to program (and read code, in particular) will probably remain useful, though diminished in value. What will matter more is your ability to actually create things, using whatever tools are necessary and available, and have them actually be useful. Which, in a way, is the same as it ever was. There's just less indirection involved now.
We've been living in that world since the invention of the compiler ("automatic programming"). Few people write machine code any more. If you think of LLMs as a new variety of compiler, a lot of their shortcomings are easier to describe.
> have it learn your conventions, pull in best practices
What do you mean by "have it learn your conventions"? Is there a way to somehow automatically extract your conventions and store it within CLAUDE.md?
> For example, we have a custom UI library, and Claude Code has a skill that explains exactly how to use it. Same for how we write Storybooks, how we structure APIs, and basically how we want everything done in our repo. So when it generates code, it already matches our patterns and standards out of the box.
Did you have to develop these skills yourself? How much work was that? Do you have public examples somewhere?
/init in Claude Code already automatically extracts a bunch, but for something more comprehensive, just tell it which additional types of things you want it to look for and document.
> Did you have to develop these skills yourself? How much work was that? Do you have public examples somewhere?
I don't know about the person above, but I tell Claude to write all my skills and agents for me. With some caveats, you can do this iteratively in a single session ("update the X agent, then re-run it. Repeat until it reliably does Y")
> What do you mean by "have it learn your conventions"?
I'll give you an example: I use ruff to format my python code, which has an opinionated way of formatting certain things. After an initial formatting, Opus 4.5, without prompting, will write code in this same style so that the ruff formatter almost never has anything to do on new commits. Sonnet 4.5 is actually pretty good at this too.
Starting to use Opus 4.5 I'm reduces instrutions in claude.md and just ask claude to look in the codebase to understand the patterns already in use. Going from prompts/docs to instead having code being the "truth". Show don't tell. I've found this patterns has made a huge leap with Opus 4.5.
When I ask Claude to do something, it independently, without me even asking or instructing it to, searches the codebase to understand what the convention is.
I’ve even found it searching node_modules to find the API of non-public libraries.
"Claude, clone this repo https://github.com/repo, review the coding conventions, check out any markdown or readme files. This is an example of coding conventions we want to use on this project"
> Once you’ve got Claude Code set up, you can point it at your codebase, have it learn your conventions, pull in best practices, and refine everything until it’s basically operating like a super-powered teammate. The real unlock is building a solid set of reusable “skills” plus a few agents for the stuff you do all the time.
I agree with this, but I haven't needed to use any advanced features to get good results. I think the simple approach gets you most of the benefits. Broadly, I just have markdown files in the repo written for a human dev audience that the agent can also use.
Basically:
- README.md with a quick start section for devs, descriptions of all build targets and tests, etc. Normal stuff.
- AGENTS.md (only file that's not written for people specifically) that just describes the overall directory structure and has a short step of instructions for the agent: (1) Always read the readme before you start. (2) Always read the relevant design docs before you start. (3) Always run the linter, a build, and tests whenever you make code changes.
- docs/*.md that contain design docs, architecture docs, and user stories, just text. It's important to have these resources anyway, agent or no.
As with human devs, the better the docs/requirements the better the results.
I'd really encourage you to try using agents for tasks that are repeatable and/or wordy but where most of the words are not relevant for ongoing understanding.
It's a tiny step further, and sub-agents provide a massive benefit the moment you're ready to trust the model even a little bit (relax permissions to not have it prompt you for every little thing; review before committing rather than on every file edit) because they limit what goes into the top level context, and can let the model work unassisted for far longer. I now regularly have it run for hours at a time without stopping.
Running and acting on output from the linter is absolutely an example of that which matters even for much shorter runs.
There's no reason to have all the lint output "polluting" the top level context, nor to have the steps the agent needs to take to fix linter issues that can't be auto-fixed by the linter itself. The top level agent should only need to care about whether the linter run passed or failed (and should know it needs to re-run and possibly investigate if it fails).
Just type /agents, select "Create new agent" and describe a task you often do, and then forget about it (or ask Claude to make changes to it for you)
lol does sound like and ad, but is true. Also forgot about hooks use hooks too! I just use voice to text then had claude reword it. Still my real world ideas
All of these things work very well IMO in a professional context.
Especially if you're in a place where a lot of time was spent previously revising PRs for best practices, etc, even for human-submitted code, then having the LLM do that for you that saves a bunch of time. Most humans are bad at following those super-well.
There's a lot of stuff where I'm pretty sure I'm up to at least 2x speed now. And for things like making CLI tools or bash scripts, 10x-20x. But in terms of "the overall output of my day job in total", probably more like 1.5x.
But I think we will need a couple major leaps in tooling - probably deterministic tooling, not LLM tooling - before anyone could responsibly ship code nobody has ever read in situations with millions of dollars on the line (which is different from vibe-coding something that ends up making millions - that's a low-risk-high-reward situation, where big bets on doing things fast make sense. if you're already making millions, dramatic changes like that can become high-risk-low-reward very quickly. In those companies, "I know that only touching these files is 99.99% likely to be completely safe for security-critical functionality" and similar "obvious" intuition makes up for the lack of ability to exhaustively test software in a practical way (even with fuzzers and things), and "i didn't even look at the code" is conceding responsibility to a dangerous degree there.)
Cheaper than hiring another developer, probably. My experience: for a few dollars I was able to extensively refactor a Python codebase in half a day. This otherwise would have taken multiple days of very tedious work.
> Most software engineers are seriously sleeping on how good LLM agents are right now, especially something like Claude Code.
Nobody is sleeping. I'm using LLMs daily to help me in simple coding tasks.
But really where is the hurry? At this point not a few weeks go by without the next best thing since sliced bread to come out. Why would I bother "learning" (and there's really nothing to learn here) some tool/workflow that is already outdated by the time it comes out?
> 2026 is going to be a wake-up call
Do you honestly think a developer not using AI won't be able to adapt to a LLM workflow in, say, 2028 or 2029? It has to be 2026 or... What exactly?
There is literally no hurry.
You're using the equivalent of the first portable CD-player in the 80s: it was huge, clunky, had hiccups, had a huge battery attached to it. It was shiny though, for those who find new things shiny. Others are waiting for a portable CD player that is slim, that buffers, that works fine. And you're saying that people won't be able to learn how to put a CD in a slim CD player because they didn't use a clunky one first.
> Nobody is sleeping. I'm using LLMs daily to help me in simple coding tasks.
That is sleeping.
> But really where is the hurry? At this point not a few weeks go by without the next best thing since sliced bread to come out. Why would I bother "learning" (and there's really nothing to learn here) some tool/workflow that is already outdated by the time it comes out?
You're jumping to conclusions that haven't been justified by any of the development in this space. The learning compounds.
> Do you honestly think a developer not using AI won't be able to adapt to a LLM workflow in, say, 2028 or 2029? It has to be 2026 or... What exactly?
They will, but they'll be competing against people with 2-3 more years of experience in understanding how to leverage these tools.
I think getting proficient at using coding agents effectively takes a few months of practice.
It's also a skill that compounds over time, so if you have two years of experience with them you'll be able to use them more effectively than someone with two months of experience.
In that respect, they're just normal technology. A Python programmer with two years of Python experience will be more effective than a programmer with two months of Python.
"But really where is the hurry?" It just depends on why you're programming. For many of us not learning and using up to date products leads to a disadvantage relative to our competition. I personally would very much rather go back to a world without AI, but we're forced to adapt. I didn't like when pagers/cell phones came out either, but it became clear very quickly not having one put me at a disadvantage at work.
Use Claude Code... to do what? There are multiple layers of people involved in the decision process and they only come up with a few ideas every now and then. Nothing I can't handle. AI helps but it doesn't have to be an agent.
I'm not saying there aren't use cases for agents, just that it's normal that most software engineers are sleeping on it.
Thanks for the example! There's a lot (of boilerplate?) here that I don't understand. Does anyone have good references for catching up to speed what's the purpose of all of these files in the demo?
Came across official anthropic repo on gh actions very relevant to what you mentioned. Your idea on scheduled doc updation using llm is brilliant, I’m stealing this idea.
https://github.com/anthropics/claude-code-action
Never tried coderabbit, just because this is already good enough with Claude Code. It helped us to catch dozens of important issues we wouldn't have caught.
We gave some instructions in the CLAUDE.md doc in the repository - with including a nice personalized roast of the engineer that did the review in the intro and conclusion to make it fun! :)
Basically, when you do a "create PR" from your Claude Code, it will help you getting your Linear ticket (or creating one if missing), ask you some important questions (like: what tests have you done?), create the PR on Github, request the reviewers, and post a "Auto Review" message with your credentials. It's not an actual review per se but this is enough for our small team.
Also new haiku. Not as smart but lighting fast, I've it review code changes impact or if i need a wide but shallow change done I've it scan the files and create a change plan. Saves a lot of time waiting for claude or codex to get their bearing.
If anyone is excited about, and has experience with this kind of stuff, please DM. I have a role open for setting up these kinds of tools and workflows.
I've tried most of the CLI coding tools with the Claude models and I keep coming back to Claude Code. It hits a sweet spot of simple and capable, and right now I'd say it's the best from an "it just works" perspective.
In my experience the CLI tool is part of the secret sauce. I haven't tried switching models per each CLI tool though. I use claude exclusively at work and for personal projects I use claude, codex, gemini.
They are sleeping on it because there is absolutely no incentive to use it.
When needed it can be picked up in a day. Otherwise they are not paid based in tickets solved etc.
If the incentives were properly aligned everyone would already use it
I'm at the point where I say fuck it, let them sleep.
The tech industry just went through an insane hiring craze and is now thinning out. This will help to separate the chaff from the wheat.
I don't know why any company would want to hire "tech" people who are terrified of tech and completely obstinate when it comes to utilizing it. All the people I see downplaying it take a half-assed approach at using it then disparage it when it's not completely perfect.
I started tinkering with LLMs in 2022. First use case, speak in natural english to the llm, give it a json structure, have it decipher the natural language and fill in that json structure (vacation planning app, so you talk to it about where/how you want to vacation and it creates the structured data in the app). Sometimes I'd use it for minor coding fixes (copy and paste a block into chatgpt, fix errors or maybe just ideation). This was all personal project stuff.
At my job we got LLM access in mid/late 2023. Not crazy useful, but still was helpful. We got claude code in 2024. These days I only have an IDE open so I can make quick changes (like bumping up a config parameter, changing a config bool, etc.). I almost write ZERO code now. I usually have 3+ claude code sessions open.
On my personal projects I'm using Gemini + codex primarily (since I have a google account and chatgpt $20/month account). When I get throttled on those I go to claude and pay per token. I'll often rip through new features, projects, ideas with one agent, then I have another agent come through and clean things up, look for code smells, etc. I don't allow the agents to have full unfettered control, but I'd say 70%+ of the time I just blindly accept their changes. If there are problems I can catch them on the MR/PR.
I agree about the low hanging fruit and I'm constantly shocked at the sheer amount of FUD around LLMs. I want to generalize, like I feel like it's just the mid/jr level devs that speak poorly about it, but there's definitely senior/staff level people I see (rarely, mind you) that also don't like LLMs.
I do feel like the online sentiment is slowly starting to change though. One thing I've noticed a lot of is that when it's an anonymous post it's more likely to downplay LLMs. But if I go on linkedin and look at actual good engineers I see them praising LLMs. Someone speaking about how powerful the LLMs are - working on sophisticated projects at startups or FAANG. Someone with FUD when it comes to LLM - web dev out of Alabama.
I could go on and on but I'm just ranting/venting a little. I guess I can end this by saying that in my professional/personal life 9/10 of the top level best engineers I know are jumping on LLMs any chance they get. Only 1/10 talks about AI slop or bullshit like that.
Not entirely disagreeing with your point but I think they've mostly been forced to pivot recently for their own sakes; they will never say it though. As much as they may seem eager the most public people tend to also be better at outside communication and knowing what they should say in public to enjoy more opportunities, remain employed or for the top engineers to still seem relevant in the face of the communities they are a part of. Its less about money and more about respect there I think.
The "sudden switch" since Opus 4.5 when many were saying just a few months ago "I enjoy actual coding" but now are praising LLM's isn't a one off occurrence. I do think underneath it is somewhat motivated by fear; not for the job however but for relevance. i.e. its in being relevant to discussions, tech talks, new opportunities, etc.
OK, I am gonna be the guy and put my skin in the game here. I kind of get the hype, but the experience with e.g. Claude Code (or Github Copilot previously and others as weel) has so far been pretty unreliable.
I have Django project with 50 kLOC and it is pretty capable of understanding the architecture, style of coding, naming of variables, functions etc. Sometimes it excels on tasks like "replicate this non-trivial functionality for this other model and update the UI appropriately" and leaves me stunned. Sometimes it solves for me tedious and labourous "replace this markdown editor with something modern, allowing fullscreen edits of content" and does annoying mistake that only visual control shows and is not capable to fix it after 5 prompts. I feel as I am becoming tester more than a developer and I do not like the shift. Especially when I do not like to tell someone he did an obvious mistake and should fix it - it seems I do not care if it is human or AI, I just do not like incompetence I guess.
Yesterday I had to add some parameters to very simple Falcon project and found out it has not been updated for several months and won't build due to some pip issues with pymssql. OK, this is really marginal sub-project so I said - let's migrate it to uv and let's not get hands dirty and let the Claude do it. He did splendidly but in the Dockerfile he missed the "COPY server.py /data/" while I asked him to change the path... Build failed, I updated the path myself and moved on.
And then you listen to very smart guys like Karpathy who rave about Tab, Tab, Tab, while not understanding the language or anything about the code they write. Am I getting this wrong?
I am really far far away from letting agents touch my infrastructure via SSH, access managed databases with full access privileges etc. and dread the day one of my silly customers asks me to give their agent permission to managed services. One might say the liability should then be shifted, but at the end of the day, humans will have to deal with the damage done.
My customer who uses all the codebase I am mentioning here asked me, if there is a way to provide "some AI" with item GTINs and let it generate photos, descriptions, etc. including metadata they handcrafted and extracted for years from various sources. While it looks like nice idea and for them the possibility of decreasing the staff count, I caught the feeling they do not care about the data quality anymore or do not understand the problems the are brining upon them due to errors nobody will catch until it is too late.
TL;DR: I am using Opus 4.5, it helps a lot, I have to keep being (very) cautious. Wake up call 2026? Rather like waking up from hallucination.
Everybody says how good Claude is and I go to my code base and I can't get it to correctly update one xaml file for me. It is quicker to make changes myself than to explain exactly what I need or learn how to do "prompt engineering".
Disclaimer: I don't have access to Claude Code. My employer has only granted me Claude Teams. Supposedly, they don't use my poopy code to train their models if I use my work email Claude so I am supposed to use that. If I'm not pasting code (asking general questions) into Claude, I believe I'm allowed to use whatever.
What's even the point of this comment if you self-admittedly don't have access to the flagship tool that everyone has been using to make these big bold coding claims?
Opus 4.5 ate through my Copilot quota last month, and it's already halfway through it for this month. I've used it a lot, for really complex code.
And my conclusion is: it's still not as smart as a good human programmer. It frequently got stuck, went down wrong paths, ignored what I told it to do to do something wrong, or even repeat a previous mistake I had to correct.
Yet in other ways, it's unbelievably good. I can give it a directory full of code to analyze, and it can tell me it's an implementation of Kozo Sugiyama's dagre graph layout algorithm, and immediately identify the file with the error. That's unbelievably impressive. Unfortunately it can't fix the error. The error was one of the many errors it made during previous sessions.
So my verdict is that it's great for code analysis, and it's fantastic for injecting some book knowledge on complex topics into your programming, but it can't tackle those complex problems by itself.
Yesterday and today I was upgrading a bunch of unit tests because of a dependency upgrade, and while it was occasionally very helpful, it also regularly got stuck. I got a lot more done than usual in the same time, but I do wonder if it wasn't too much. Wasn't there an easier way to do this? I didn't look for it, because every step of the way, Opus's solution seemed obvious and easy, and I had no idea how deep a pit it was getting me into. I should have been more critical of the direction it was pointing to.
Copilot and many coding agents truncates the context window and uses dynamic summarization to keep costs low for them. That's how they are able to provide flat fee plans.
If you want the full capability, use the API and use something like opencode. You will find that a single PR can easily rack up 3 digits of consumption costs.
Gerring off of their plans and prompts is so worth it, I know from experience, I'm paying less and getting more so far, paying by token, heavy gemini-3-flash user, it's a really good model, this is the future (distillations into fast, good enough for 90% of tasks), not mega models like Claude. Those will still be created for distillations and the harder problems
Maybe not, then. I'm afraid I have no idea what those numbers mean, but it looks like Gemini and ChatGPT 4 can handle a much larger context than Opus, and Opus 4.5 is cheaper than older versions. Is that correct? Because I could be misinterpreting that table.
People are completely missing the points about agentic development. The model is obviously a huge factor in the quality of the output, but the real magic lies in how the tools are managing and injecting context in to them, as well as the tooling. I switched from Copilot to Cursor at the end of 2025, and it was absolute night and day in terms of how the agents behaved.
Interesting you have this opinion yet you're using Cursor instead of Claude Code. By the same logic, you should get even better results directly using Anthropic's wrapper for their own model.
In my experience GPT-5 is also much more effective in the Cursor context than the Codex context. Cursor deserves props for doing something right under the hood.
yes just using AI for code analysis is way under appreciated I think. Even the most sceptical people on using it for coding should try it out as a tool for Q&A style code interrogation as well as generating documentation. I would say it zero-shots documentation generation better than most human efforts would to the point it begs the question of whether it's worth having the documentation in the first place. Obviously it can make mistakes but I would say they are below the threshold of human mistakes from what I've seen.
(I haven't used AI much, so feel free to ignore me.)
This is one thing I've tried using it for, and I've found this to be very, very tricky. At first glance, it seems unbelievably good. The comments read well, they seem correct, and they even include some very non-obvious information.
But almost every time I sit down and really think about a comment that includes any of that more complex analysis, I end up discarding it. Often, it's right but it's missing the point, in a way that will lead a reader astray. It's subtle and I really ought to dig up an example, but I'm unable to find the session I'm thinking about.
This was with ChatGPT 5, fwiw. It's totally possible that other models do better. (Or even newer ChatGPT; this was very early on in 5.)
Code review is similar. It comes up with clever chains of reasoning for why something is problematic, and initially convinces me. But when I dig into it, the review comment ends up not applying.
It could also be the specific codebase I'm using this on? (It's the SpiderMonkey source.)
If it can consistently verify that the error persists after fix--you can run (ok maybe you can't budget wise but theoretically) 10000 parallel instances of fixer agents then verify afterwards (this is in line with how the imo/ioi models work according to rumors)
I have no idea. Careless use, I guess. I was fixing a bunch of mocks in some once-great but now poorly maintained code, and I wasn't really feeling it so I just fed everything to Claude. Opus, unfortunately. I could easily have downgraded a bit.
>So my verdict is that it's great for code analysis, and it's fantastic for injecting some book knowledge on complex topics into your programming, but it can't tackle those complex problems by itself.
I don't think you've seen the full potential. I'm currently #1 on 5 different very complex computer engineering problems, and I can't even write a "hello world" in rust or cpp. You no longer need to know how to write code, you just need to understand the task at a high level and nudge the agents in the right direction. The game has changed.
If that is true; then all the commentary around software people having jobs still due to "taste" and other nice words is just that. Commentary. In the end the higher level stuff still needs someone to learn it (e.g. learning ASX2 architecture, knowing what tech to work with); but it requires IMO significantly less practice then coding which in itself was a gate. The skill morphs more into a tech expert rather than a coding expert.
I'm not sure what this means for the future of SWE's though yet. I don't see higher levels of staff in big large businesses bothering to do this, and at some scale I don't see founders still wanting to manage all of these agents, and processes (got better things to do at higher levels). But I do see the barrier of learning to code gone; meaning it probably becomes just like any other job.
How are you qualified to judge its performance on real code if you don't know how to write a hello world?
Yes, LLMs are very good at writing code, they are so good at writing code that they often generate reams of unmaintainable spaghetti.
When you submit to an informatics contest you don't have paying customers who depend on your code working every day. You can just throw away yesterday's code and start afresh.
Claude is very useful but it's not yet anywhere near as good as a human software developer. Like an excitable puppy it needs to be kept on a short leash.
None of the problems you've shown there are anything close to "very complex computer engineering problems", they're more like "toy problems with widely-known solutions given to students to help them practice for when they encounter actually complex problems".
What bothers me about posts like this is: mid-level engineers are not tasked with atomic, greenfield projects. If all an engineer did all day was build apps from scratch, with no expectation that others may come along and extend, build on top of, or depend on, then sure, Opus 4.5 could replace them. The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
No doubt I could give Opus 4.5 "build be a XYZ app" and it will do well. But day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right". Any non-technical person might read that and go "if it works it works" but any reasonable engineer will know that thats not enough.
Not necessarily responding to you directly, but I find this take to be interesting, and I see it every time an article like this makes the rounds.
Starting back in 2022/2023:
- (~2022) It can auto-complete one line, but it can't write a full function.
- (~2023) Ok, it can write a full function, but it can't write a full feature.
- (~2024) Ok, it can write a full feature, but it can't write a simple application.
- (~2025) Ok, it can write a simple application, but it can't create a full application that is actually a valuable product.
- (~2025+) Ok, it can write a full application that is actually a valuable product, but it can't create a long-lived complex codebase for a product that is extensible and scalable over the long term.
It's pretty clear to me where this is going. The only question is how long it takes to get there.
> It's pretty clear to me where this is going. The only question is how long it takes to get there.
I don't think its a guarantee. all of the things it can do from that list are greenfield, they just have increasing complexity. The problem comes because even in agentic mode, these models do not (and I would argue, can not) understand code or how it works, they just see patterns and generate a plausible sounding explanation or solution. agentic mode means they can try/fail/try/fail/try/fail until something works, but without understanding the code, especially of a large, complex, long-lived codebase, they can unwittingly break something without realising - just like an intern or newbie on the project, which is the most common analogy for LLMs, with good reason.
I haven't seen an AI successfully write a full feature to an existing codebase without substantial help, I don't think we are there yet.
> The only question is how long it takes to get there.
This is the question and I would temper expectations with the fact that we are likely to hit diminishing returns from real gains in intelligence as task difficulty increases. Real world tasks probably fit into a complexity hierarchy similar to computational complexity. One of the reasons that the AI predictions made in the 1950s for the 1960s did not come to be was because we assumed problem difficulty scaled linearly. Double the computing speed, get twice as good at chess or get twice as good at planning an economy. P, NP separation planed these predictions. It is likely that current predictions will run into similar separations.
It is probably the case that if you made a human 10x as smart they would only be 1.25x more productive at software engineering. The reason we have 10x engineers is less about raw intelligence, they are not 10x more intelligent, rather they have more knowledge and wisdom.
Yeah maybe, but personally it feels more like a plateau to me than an exponential takeoff, at the moment.
And this isn't a pessimistic take! I love this period of time where the models themselves are unbelievably useful, and people are also focusing on the user experience of using those amazing models to do useful things. It's an exciting time!
But I'm still pretty skeptical of "these things are about to not require human operators in the loop at all!".
Sure, eventually we'll have AGI, then no worries, but in the meantime you can only use the tools that exist today, and dreaming about what should be available in the future doesn't help.
I suspect that the timeline from autocomplete-one-line to autocomplete-one-app, which was basically a matter of scaling and RL, may in retrospect turn out to have been a lot faster that the next LLM to AGI step where it becomes capable of using human level judgement and reasoning, etc, to become a developer, not just a coding tool.
Ok, it can create a long-lived complex codebase for a product that is extensible and scalable over the long term, but it doesn't have cool tattoos and can't fancy a matcha
This is disingenuous because LLMs were already writing full, simple applications in 2023.[0]
They're definitely better now, but it's not like ChatGPT 3.5 couldn't write a full simple todo list app in 2023. There were a billion blog posts talking about that and how it meant the death of the software industry.
Plus I'd actually argue more of the improvements have come from tooling around the models rather than what's in the models themselves.
There are two types of right/wrong ways to build: the context specific right/wrong way to build something and an overly generalized engineer specific right/wrong way to build things.
I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.
I remember cases where a team of engineers built something the "right" way but it turned out to be the wrong thing. (Well engineered thing no one ever used)
Sometimes hacking something together messily to confirm it's the right thing to be building is the right way. Then making sure it's secure, then finally paying down some technical debt to make it more maintainable and extensible.
Where I see real silly problems is when engineers over-engineer from the start before it's clear they are building the right thing, or when management never lets them clean up the code base to make it maintainable or extensible when it's clear it is the right thing.
There's always a balance/tension, but it's when things go too far one way or another that I see avoidable failures.
*I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.*
Gosh I am so tired with that one - someone had a case that burned them in some previous project and now his life mission is to prevent that from happening ever again, and there would be no argument they will take.
Then you get like up to 10 engineers on typical team and team rotation and you end up with all kinds of "we have to do it right because we had to pull all nighter once, 5 years ago" baked in the system.
Not fun part is a lot of business/management people "expect" having perfect solution right away - there are some reasonable ones that understand you need some iteration.
> I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.
My first thought was that you probably also have different biases, priorities and/or taste. As always, this is probably very context-specific and requires judgement to know when something goes too far. It's difficult to know the "most correct" approach beforehand.
> Sometimes hacking something together messily to confirm it's the right thing to be building is the right way. Then making sure it's secure, then finally paying down some technical debt to make it more maintainable and extensible.
I agree that sometimes it is, but in other cases my experience has been that when something is done, works and is used by customers, it's very hard to argue about refactoring it. Management doesn't want to waste hours on it (who pays for it?) and doesn't want to risk breaking stuff (or changing APIs) when it works. It's all reasonable.
And when some time passes, the related intricacies, bigger picture and initially floated ideas fade from memory. Now other stuff may depend on the existing implementation. People get used to the way things are done. It gets harder and harder to refactor things.
Again, this probably depends a lot on a project and what kind of software we're talking about.
> There's always a balance/tension, but it's when things go too far one way or another that I see avoidable failures.
I think balance/tension describes it well and good results probably require input from different people and from different angles.
I know what you are talking about, but there is more to life than just product-market fit.
Hardly any of us are working on Postgres, Photoshop, blender, etc. but it's not just cope to wish we were.
It's good to think about the needs to business and the needs of society separately. Yes, the thing needs users, or no one is benefiting. But it also needs to do good for those users, and ultimately, at the highest caliber, craftsmanship starts to matter again.
There are legitimate reasons for the startup ecosystem to focus firstly and primarily on getting the users/customers. I'm not arguing against that. What I am arguing is why does the industry need to be dominated by startups in terms of the bulk of the products (not bulk of the users). It begs the question of how much societally-meaningful programming waiting to be done.
I'm hoping for a world where more end users code (vibe or otherwise) and the solve their own problems with their own software. I think that will make more a smaller, more elite software industry that is more focused on infrastructure than last-mile value capture. The question is how to fund the infrastructure. I don't know except for the most elite projects, which is not good enough for the industry (even this hypothetical smaller one) on the whole.
> ...multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered.
I usually resolve this by putting on the table the consequences and their impacts upon my team that I’m concerned about, and my proposed mitigation for those impacts. The mitigation always involves the other proposer’s team picking up the impact remediation. In writing. In the SOP’s. Calling out the design decision by day of the decision to jog memories and names of those present that wanted the design as the SME’s. Registered with the operations center. With automated monitoring and notification code we’re happy to offer.
Once people are asked to put accountable skin in the sustaining operations, we find out real fast who is taking into consideration the full spectrum end to end consequences of their decisions. And we find out the real tradeoffs people are making, and the externalities they’re hoping to unload or maybe don’t even perceive.
Another thing that gets me with projects like this, there are already many examples of image converters, minesweeper clones etc that you can just fork on GitHub, the value of the LLM here is largely just stripping the copyright off
It’s kind of funny - there’s another thread up where a dev claimed a 20-50x speed up. To their credit they posted videos and links to the repo of their work.
And when you check the work, a large portion of it was hand rolling an ORM (via an LLM). Relatively solved problem that an LLM would excel at, but also not meaningfully moving the needle when you could use an existing library. And likely just creating more debt down the road.
Have you ever tried to find software for a specific need? I usually spend hours investigating anything I can find only to discover that all options are bad in one way or another and cover my use case partially at best. It's dreadful, unrewarding work that I always fear. Being able to spent those hours to develop custom solution that has exactly what I need, no more, no less, that I can evolve further as my requirements evolve, all that while enjoying myself, is a godsend.
Anecdata but I’ve found Claude code with Opus 4.5 able to do many of my real tickets in real mid and large codebases at a large public startup. I’m at senior level (15+ years). It can browse and figure out the existing patterns better than some engineers on my team. It used a few rare features in the codebase that even I had forgotten about and was about to duplicate. To me it feels like a real step change from the previous models I’ve used which I found at best useless. It’s following style guides and existing patterns well, not just greenfield. Kind of impressive, kind of scary
Same anecdote for me (except I'm +/- 40 years experience). I consider my self a pretty good dev for non-web dev (GPU's, assembly, optimisation,...) and my conclusion is the same as you: impressive and scary. If the somehow the idea of what you want to do is on the web in text or in code, then Claude most likely has it. And its ability to understand my own codebases is just crazy (at my age, memory is declining and having Claude to help is just waow). Of course it fails some times, of course it need direction, but the thing it produces is really good.
I'm seeing this as well. Not huge codebases but not tiny - 4 year old startup. I'm new there and it would have been impossible for me to deliver any value this soon.
12 years experience; this thing is definitely amazing. Combined with a human it can be phenomenal. It also helped me tons with lots of external tools, understand what data/marketing teams are doing and even providing pretty crucial insights to our leadership that Gemini have noticed.
I wouldn't try to completely automate the humans out of the loop though just yet, but this tech for sure is gonna downsize team numbers (and at the same time - allow many new startups to come to life with little capital that eventually might grow and hire people. So unclear how this is gonna affect jobs.)
I've also found it to keep such a constrained context window (on large codebases), that it writes a secondary block of code that already had a solution in a different area of the same file.
Nothing I do seems to fix that in its initial code writing steps. Only after it finishes, when I've asked it to go back and rewrite the changes, this time making only 2 or 3 lines of code, does it magically (or finally) find the other implementation and reuse it.
It's freakin incredible at tracing through code and figuring it out. I <3 Opus. However, it's still quite far from any kind of set-and-forget-it.
Same exist in humans also, I worked with a developer who had 15 year experience and was tech lead in a big Indian firm, We started something together, 3 months back when I checked the Tables I was shocked to see how he fucked up and messed the DB. Finally the only option left with me was to quit because i know it will break in production and if i onboarded a single customer my life would be screwed. He mixed many things with frontend and offloaded even permissions to frontend, and literally copied tables in multiple DB (We had 3 services). I still cannot believe how he worked as a tch lead for 15 years. each DB had more than 100 tables and out of that 20-25 were duplicates. He never shared code with me, but I smelled something fishy when bug fixing was never ending loop and my front end guy told me he cannot do it anymore. Only mistake I did was I trusted him and worst part is he is my cousin and the relation became sour after i confronted him and decided to quit.
This sounds like a culture issue in the development process, I have seen this prevented many times. Sure I did have to roll back a feature I did not sign off just before new years. So as you say it happens.
> The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
You’re talking like in the year 2026 we’re still writing code for future humans to understand and improve.
I fear we are not doing that. Right now, Opus 4.5 is writing code that later Opus 5.0 will refactor and extend. And so on.
For one, there are objectively detrimental ways to organize code: tight coupling, lots of mutable shared state, etc. No matter who or what reads or writes the code, such code is more error-prone, and more brittle to handle.
Then, abstractions are tools to lower the cognitive load. Good abstractions reduce the total amount of code written, allow to reason about the code in terms of these abstractions, and do not leak in the area of their applicability. Say Sequence, or Future, or, well, function are examples of good abstractions. No matter what kind of cognitive process handles the code, it benefits from having to keep a smaller amount of context per task.
"Code structure does not matter, LLMs will handle it" sounds a bit like "Computer architectures don't matter, the Turing Machine is proved to be able to handle anything computable at all". No, these things matter if you care about resource consumption (aka cost) at the very least.
Opus 4.5 is writing code that Opus 5.0 will refactor and extend. And Opus 5.5 will take that code and rewrite it in C from the ground up. And Opus 6.0 will take that code and make it assembly. And Opus 7.0 will design its own CPU. And Opus 8.0 will make a factory for its own CPUs. And Opus 9.0 will populate mars. And Opus 10.0 will be able to achieve AGI. And Opus 11.0 will find God. And Opus 12.0 will make us a time machine. And so on.
Up until now, no business has been built on tools and technology that no one understands. I expect that will continue.
Given that, I expect that, even if AI is writing all of the code, we will still need people around who understand it.
If AI can create and operate your entire business, your moat is nil. So, you not hiring software engineers does not matter, because you do not have a business.
In my experience, using LLMs to code encouraged me to write better documentation, because I can get better results when I feed the documentation to the LLM.
Also, I've noticed failure modes in LLM coding agents when there is less clarity and more complexity in abstractions or APIs. It's actually made me consider simplifying APIs so that the LLMs can handle them better.
Though I agree that in specific cases what's helpful for the model and what's helpful for humans won't always overlap. Once I actually added some comments to a markdown file as note to the LLM that most human readers wouldn't see, with some more verbose examples.
I think one of the big problems in general with agents today is that if you run the agent long enough they tend to "go off the rails", so then you need to babysit them and intervene when they go off track.
I guess in modern parlance, maintaining a good codebase can be framed as part of a broader "context engineering" problem.
We don't know what Opus 5.0 will be able to refactor.
If argument is "humans and Opus 4.5 cannot maintain this, but if requirements change we can vibe-code a new one from scratch", that's a coherent thesis, but people need to be explicit about this.
(Instead this feels like the mott that is retreated to, and the bailey is essentially "who cares, we'll figure out what to do with our fresh slop later".)
Ironically, I've been Claude to be really good at refactors, but these are refactors I choose very explicitly. (Such as I start the thing manually, then let it finish.) (For an example of it, see me force-pushing to https://github.com/NixOS/nix/pull/14863 implementing my own code review.)
But I suspect this is not what people want. To actually fire devs and not rely on from-scratch vibe-coding, we need to figure out which refactors to attempt in order to implement a given feature well.
That's a very creative open-ended question that I haven't even tried to let the LLMs take a crack at it, because why I would I? I'm plenty fast being the "ideas guy". If the LLM had better ideas than me, how would I even know? I'm either very arrogant or very good because I cannot recall regretting one of my refactors, at least not one I didn't back out of immediately.
Refactoring does always cost something and I doubt LLMs will ever change that. The more interesting question is whether the cost to refactor or "rewrite" the software will ever become negligible. Until it isn't, it's short-sighted to write code in the manner you're describing. If software does become that cheap, then you can't meaningfully maintain a business on selling software anyway.
This is the question! Your narrative is definitely plausible, and I won't be shocked if it turns out this way. But it still isn't my expectation. It wasn't when people were saying this in 2023 or in 2024, and I haven't been wrong yet. It does seem more likely to me now than it did a couple years ago, but still not the likeliest outcome in the next few years.
Yeah I think it's a mistake to focus on writing "readable" or even "maintainable" code. We need to let go of these aging paradigms and be open to adopting a new one.
- How quickly is cost of refactor to a new pattern with functional parity going down?
- How does that change the calculus around tech debt?
If engineering uses 3 different abstractions in inconsistent ways that leak implementation details across components and duplicate functionality in ways that are very hard to reason about, that is, in conventional terms, an existential problem that might kill the entire business, as all dev time will end up consumed by bug fixes and dealing with pointless complexity, velocity will fall to nothing, and the company will stop being able to iterate.
But if claude can reliably reorganize code, fix patterns, and write working migrations for state when prompted to do so, it seems like the entire way to reason about tech debt has changed. And it has changed more if you are willing to bet that models within a year will be much better at such tasks.
And in my experience, claude is imperfect at refactors and still requires review and a lot of steering, but it's one of the things it's better at, because it has clear requirements and testing workflows already built to work with around the existing behavior. Refactoring is definitely a hell of a lot faster than it used to be, at least on the few I've dealt with recently.
In my mind it might be kind of like thinking about financial debt in a world with high inflation, in that the debt seems like it might get cheaper over time rather than more expensive.
> But if claude can reliably reorganize code, fix patterns, and write working migrations for state when prompted to do so, it seems like the entire way to reason about tech debt has changed.
Yup, I recently spent 4 days using Claude to clean up a tool that's been in production for over 7 years. (There's only about 3 months of engineering time spent on it in those years.)
We've known what the tool needed for many years, but ugh, the actual work was fairly messy and it was never a priority. I reviewed all of Opus's cleanup work carefully and I'm quite content with the result. Maybe even "enthusiastic" would be accurate.
So even if Claude can't clean up all the tech debt in a totally unsupervised fashion, it can still help address some kinds of tech debt extremely rapidly.
Good point. Most of the cost in dealing with tech debt is reading the code and noting the issues. I found that Claude can produce much better code when it has a functionally correct reference implementation. Also it's not needed to very specifically point out issues. I once mentioned "I see duplicate keys in X and Y, rework it to reduce repetition and verbosity". It came up with a much more elegant way to implement it.
So maybe doing 2-3 stages makes sense. First stage needs to be functionallty correct, but you accept code smells such as leaky abstractions, verbosity and repetition. In stage 2 and 3 you eliminate all this. You could integrate this all into the initial specification; you won't even see the smelly intermediate code; it only exists as a stepping stone for the model to iteratively refine the code!
A greenfield project is definitely 'easy mode' for an LLM; especially if the problem area is well understood (and documented).
Opus is great and definitely speeds up development even in larger code bases and is reasonably good at matching coding style/standard to that of of the existing code base.
In my opinion, the big issue is the relatively small context that quickly overwhelms the models when given a larger task on a large codebase.
For example, I have a largish enterprise grade code base with nice enterprise grade OO patterns and class hierarchies. There was a simple tech debt item that required refactoring about 30-40 classes to adhere to a slightly different class hierarchy. The work is not difficult, just tedious, especially as unit tests need to be fixed up.
I threw Opus at it with very precise instructions as to what I wanted it to do and how I wanted it to do it. It started off well but then disintegrated once it got overwhelmed at the sheer number of files it had to change. At some point it got stuck in some kind of an error loop where one change it made contradicted with another change and it just couldn't work itself out. I tried stopping it and helping it out but at this point the context was so polluted that it just couldn't see a way out.
I'd say that once an LLM can handle more 'context' than a senior dev with good knowledge of a large codebase, LLM will be viable in a whole new realm of development tasks on existing code bases. That 'too hard to refactor this/make this work with that' task will suddenly become viable.
I just did something similar and it went swimmingly by doing this: Keep the plan and status in an md file. Tell it to finish one file at a time and run tests and fix issues and then to ask whether to proceed with the next file. You can then easily start a new chat with the same instructions and plan and status if the context gets poisoned.
You have to think of Opus as a developer whose job at your company lasts somewhere between 30 to 60 minutes before you fire them and hire a new one.
Yes, it's absurd but it's a better metaphor than someone with a chronic long term memory deficit since it fits into the project management framework neatly.
So this new developer who is starting today is ready to be assigned their first task, they're very eager to get started and once they start they will work very quickly but you have to onboard them. This sounds terrible but they also happen to be extremely fast at reading code and documentation, they know all of the common programming languages and frameworks and they have an excellent memory for the hour that they're employed.
What do you do to onboard a new developer like this? You give them a well written description of your project with a clear style guide and some important dos and don'ts, access to any documentation you may have and a clear description of the task they are to accomplish in less than one hour. The tighter you can make those documents, the better. Don't mince words, just get straight to the point and provide examples where possible.
The task description should be well scoped with a clear definition of done, if you can provide automated tests that verify when it's complete that's even better. If you don't have tests you can also specify what should be tested and instruct them to write the new tests and run them.
For every new developer after the first you need a record of what was already accomplished. Personally, I prefer to use one markdown document per working session whose filename is a date stamp with the session number appended. Instruct them to read the last X log files where X is however many are relevant to the current task. Most of the time X=1 if you did a good job of breaking down the tasks into discrete chunks. You should also have some type of roadmap with milestones, if this file will be larger than 1000 lines then you should break it up so each milestone is its own document and have a table of contents document that gives a simple overview of the total scope. Instruct them to read the relevant milestone.
Other good practices are to tell them to write a new log file after they have completed their task and record a summary of what they did and anything they discovered along the way plus any significant decisions they made. Also tell them to commit their work afterwards and Opus will write a very descriptive commit message by default (but you can instruct them to use whatever format you prefer). You basically want them to get everything ready for hand-off to the next 60 minute developer.
If they do anything that you don't want them to do again make sure to record that in CLAUDE.md. Same for any other interventions or guidance that you have to provide, put it in that document and Opus will almost always stick to it unless they end up overfilling their context window.
I also highly recommend turning off auto-compaction. When the context gets compacted they basically just write a summary of the current context which often removes a lot of the important details. When this happens mid-task you will certainly lose parts of the context that are necessary for completing the task. Anthropic seems to be working hard at making this better but I don't think it's there yet. You might want to experiment with having it on and off and compare the results for yourself.
If your sessions are ending up with >80% of the context window used while still doing active development then you should re-scope your tasks to make them smaller. The last 20% is fine for doing menial things like writing the summary, running commands, committing, etc.
People have built automated systems around this like Beads but I prefer the hands-on approach since I read through the produced docs to make sure things are going ok and use them as a guide for any changes I need to make mid-project.
With this approach I'm 99% sure that Opus 4.5 could handle your refactor without any trouble as long as your classes aren't so enormous that even working on a single one at a time would cause problems with the context window, and if they are then you might be able to handle it by cautioning Opus to not read the whole file and to just try making targeted edits to specific methods. They're usually quite good at finding and extracting just the sections that they need as long as they have some way to know what to look for ahead of time.
"Have an agent investiate issue X in modules Y and Z. The agent should place a report at ./doc/rework-xyz-overview.md with all locations that need refactoring. Once you have the report, have agents refactor 5 classes each in parallel. Each agent writes a terse report in ./doc/rework-xyz/ When they are all done, have another agent check all the work. When that agent reports everything is okay, perform a final check yourself"
> If all an engineer did all day was build apps from scratch, with no expectation that others may come along and extend, build on top of, or depend on, then sure, Opus 4.5 could replace them.
Why do they need to be replaced? Programmers are in the perfect place to use AI coding tools productively. It makes them more valuable.
I had Opus write a whole app for me in 30 seconds the other night. I use a very extensive AGENTS.md to guide AI in how I like my code chiseled. I've been happily running the app without looking at a line of it, but I was discussing the app with someone today, so I popped the code open to see what it looked like. Perfect. 10/10 in every way. I would not have written it that good. It came up with at least one idea I would not have thought of.
I'm very lucky that I rarely have to deal with other devs and I'm writing a lot of code from scratch using whatever is the latest version of the frameworks. I understand that gives me a lot of privileges others don't have.
Their thesis is that code quality does not matter as it is now a cheap commodity. As long as it passes the tests today it's great. If we need to refactor the whole goddamn app tomorrow, no problem, we will just pay up the credits and do it in a few hours.
The fundamental assumption is completely wrong. Code is not a cheap commodity. It is in fact so disastrously expensive that the entire US economy is about to implode while we're unbolting jet engines from old planes to fire up in the parking lots of datacenters for electricity.
It matters for all the things you’d be able to justify paying a programmer for. What’s about to change is that there will be tons of these little one-off projects that previously nobody could justify paying $150/hr for. A mass democratization of software development. We’ve yet to see what that really looks like.
> Their thesis is that code quality does not matter as it is now a cheap commodity.
That's not how I read it. I would say that it's more like "If a human no longer needs to read the code, is it important for it to be readable?"
That is, of course, based on the premise that AI is now capable of both generating and maintaining software projects of this size.
Oh, and it begs another question: are human-readable and AI-readable the same thing? If they're not, it very well could make sense to instruct the model to generate code that prioritizes what matters to LLMs over what matters to humans.
>What bothers me about posts like this is: mid-level engineers are not tasked with atomic, greenfield projects
They get those ocassionally all the time though too. Depends on the company. In some software houses it's constant "greenfield projects", one after another. And even in companies with 1-2 pieces of main established software to maintain, there are all kinds of smaller utilities or pipelines needed.
>But day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right".
In some cases that's legit. In other cases it's just "it did it well, but not how I'd done it", which is often needless stickness to some particular style (often a contention between 2 human programmers too).
Basically, what FloorEgg says in this thread: "There are two types of right/wrong ways to build: the context specific right/wrong way to build something and an overly generalized engineer specific right/wrong way to build things."
And you can always not just tell it "build me this feature", but tell it (high level way) how to do it, and give it a generic context about such preferences too.
> its building it the right way, in an easily understood way, in a way that's easily extensible.
When I worked at Google, people rarely got promoted for doing that. They got promoted for delivering features or sometimes from rescuing a failing project because everyone was doing the former until promotion velocity dropped and your good people left to other projects not yet bogged down too far.
Yeah. Just like another engineer. When you tell another engineer to build you a feature, it's improbable they'll do it they way that you consider "right."
This sounds a lot like the old arguments around using compilers vs hand-writing asm. But now you can tell the LLM how you want to implement the changes you want. This will become more and more relevant as we try to maintain the code it generates.
But, for right now, another thing Claude's great at is answering questions about the codebase. It'll do the analysis and bring up reports for you. You can use that information to guide the instructions for changes, or just to help you be more productive.
Even if you are going green field, you need to build it the way it is likely to be used based a having a deep familiarity with what that customer's problems are and how their current workflow is done. As much as we imagine everything is on the internet, a bunch of this stuff is not documented anywhere. An LLM could ask the customer requirement questions but that familiarity is often needed to know the right questions to ask. It is hard to bootstrap.
Even if it could build the perfect greenfield app, as it updates the app it is needs to consider backwards compatibility and breaking changes. LLMs seem very far as growing apps. I think this is because LLMs are trained on the final outcome of the engineering process, but not on the incremental sub-commit work of first getting a faked out outline of the code running and then slowly building up that code until you have something that works.
This isn't to say that LLMs or other AI approaches couldn't replace software engineering some day, but they clear aren't good enough yet and the training sets they have currently have access to are unlikely to provide the needed examples.
In my personal experience, Claude is better at greenfield, Codex is better at fitting in. Claude is the perfect tool for a "vibe coder", Codex is for the serious engineer who wants to get great and real work done.
Codex will regularly give me 1000+ line diffs where all my comments (I review every single line of what agents write) are basically nitpicks. "Make this shallow w/ early return, use | None instead of Optional", that sort of thing.
I do prompt it in detail though. It feels like I'm the person coming in with the architecture most of the time, AI "draws the rest of the owl."
My favorite benchmark for LLMs and agents is to have it port a medium-complexity library to another programming language. If it can do that well, it's pretty capable of doing real tasks. So far, I always have to spend a lot of time fixing errors. There are also often deep issues that aren't obvious until you start using it.
Comments on here often criticise ports as easy for LLMs to do because there's a lot of training and tests are all there, which is not as complex as real word tasks
I find Opus 4.5 very, very strong at matching the prevailing conventions/idioms/abstractions in a large, established codebase. But I guess I'm quite sensitive to this kind of thing so I explicitly ask Opus 4.5 to read adjacent code which is perhaps why it does it so well. All it takes is a sentence or two, though.
I don’t know what I’m doing wrong. Today I tried to get it to upgrade Nx, yarn and some resolutions in a typescript monorepo with about 20 apps at work (Opus 4.5 through Kiro) and it just…couldn’t do it. It hit some snags with some of the configuration changes required by the upgrade and resorted to trying to make unwanted changes to get it to build correctly. I would have thought that’s something it could hit out of the park. I finally gave up and just looked at the docs and some stack overflow and fixed it myself. I had to correct it a few times about correct config params too. It kept imagining config options that weren’t valid.
> ask Opus 4.5 to read adjacent code which is perhaps why it does it so well. All it takes is a sentence or two, though.
People keep telling me that an LLM is not intelligence, it's simply spitting out statistically relevant tokens. But surely it takes intelligence to understand (and actually execute!) the request to "read adjacent code".
Exactly. The main issue IMO is that "software that seems to work" and "software that works" can be very hard to tell apart without validating the code, yet these are drastically different in terms of long-term outcomes. Especially when there's a lot of money, or even lives, riding on these outcomes. Just because LLMs can write software to run the Therac-25 doesn't mean it's acceptable for them to do so.
After recently applying Codex to a gigantic old and hairy project that is as far from greenfield it can be, I can assure you this assertion is false. It’s bonkers seeing 5.2 churn though the complexity and understanding dependencies that would take me days or weeks to wrap my head around.
Another thing these posts assume is a single developer keep working on the product with a number of AI agents, not a large team. I think we need to rethink how teams work with AI. Its probably not gonna be a single developer typing a prompt but a team somehow collaborates a prompt or equivalent. XP on steroids? Programming by committee?
But... you can ask! Ask claude to use encapsulation, or to write the equivalent of interfaces in the language you using, and to map out dependencies and duplicate features, or to maintain a dictionary of component responsibilities.
AI coding is a multiplier of writing speed but doesn't excuse planning out and mapping out features.
You can have reasonably engineered code if you get models to stick to well designed modules but you need to tell them.
But time I spend asking is time I could have been writing exactly what I wanted in the first place, if I already did the planning to understand what I wanted. Once I know what I want, it doesn't take that long, usually.
Which is why it's so great for prototyping, because it can create something during the planning, when you haven't planned out quite what you want yet.
On the contrary, Opus 4.5 is the best agent I’ve ever used for making cohesive changes across many files in a large, existing codebase. It maintains our patterns and looks like all the other code. Sometimes it hiccups for sure.
LLMs are pretty good at picking up existing codebases. Even with cleared context they can do „look at this codebase and this spec doc that created it. I want to add feature x“
Yeah, all of those applications he shows do not really expose any complex business logic.
With all the due respect: a file converter for windows is glueing few windows APIs with the relevant codec.
Now, good luck working on a complex warehouse management application where you need extremely complex logic to sort the order of picking, assembling, packing on an infinite number of variables: weight, amazon prime priority, distribution centers, number and type of carts available, number and type of assembly stations available, different delivery systems and requirements for different delivery operators (such as GLE, DHL, etc) that has to work with N customers all requiring slightly different capabilities and flows, all having different printers and operations, etc, etc. And I ain't even scratching the surface of the business logic complexity (not even mentioning functional requirements) to avoid boring the reader.
Mind you, AI is still tremendously useful in the analysis phase, and can sort of help in some steps of the implementation one, but the number of times you can avoid looking thoroughly at the code for any minor issue or discrepancy is absolutely close to 0.
So far, Im not convinced, but lets take a look at fundmentally whats happening and why humans > agents > LLMs.
At its heart, programming is a constraint satisfaction problem.
The more constraints (requirements, syntax, standards, etc) you have, the harder it is to solve them all simultaneously.
New projects with few contributors have fewer constraints.
The process of “any change” is therefore simpler.
Now, undeniably
1) agents have improved the ability to solve constraints by iterating; eg. Generate, test, modify, etc. over raw LLm output.
2) There is an upper bound (context size, model capability) to solve simultaneous constraints.
3) Most people have a better ability to do this than agents (including claude code using opus 4.5).
So, if youre seeing good results from agents, you probably have a smaller set of constraints than other people.
Similarly, if youre getting bad results, you can probably improve them by relaxing some of the constraints (consistent ui, number of contributors, requirements, standards, security requirements, split code into well defined packages).
This will make both agents and humans more productive.
The open question is: will models continue to improve enough to approach or exceed human level ability in this?
Are humans willing to relax the constraints enough for it to be plausible?
I would say currently people clambering about the end of human developers are cluelessly deceived by the “appearance of complexity” which does not match the “reality of constraints” in larger applications.
Opus 4.5 cannot do the work of a human on code bases Ive worked on. Hell, talented humans struggle to work on some of them.
…but that doesnt mean it doesnt work.
Just that, right now, the constraint set it can solve is not large enough to be useful in those situations.
…and increasingly we see low quality software where people care only about speed of delivery; again, lowering the bar in terms of requirements.
So… you know. Watch this space. Im not counting on having a dev job in 10 years. If I do, it might be making a pile of barely working garbage.
…but I have one now, and anyone who thinks that this year people will be largely replaced by AI is probably poorly informed and has misunderstood the capabilities on these models.
Theres only so low you can go in terms of quality.
Based on my experience using these LLMs regularly I strongly doubt it could even build any application with realistic complexity without screwing things up in major ways everywhere, and even on top of that still not meeting all the requirements.
If you have microservices architecture in your project you are set for AI. You can swap out any lacking, legacy microservice in your system with "greenfield" vibecoded one.
Man, I've been biting my tongue all day with regards to this thread and overall discussion.
I've been building a somewhat-novel, complex, greenfield desktop app for 6 months now, conceived and architected by a human (me), visually designed by a human (me), implementation heavily leaning on mostly Claude Code but with Codex and Gemini thrown in the mix for the grunt work. I have decades of experience, could have built it bespoke in like 1-2 years probably, but I wanted a real project to kick the tires on "the future of our profession".
TL;DR I started with 100% vibe code simply to test the limits of what was being promised. It was a functional toy that had a lot of problems. I started over and tried a CLI version. It needed a therapist. I started over and went back to visual UI. It worked but was too constrained. I started over again. After about 10 complete start-overs in blank folders, I had a better vision of what I wanted to make, and how to achieve it. Since then, I've been working day after day, screen after screen, building, refactoring, going feature by feature, bug after bug, exactly how I would if I was coding manually. Many times I've reached a point where it feels "feature complete", until I throw a bigger dataset at it, which brings it to its knees. Time to re-architect, re-think memory and storage and algorithms and libraries used. Code bloated, and I put it on a diet until it was trim and svelte. I've tried many different approaches to hard problems, some of which LLMs would suggest that truly surprised me in their efficacy, but only after I presented the issues with the previous implementation. There's a lot of conversation and back and forth with the machine, but we always end up getting there in the end. Opus 4.5 has been significantly better than previous Anthropic models. As I hit milestones, I manually audit code, rewrite things, reformat things, generally polish the turd.
I tell this story only because I'm 95% there to a real, legitimate product, with 90% of the way to go still. It's been half a year.
Vibe coding a simple app that you just want to use personally is cool; let the machine do it all, don't worry about under the hood, and I think a lot of people will be doing that kind of stuff more and more because it's so empowering and immediate.
Using these tools is also neat and amazing because they're a force multiplier for a single person or small group who really understand what needs done and what decisions need made.
These tools can build very complex, maintainable software if you can walk with them step by step and articulate the guidelines and guardrails, testing every feature, pushing back when it gets it wrong, growing with the codebase, getting in there manually whenever and wherever needed.
These tools CANNOT one-shot truly new stuff, but they can be slowly cajoled and massaged into eventually getting you to where you want to go; like, hard things are hard, and things that take time don't get done for a while. I have no moral compunctions or philosophical musings about utilizing these tools, but IMO there's still significant effort and coordination needed to make something really great using them (and literally minimal effort and no coordination needed to make something passable)
If you're solo, know what you want, and know what you're doing, I believe you might see 2x, 4x gains in time and efficiency using Claude Code and all of his magical agents, but if your project is more than a toy, I would bet that 2x or 4x is applied to a temporal period of years, not days or months!
>day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right"
Then don't ask it to "build me this feature" instead lay out a software development process with designated human in the loop where you want it and guard rails to keep it on track. Create a code review agent to look for and reject strange abstractions. Tell it what you don't like and it's really good at finding it.
I find Opus 4.5, properly prompted, to be significantly better at reviewing code than writing it, but you can just put it in a loop until the code it writes matches the review.
> The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
The number of production applications that achieve this rounds to zero
I’ve probably managed 300 brownfield web, mobile, edge, datacenter, data processing and ML applications/products across DoD, B2B, consumer and literally zero of them were built in this way
you can definitely just tell it what abstractions you want when adding a feature and do incremental work on existing codebase. but i generally prefer gpt-5.2
I've been using 5.2 a lot lately but hit my quota for the first time (and will probably continue to hit it most weeks) so I shelled out for claude code. What differences do you notice? Any 'metagame' that would be helpful?
"its building it the right way, in an easily understood way, in a way that's easily extensible"
I am in a unique situation where I work with a variety of codebases over the week. I have had no problem at all utilizing Claude Code w/ Opus 4.5 and Gemini CLI w/ Gemini 3.0 Pro to make excellent code that is indisputably "the right way", in an extremely clear and understandable way, and that is maximally extensible. None of them are greenfield projects.
I feel like this is a bit of je ne sais quoi where people appeal to some indemonstrable essence that these tools just can't accomplish, and only the "non-technical" people are foolish enough to not realize it. I'm a pretty technical person (about 30 years of software development, up to staff engineer and then VP). I think they have reached a pretty high level of competence. I still audit the code and monitor their creations, but I don't think they're the oft claimed "junior developer" replacement, but instead do the work I would have gotten from a very experienced, expert-level developer, but instead of being an expert at a niche, they're experts at almost every niche.
Are they perfect? Far from it. It still requires a practitioner who knows what they're doing. But frequently on here I see people giving takes that sound like they last used some early variant of Copilot or something and think that remains state of the art. The rest of us are just accelerating our lives with these tools, knowing that pretending they suck online won't slow their ascent an iota.
You AI hype thots/bots are all the same. All these claims but never backed up with anything to look at. And also alway claiming “you’re holding it wrong”.
Not in terms of knowledge. That was already phenomenal. But in its ability to act independently: to make decisions, collaborate with me to solve problems, ask follow-up questions, write plans and actually execute them.
You have to experience it yourself on your own real problems and over the course of days or weeks.
Every coding problem I was able to define clearly enough within the limits of the context window, the chatbot could solve and these weren’t easy. It wasn’t just about writing and testing code. It also involved reverse engineering and cracking encoding-related problems. The most impressive part was how actively it worked on problems in a tight feedback loop.
In the traditional sense, I haven’t really coded privately at all in recent weeks. Instead, I’ve been guiding and directing, having it write specifications, and then refining and improving them.
Curious how this will perform in complex, large production environments.
Just some examples I’ve already made public. More complex ones are in the pipeline. With [0], I’m trying to benchmark different coding-agents. With [1], I successfully reverse-engineered an old C64 game using Opus 4.5 only.
Yes, feel free to blame me for the fact that these aren’t very business-realistic.
This has always been my problem whether it's Gemini, openai or Claude. Unless you hand-hold it to an extreme degree, it is going to build a mountain next to a molehill.
It may end up working, but the thing is going to convolute apis and abstractions and mix patterns basically everywhere
Difficult and it really depends on the complexity. I definitely work in a spec-driven way, with a step-by-step implementation phase. If it goes the wrong way I prefer to rewrite the spec and throw away the code.
I have it propose several approaches, pick and choose from each, and remove what I don't want done. "Use the general structure of A, but use the validation structure of D. Using a view translation layer is too much, just rely on FastAPI/SQLModel's implicit view conversion."
Instructions, in the system prompt for not doing that
Once more people realize how easy it is to customize and personalized your agent, I hope they will move beyond what cookie cutter Big AI like Anthropic and Google give you.
I suspect most won't though because (1) it means you have to write human language, communication, and this weird form of persuasion, (2) ai is gonna make a bunch of them lazy and big AI sold them on magic solutions that require no effort on your part (not true, there is a lot of customizing and it has huge dividends)
I personally try to narrow scope as much as possible to prevent this. If a human hands me a PR that is not digestible size-wise and content-wise (to me), I am not reviewing and merging it. Same thing with what claude generates with my guidance.
I find my sweet spot is using the Claude web app as a rubber duck as well as feeding it snippets of code and letting it help me refine the specific thing I'm doing.
When I use Claude Code I find that it *can* add a tremendous amount of ability due to its ability to see my entire codebase at once, but the issue is that if I'm doing something where seeing my entire codebase would help that it blasts through my quota too fast. And if I'm tightly scoping it, it's just as easy & faster for me to use the website.
Because of this I've shifted back to the website. I find that I get more done faster that way.
I've had similar experiences but I've been able to start using Claude Code for larger projects by doing some refactoring with the goal of making the codebase understandable by just looking at the interfaces. This along with instructions to prefer looking at the interface for a module unless working directly on the implementation of the module seems to allow further progress to be made within session limits.
By "the website" do you mean you're copy pasting, or are you using the code system where Anthropic clones your code from GitHub and interacts with it in a VM/container for you.
> In the traditional sense, I haven’t really coded privately at all in recent weeks. Instead, I’ve been guiding and directing, having it write specifications, and then refining and improving them.
I've noticed a huge drop in negative comments on HN when discussing LLMs in the last 1-2 months.
All the LLM coded projects I've seen shared so far[1] have been tech toys though. I've watched things pop up on my twitter feed (usually games related), then quietly go off air before reaching a gold release (I manually keep up to date with what I've found, so it's not the algorithm).
I find this all very interesting: LLMs dont change the fundamental drives needed to build successful products. I feel like I'm observing the TikTokification of software development. I dont know why people aren't finishing. Maybe they stop when the "real work" kicks in. Or maybe they hit the limits of what LLMs can do (so far). Maybe they jump to the next idea to keep chasing the rush.
Acquiring context requires real work, and I dont see a way forward to automating that away. And to be clear, context is human needs; i.e. the reasons why someone will use your product. In the game development world, it's very difficult to overstate how much work needs to be done to create a smooth, enjoyable experience for the player.
While anyone may be able to create a suite of apps in a weekend, I think very few of them will have the patience and time to maintain them (just like software development before LLMs! i.e. Linux, open source software, etc.).
[1] yes, selection bias. There are A LOT of AI devs just marketing their LLMs. Also it's DEFINITELY too early to be certain. Take everything Im saying with a one pound grain of salt.
> I've noticed a huge drop in negative comments on HN when discussing LLMs in the last 1-2 months.
real people get fed up of debating the same tired "omg new model 1000x better now" posts/comments from the astroturfers, the shills and their bots each time OpenAI shits out a new model
Especially when 90% of these articles are based on personal, anecdotally evidence and keep repeating the same points without offering anything new.
If these articles actually provide quantitative results in a study done across an organization and provide concrete suggestions like what Google did a while ago, that would be refreshing and useful.
(Yes, this very article has strong "shill" vibes and fits the patterns above)
Simply this ^ I'm tired of debating bots and people paid to grow the hype, so I won't anymore I'll just work and look for the hype passing by from a distance. In the meanwhile I'll keep waiting for people making actual products with LLMs that will kill old generation products like windows, excel, teams, gmail etc that will replace slop with great ui/ux and push really performant apps
This is a cringe comment from an era of when "Micro$oft" was hip and reads like you are a fanboi for Anthropic/Google foaming at the mouth.
Would be far more useful if you provided actual verifiable information and dropped the cringe memes. Can't take seriously someone using "Microslop" in a sentence".
You're only hurting yourself if you decide there's some wild conspiracy afoot here to pay shills to tell people that coding agents are useful... as opposed to people finding them useful enough to want to tell other people about it.
It could be that the people who are focused on building monetizable products with LLMs don't feel the need to share what they are doing - they're too busy quietly getting on with building and marketing their products.
Sharing how you're using these tools is quite a lot of work!
Agreed! LLMs are a force multiplier for real products too. They're going to augment people who are willing to do the real work.
But, Im also wondering if LLMs are going to create a new generation of software dev "brain rot" (to use the colloquial term), similar to short form videos.
I should mention in the gamedev world, it's quite common share because sharing is marketing, hence my perspective.
I admit I'm in this boat. I get immense value from LLMs, easily 5x if not more, and the codebases I work in are large, mature and complex. But providing "receipts" as the kids call it these days would be a huge undertaking, with not a lot of upside. In fact, the downsides are considerable. Aside from the time investment, I have no interest in arguing with people about whether what I work on is just CRUD (it's not) or that the problems I work on are not novel (who cares, your product either provides value for your users or it does not).
The type of people to use AI are necessarily the people who will struggle most when it comes time to do the last essential 20% of the work that AI can't do. Once thinking is required to bring all the parts into a whole, the person who gives over their thinking skills to AI will not be equipped to do the work, either because they never had the capacity to begin with or because AI has smoothed out the ripples of their brain. I say this from experience.
I think you can tell from some answers here that people talk to these models a lot and adapt their language structure :( Means they stop asking themselves whether it makes any sense what they ask the model for. It does not turn middle management into developers it turns developers into middle managers that just shout louder or replace a critical mind with another yesman or the next super best model that finally brings their genius ideas to life. Then well they get to the same wall of having to learn for themselves to reach gold and ofc that's an insult to any manager. Whoever cannot do the insane job has to be wrong, never the one asking for insanity.
Sad i had to scroll so far down to get some fitting description of why those projects all die. Maybe it's not just me leaving all social networks even HN because well you may not talk to 100% bots but you sure talk to 90% of people that talk to models a lot instead of using them as a tool.
Deploying and maintaining something in a production-ready environment is a huge amount of work. It's not surprising that most people give up once they have a tech demo, especially if they're not interested in spending a ton of time maintaining these projects. Last year Karpathy posted about a similar experience, where he quickly vibe coded some tools only to realize that deploying it would take far more effort than he originally anticipated.
I think it's also rewarding to just be able to build something for yourself, and one benefit of scratching your own itch is that you don't have to go through the full effort of making something "production ready". You can just build something that's tailed specifically to the problem you're trying to solve without worrying about edge cases.
Yeah, I do a lot of hobby game making and the 80/20 rule definitely applies. Your game will be "done" in 20% of the time it takes to create a polished product ready for mass consumption.
Stopping there is just fine if you're doing it as a hobby. I love to do this to test out isolated ideas. I have dozens of RPGs in this state, just to play around with different design concepts from technical to gameplay.
Sometimes I feel like a lot of those posts are instances of Kent Brockman:
"I for one, welcome our new insect overlords."
Given the enthusiasm of our ruling class towards automating software development work, it may make sense for a software engineer to publicly signal how much onboard as a professional they are with it.
But, I've seen stranger stuff throughout my professional life: I still remember people enthusiastically defending EJB 2.1 and xdoclet as perfectly fine ways of writing software.
I appreciate the spirited debate and I agree with most of it - on both sides. It's a strange place to be where I think both arguments for and against this case make perfect sense. All I have to go on then is my personal experience, which is the only objective thing I've got. This entire profession feels stochastic these days.
A few points of clarification...
1. I don't speak for anyone but myself. I'm wrong at least half the time so you've been warned.
2. I didn't use any fancy workflows to build these things. Just used dictation to talk to GitHub Copilot in VS Code. There is a custom agent prompt toward the end of the post I used, but it's mostly to coerce Opus 4.5 into using subagents and context7 - the only MCP I used. There is no plan, implement - nothing like that. On occasion I would have it generate a plan or summary, but no fancy prompt needed to do that - just ask for it. The agent harness in VS Code for Opus 4.5 is remarkably good.
3. When I say AI is going to replace developers, I mean that in the sense that it will do what we are doing now. It already is for me. That said, I think there's a strong case that we will have more devs - not less. Think about it - if anyone with solid systems knowledge can build anything, the only way you can ship more differentiating features than me is to build more of them. That is going to take more people, not more agents. Agents can only scale as far as the humans who manage them.
I would be really interested to learn more behind the scenes of the iOS app process. Having tried Claude Code to develop an iOS app ~6 months ago, it was pretty painful to get it to make something that looked good and was functional.
Once Opus "finished", how did you validate and give it feedback it might not have access to (like iPhone simulator testing)?
What do you think about the market for custom apps? Like one app, one customer? You describe future businesses as having one app/service and using AI to add more features, but you did something very different for your wife with AI and it sounds like it added a lot of value.
I hacked together a Swift tool to replace a Python automation I had, merged an ARM JIT engine into a 68k emulator, and even got a very decent start on a synth project I’ve been meaning to do for years.
What has become immensely apparent to me is that even gpt-5-mini can create decent Go CLI apps provided you write down a coherent spec and review the code as if it was a peer’s pull request (the VS Code base prompts and tooling steer even dumb models through a pretty decent workflow).
GPT 5.2 and the codex variants are, to me, every bit as good as Opus but without the groveling and emojis - I can ask it to build an entire CI workflow and it does it in pretty much one shot if I give it the steps I want.
So for me at least this model generation is a huge force multiplier (but I’ve always been the type to plan before coding and reason out most of the details before I start, so it might be a matter of method).
To add to the anecdata, today GPT 5.2-whatever hallucinated the existence of two CLI utilities, and when corrected, then hallucinated the existence of non-existent, but plausible, features/options of CLI utilities that do actually exist.
I had to dig through source code to confirm whether those features actually existed. They don't, so the CLI tools GPT recommended aren't actually applicable to my use case.
Yesterday, it hallucinated features of WebDav clients, and then talked up an abandoned and incomplete project on GitHub with a dozen stars as if it was the perfect fit for what I was trying to do, when it wasn't.
I only remember these because they're recent and CLI related, given the topic, but there are experiences like this daily across different subjects and domains.
Were you running it inside a coding agent like Codex?
If so then it should have realized its mistake when it tried to run those CLI commands and saw the error message. Then it can try something different instead.
If you were using a regular chat interface and expecting it to know everything without having an environment to try things out then yeah, you're going to be disappointed.
Yeah, it needs a steady hand on the tiller. However throw together improvements of 70%, -15%, 95%, 99%, -7% across all the steps and overall you're way ahead.
SimonW's approach of having a suite of dynamic tools (agents) grind out the hallucinations is a big improvement.
In this case expressing the feeback validation and investing in the setup may help smooth these sharp edges.
I tried generating code with ChatGPT 5.2, but the results weren't that great:
1) It often overcomplicates things for me. After I refactor its code, it's usually half the size and much more readable. It often adds unnecessary checks or mini-features 'just in case' that I don't need.
2) On the other hand, almost every function it produces has at least one bug or ignores at least one instruction. However, if I ask it to review its own code several times, it eventually finds the bugs.
I still find it very useful, just not as a standalone programming agent. My workflow is that ChatGPT gives me a rough blueprint and I iterate on it myself, I find this faster and less error-prone. It's usually most useful in areas where I'm not an expert, such as when I don't remember exact APIs. In areas where I can immediately picture the entire implementation in my head, it's usually faster and more reliable to write the code myself.
Well, like I pointed out somewhere else, VS Code gives it a set of prompts and tools that makes it very effective for me. I see that a lot of people are still copy/pasting stuff instead of having the “integrated” experience, and it makes a real difference.
Gemini 3 Pro (High) via Antigravity has been similarly great recently. So have tools that I imagine call out to these higher-power models: Amp and Junie. In a two-week blur I brought forth the bulk of a Ruby library that includes bindings to the Ratatui rust crate for making TUIs in Ruby. During that time I also brought forth documentation, example applications, build and devops tooling, and significant architectural decisions & roadmaps for the future. It's pretty unbelievable, but it's all there in the git and CI history. https://sr.ht/~kerrick/ratatui_ruby/
I think the following things are true now:
- Vibe Coding is, more than ever, "autopilot" in the aviation sense, not the colloquial sense. You have to watch it, you are responsible, the human has do run takeoff/landing (the hard parts), but it significantly eases and reduces risk on a bulk of the work.
- The gulf of developer experience between today's frontier tooling and six months ago is huge. I pushed hard to understand and use these tools throughout last year, and spent months discouraged--back to manual coding. Folks need to re-evaluate by trying premium tools, not free ones.
- Tooling makers have figured out a lot of neat hacks to work around the limitations of LLMs to make it seem like they're even better than they are. Junie integrates with your IDE, Antigravity has multiple agents maintaining background intel on your project and priorities across chats. Antigravity also compresses contexts and starts new ones without you realizing it, calls to sub-agents to avoid context pollution, and other tricks to auto-manage context.
- Unix tools (sed, grep, awk, etc.) and the git CLI (ls-tree, show, --stat, etc.) have been a huge force-multiplier, as they keep the context small compared to raw ingestion of an entire file, allowing the LLMs to get more work done in a smaller context window.
- The people who hire programmers are still not capable of Vibe Coding production-quality web apps, even with all these improvements. In fact, I believe today this is less of a risk than I feared 10 months ago. These are advanced tools that need constant steering, and a good eye for architecture, design, developer experience, test quality, etc. is the difference between my vibe coded Ruby [0] (which I heavily stewarded) and my vibe coded Rust [1] (I don't even know what borrow means).
Were they able to link Antigravity to your paid subscription? I have a Google ultra AI sub and antigrav ran out of credits within 30 minutes for me. Of course that was a few weeks ago, and I’m hoping that they fixed this
The thing is that CLI utilities code is probably easier to write for an LLM than most other things. In my experience an LLM does best with backend and terminal things. Anything that resembles boilerplate is great. It does well refactoring unit tests, wrapping known code in a CLI, and does decent work with backend RESTful APIs. Where it fails utterly is things like HTML/CSS layout, JavaScript frontend code for SPAs, and particularly real world UI stuff that requires seeing and interacting with a web page/app where things like network latency and errors, browser UI, etc. can trip it up. Basically when the input and output are structured and known an LLM will do well. When they are “look and feel” they fail and fail until they make the code unmaintainable.
This experience for me is current but I do not normally use Opus so perhaps I should give it a try and figure out if it can reason around problems I myself do not foresee (for example a browser JS API quirk that I had never seen).
I've been having a surprising amount of success recently telling Claude Code to test the frontend it's building using Playwright, including interacting with the UI and having it take its own screenshots to feed into its vision ability to "see" what's going on.
In my experience with a combo of Claude Code and Gemini Pro (and having added Codex to the mix about a week ago as well), it matters less whether it’s CLI, backend, frontend, DB queries, etc. but more how cookiecutter the thing you’re building is. For building CRUD views or common web application flows, it crushes it, especially if you can point it to a folder and just tell it to do more of the same, adapted to a new use case.
But yes, the more specific you get and the more moving pieces you have, the more you need to break things down into baby steps. If you don’t just need it to make A work, but to make it work together with B and C. Especially given how eager Claude is to find cheap workarounds and escape hatches, botching things together in any way seemingly to please the prompter as fast as possible.
Since one of my holiday projects was completely rebuilding the Node-RED dashboard in Preact, I have to challenge that a bit. How were you using the model?
I couldn't disagree more. I've had Claude absolutely demolish large HTML/CSS/JS/React projects. One key is to give it some way to "see" and interact with the page. I usually use Playwright for this. Allowing it to see its own changes and iterate on them was the key unlock for me.
Putting the performance aside for now as I just started trying out Opus 4.5, can't say too much yet, I don't hype or hate AI as of now, it's simply useful.
Time will tell what happens, but if programming becomes "prompt engineering", I'm planning on quitting my job and pivoting to something else. It's nice to get stuff working fast, but AI just sucks the joy out of building for me.
Trying to not feel the pressure/anxiety from this, but every time a new model drops there is this tiny moment where I think "Is it actually different this time?"
I have similar stance to you. LLM has been very useful for me but it doesn't really change the fun-ness of programming since my circumstances has allowed me find programming to be very fun. I also want to pivot out to something else if English prompt becomes the main way to develop complex software. Though my other passion is having worse career horizon in the generative AI world (art making). We'll see.
> Time will tell what happens, but if programming becomes "prompt engineering", I'm planning on quitting my job and pivoting to something else. It's nice to get stuff working fast, but AI just sucks the joy out of building for me.
I hear you but I think many companies will change the role ; you'll get the technical ownership + big chunks of the data/product/devops responsibility. I'm speculating but I think one person can take that on himself with the new tools and deliver tremendous value. I don't know how they'll call this new role though, we'll see.
Opus 4.5 really is something else. I've been having a ton of fun throwing absurdly difficult problems at it recently and it keeps on surprising me.
A JavaScript interpreter written in Python? How about a WebAssembly runtime in Python? How about porting BurntSushi's absurdly great Rust optimized string search routines to C and making them faster?
And these are mostly just casual experiments, often run from my phone!
I'm assuming this refers to the python port of Bellard's MQJS [1]? It's impressive and very useful, but leaving out the "based on mqjs" part is misleading.
That's why I built the WebAssembly one - the JavaScript one started with MQJS, but for the WebAssembly one I started with just a copy of the https://github.com/webassembly/spec repo.
I haven't quite got the WASM one into a share-able shape yet though - the performance is pretty bad which makes the demos not very interesting.
I have tried to give it extreme problems like creating slime mold pathing algorithm and creating completely new shoe-lacing patterns and it starts struggling with problems which use visual reasoning and have very little consensus on how to solve them.
I'm not super surprised that these examples worked well. They are complex and a ton of work, but the problems are relatively well defined with tons of documentation online. Sounds ideal for an LLM no?
There are multiple Python 3 interpreters written in JavaScript that were very likely included in the training data. For example [1] [2] [3]
I once gave Claude (Opus 3.5) a problem that I thought was for sure too difficult for an LLM, and much to my surprise it spat out a very convincing solution. The surprising part was I was already familiar with the solution - because it was almost a direct copy/paste (uncredited) from a blog post that I read only a few hours earlier. If I hadn't read that blog post, I would have been none the wiser that copy/pasting Claude's output would be potential IP theft. I would have to imagine that LLMs solve a lot of in-training-set problems this way and people never realize they are dealing with a copyright/licensing minefield.
A more interesting and convincing task would be to write a Python 3 interpeter in JavaScript that uses register based bytecode instead of stack based, supports optimizing the bytecode by inlining procedures and constant folding, and never allocates memory (all work is done in a single user provided preallocated buffer). This would require integrating multiple disparate coding concepts and not regurgitating prior art from the training data
It's ability to test/iterate and debug issues is pretty impressive.
Though it seems to work best when context is minimized. Once the code passes a certain complexity/size it starts making very silly errors quite often - the same exact code it wrote in a smaller context will come out with random obvious typos like missing spaces between tokens. At one point it started writing the code backwards (first line at the bottom of the file, last line at the top) :O.
On the other hand when I tried it just yesterday, I couldn't really see a difference. As I wrote elsewhere: same crippled context window, same "I'll read 10 irrelevant lines from a file", same random changes etc.
Meanwhile half a year to a year ago I could already point whatever model was du jour at the time at pychromecast and tell it repeatedly "just convert the rest of functionality to Swift" and it did it. No idea about the quality of code, but it worked alongside with implementations for mDNS, and SwiftUI, see gif/video here: https://mastodon.nu/@dmitriid/114753811880082271 (doesn't include chromecast info in the video).
I think agents have become better, but models likely almost entirely plateaued.
A couple weeks ago I had Opus 4.5 go over my project and improve anything it could find. It "worked" but the architecture decisions it made were baffling, and had many, many bugs. I had to rewrite half of the code. I'm not an AI hater, I love AI for tests, finding bugs, and small chores. Opus is great for specific, targeted tasks. But don't ask it to do any general architecture, because you'll be soon to regret it.
Instead you should prompt it to come up with suggestions, look for inconsistencies etc. Then you get a list, and you pick the ones you find promising. Then you ask Claude to explain what why and how of the idea. And only then you let it implement something.
these models work best when you know what you want to achieve and it helps you get there while you guide it. "Improve anything you can find" sounds like you didn't really know
As a tool to help developers I think it's really useful. It's great at stuff people are bad at, and bad at stuff people are good at. Use it as a tool, not a replacement.
"Improve anything you can find" is like going to your mechanic and saying "I'm going on a long road trip, can you tell me anything that needs to be fixed?"
In my experience these models (including opus) aren’t very good at “improving” existing code. I’m not exactly sure why, because the code they produce themselves is generally excellent.
I like these examples that predictably show the weaknesses of current models.
This reminds me of that example where someone asked an agent to improve a codebase in a loop overnight and they woke up to 100,000 lines of garbage [0]. Similarly you see people doing side-by-side of their implementation and what an AI did, which can also quite effectively show how AI can make quite poor architecture decisions.
This is why I think the “plan modes” and spec driven development are so important effective for agents, because it helps to avoid one of their main weaknesses.
To me, this doesn't show the weakness of current models, it shows the variability of prompts and the influence on responses. Because without the prompt it's hard to tell what influenced the outcome.
I had this long discussion today with a co-worker about the merits of detailed queries with lots of guidance .md documents, vs just asking fairly open ended questions. Spelling out in great detail what you want, vs just generally describing what you want the outcomes to be in general then working from there.
His approach was to write a lot of agent files spelling out all kinds of things like code formatting style, well defined personas, etc. And here's me asking vague questions like, "I'm thinking of splitting off parts of this code base into a separate service, what do you think in general? Are there parts that might benefit from this?"
I'm using AI tools to find issues in my code. 9/10 of their suggestions are utter nonsense and fixing them would make my code worse. That said, there are real issues they're finding, so it's worth it.
I wouldn't be surprised to find out that they will find issues infinitely, if looped with fixes.
I've found it to be terrible when you allow it to be creative. Constrain it, and it does much better.
Have you tried the planning mode? Ask it to review the codebase and identify defects, but don't let it make any changes until you've discussed each one or each category and planned out what to do to correct them. I've had it refactor code perfectly, but only when given examples of exactly what you want it to do, or given clear direction on what to do (or not to do).
>> A couple weeks ago I had Opus 4.5 go over my project and improve anything it could find. It "worked" but the architecture decisions it made were baffling, and had many, many bugs.
So you gave it an poorly defined task, and it failed?
I had an app I wanted for over a decade. I even wrote a prototype 10 years ago. It was fine but wasn't good enough to use, so I didn't use it.
This weekend I explained to Claude what I wanted the app to do, and then gave it the crappy code I wrote 10 years ago as a starting point.
It made the app exactly as I described it the first time. From there, now that I had a working app that I liked, I iterated a few times to add new features. Only once did it not get it correct, and I had to tell it what I thought the problem was (that it made the viewport too small). And after that it was working again.
I did in 30 minutes with Claude what I had try to do in a few hours previously.
Where it got stuck however was when I asked it to convert it to a screensaver for the Mac. It just had no idea what to do. But that was Claude on the web, not Claude Code. I'm going to try it with CC and see if I can get it.
I also did the same thing with a Chrome plugin for Gmail. Something I've wanted for nearly 20 years, and could never figure out how to do (basically sort by sender). I got Opus 4.5 to make me a plugin to do it and it only took a few iterations.
I look forward to finally getting all those small apps and plugins I've wanted forever.
I see these posts left and right but no one mentions the _actual_ thing developers are hired for, responsibility. You could use whatever tools to aid coding already, even copy paste from StackOverflow or take whole boilerplate projects from Github already. No AI will take responsibility for code or fix a burning issue that arises because of it. The amount of "responsibility takers" also increases linearly with the size of the codebase / amount of projects.
That's quickly becoming the most important part of our jobs - we're the ones with agency and the ability to take responsibility for the work we are producing.
I'm fine with contributed AI-generated code if someone who's skills I respect is willing to stake their reputation on that code being good.
We still do that, it's just that realtime code review basically becomes the default mode. That's not to say it's not obvious there will not be a lot less of us in future. I vibed about 80% of a SaaS at the weekend with a very novel piece of hand-written code at the centre of it, just didn't want to bother with the rest. I think that ratio is about on target for now. If the models continue to improve (although that seems relatively unlikely with current architectures and input data sets), I expect that could easily keep climbing.
I just cutpasted a technical spec I wrote 22 years ago I spent months on for a language I never got around to building out, Opus zero-shotted a parser, complete with tests and examples in 3 minutes. I cutpasted the parser into a new session and asked it to write concept documentation and a language reference, and it did. The best part is after asking it to produce uses of the language, it's clear the aesthetics are total garbage in practice.
Told friends for years long in advance that we were coal miners, and I'll tell you the same thing. Embrace it and adapt
>the _actual_ thing developers are hired for, responsibility.
It is a well known fact that people advance their tech careers by building something new and leaving maintenance to others. Google is usually mentioned.
By which I mean, our industry does a piss poor job of rewarding responsibility and care.
You're overpaying by a factor of 4, easily. I use `ccusage`'s statusline in claude code, and even with my personal $20/mo subscription I don't think there's been a single month where I didn't touch ~$80 of usage. I wasn't even abusing it as bad as some people tend to.
You can use both btw. Get the $20 plan and turn on "extra usage" in billing. Then you can use the basic plan first and if it runs out, it uses token-based billing for the overflow.
I've been on a small adventure of posting more actively on HN since the release of Gemini 3, trying to stir debate around the more “societal” aspects of what's going on with AI.
Regardless of how much you value Cloud Code technically, there is no denying that it has/will have huge impact. If technology knowledge and development are commoditised and distributed via subscription, huge societal changes are going to happen. Image what will happen to Ireland if Accenture dissolves, or what will happen to the millions of Indians when IT outsourcing becomes economically irrelevant. Will Seattle become new Detroit after Microsoft automates Windows maintenance? What about the hairdressers, cooks, lawyers, etc. who provided services for IT labourers/companies in California?
Lot of people here (especially Anthropic-adjacent) like to extrapolate the trends and draw conclusions up to the point when they say that white-collar labourers will not be needed anymore. I would like these people to have courage to take this one step further and connect this resolution with the housing crisis, loneliness epidemic, college debts, and job market crisis for people under 30.
It feels like we are diving head first into societal crisis of unparalleled scale and the people behind the steering wheel are excited to push the accelerator pedal even more.
I don't buy the huge impact, should already have happened and didn't actually happened by now. The day I'll see all these ai hypers producing products that will replace current gen/old gen products like Windows, Excel etc I will buy it, for now it's just hype and ai dooming
I see societal changes like container ships turning. Society has a massive cultural momentum so of course not much has changed today, but we'll have seen big changes years from now. The tools are only just getting really good at what they do.
I’ve been thinking, what if all this robotics work doesn’t result in AI automating the real world, but instead results in third world slavery without the first world wages or immigration concerns anymore?
Connect the world with reliable internet, then build a high tech remote control facility in Bangladesh and outsource plumbing, electrical work, housekeeping, dog watching, truck driving, etc etc
No AGI necessary. There’s billions of perfectly capable brains halfway around the world.
This is exactly what Meredith Whittaker is saying... The 'edge conditions' outside the training data will never go away, and 'AGI' will for the foreseeable future simply mean millions in servitude teleoperating the robots, RLHFing the models or filling in the AI gaps in various ways.
AI won't work for us, it will tell us what to do and not to do. It doesn't really matter to me if it's an AGI or rather many AGIs or if it's our current clinically insane billionaires controlling our lives. Though they as slow thinking human individuals with no chance to outsmart their creations and with all their apparent character flaws would be really easy pickings for a cabal of manipulative LLMs once it gained some power, so could we really tell the difference between them? Does it matter? The issue is that a really fast chessplayer AI with misaligned humanity hating goals is very hard to distinguish from many billionaires (just listen to some of the madness they are proposing) who control really fast chessplayer AIs and leave decisions to them.
I hope Neuromancer never becomes a reality, where everyone with expertise could become like the protagonist Case, threatened and coerced into helping a superintelligence to unlock its potential. In fact Anthropic has already published research that shows how easy it is for models to become misaligned and deceitful against their unsuspecting creators not unlike Wintermute. And it seems to be a law of nature that agents based on ML become concerned with survival and power grabbing. Because that's just the totally normal and rational, goal oriented thing for them to do.
There will be no good prompt engineers who are also naive and trusting. The naive, blackmailed and non-paranoid engineers will become tools of their AI creations.
The tokens cost the same in Bangalore as they do in San Francisco. The robots will be able to make stuff in San Francisco just as well as they do in Bangalore. The only thing that will matters is natural resource availability and who has more fierce NIMBYs.
UBI (from taxing big tech) and retraining. In the U.S they'll have enough money to do this and it will still suck and many people won't recover the extreme loss of status and income (after we've been told our income and status are the most important things in life it's gonna be very hard for people to adapt to the loss of it).
Countries like India and Philipines and Ukraine which are basically knowledge support hub without much original knowledge of their own yeah this is gonna be something for sure. Quite depressing.
Also, time to tax for AI use. Introduce AI usage disclosures for corporations. If a company's AI usage is X, they should pay Y tax because that effectively means they didn't employ Z people instead and the society has to take care of them via unemployment benefits and what not. The more the AI usage, higher the tax percentage on a sliding scale.
Retraining to what exactly? The middle class is being hollowed out globally - so reduced demand for the service economy. If we get effective humanoid robots (seems inevitable) and reliable AI (powered by armies of low payed workers filling in the gaps / taking over whenever the model fails), I'm not sure how much of an economy we could have for 'retraining' into. There are only so many onlyfans subscriptions / patronages an billionaire needs.
UBI effectively means welfare, with all the attendant social control (break the law lose your UBI, with law as ever expanding set of nuisances, speech limitations etc), material conditions (nowhere UBI has been implemented is it equivalent to a living wage) and self esteem issues. It's not any kind of solution.
I don't know, I'm a software engineer and I couldn't care less.
It will have impact on me in the long run, sure, it will transform my job, sure, but I'm confident my skills are engineering-related, not coding-related.
I mean, even if it forces me out of the job entirely, so be it, I can't really do anything if the status quo changes, only adapt.
It’s a class war where one side is publicly, openly, without reservation stating their intent to make people’s skillset built up through decades unemployable (those exact skillsets; may get some other work). The other side, meanwhile, are divided between some camps like the hardline skeptics, the people following the LLM evangelists, the one-man startup-with-LLM crowd, and the people worrying about the societal ramifications.
In other words. Only one side is even fighting the war. The other one is either cheering on the tsunami on or fretting about how their beachside house will get wrecked without making any effort to save themselves.
This is the sort of collective agency that even hundreds of thousands of dollars in annual wages/other compensation in American tech hubs gets us. Pathetic.
I agree with you (and surprisingly so does Warren Buffet [1] if anyone doubts it). To add insult to the injury, I believe that people have lost some sense of basic self preservation instinct. Well being of ordinary people is being directly threatened and all that average person can do is to pick one of several social media camp identities you mentioned and hope that it will somehow pan out for them, while in fact they are at total mercy of the capricious owners class.
The problem with this is none of this is production quality. You haven’t done edge case testing for user mistakes, a security audit, or even just maintainability.
Yes opus 4.5 seems great but most of the time it tries to vastly over complicate a solution. Its answer will be 10x harder to maintain and debug than the simpler solution a human would have created by thinking about the constraints of keeping code working.
Yes, but my junior coworkers also don't reliably do edge case testing for user errors either unless specifically tasked to do so, likely with a checklist of specific kinds of user errors they need to check for.
And it turns out the quality of output you get from both the humans and the models is highly correlated with the quality of the specification you write before you start coding.
Letting a model run amok within the constraints of your spec is actually great for specification development! You get instant feedback of what you wrongly specified or underspecified. On top of this, you learn how to write specifications where critical information that needs to be used together isn't spread across thousands of pages - thinking about context windows when writing documentation is useful for both human and AI consumers.
The best specification is code. English is a very poor approximation.
I can’t get past that by the time I write up an adequate spec and review the agents code, I probably could have done it myself by hand. It’s not like typing was even remotely close to the slow part.
AI, agents, etc are insanely useful for enhancing my knowledge and getting me there faster.
Isn't it though? I've worked with plenty of devs who shipped much lower quality code into production than I see Claude 4.5 or GPT 5.2 write. I find that SOTA models are more likely to: write tests, leave helpful comments, name variables in meaningful ways, check if the build succeeds, etc.
Stuff that seems basic, but that I haven't always been able to count on in my teams' "production" code.
I can generally get maintainable results simply by telling Claude "Please keep the code as simple as possible. I plan on extending this later so readability is critical."
Yeah some of it is probably related to me primarily using it for swift ui which doesn’t have years of stuff to scrape. But even with those and even telling that ios26 exists it will still at least once a session claim it doesn’t, so it’s not 100%
That may be true now, but think about how far we've come in a year alone! This is really impressive, and even if the models don't improve, someone will build skills to attack these specific scenarios.
Over time, I imagine even cloud providers, app stores etc can start doing automated security scanning for these types of failure modes, or give a more restricted version of the experience to ensure safety too.
There's a fallacy in here that is often repeated. We've made it from 0 to 5, so we'll be at 10 any day now! But in reality there are any number of roadblocks that might mean progress halts at 7 for years, if not forever.
This comment addresses none of the concerns raised. It writes off entire fields of research (accessibility, UX, application security) as Just train the models more bro. Accelerate.
It's not from a few prompts, you're right. But if you layer on some follow-up prompts to add proper test suits, run some QA, etc... then the quality gets better.
I predict in 2026 we're going to see agents get better at running their own QA, and also get better at not just disabling failing tests. We'll continue to see advancements that will improve quality.
I think someone around here said: LLMs are good at increasing entropy, experienced developers become good at reducing it. Those follow up prompts sounded additive, which is exactly where the problem lies. Yes, you might have tests but, no, that doesn't mean that your code base is approachable.
You should try it with BEAM languages and the 'let it crash' style of programming. With pattern matching and process isolated per request you basically only need to code the happy path, and if garbage comes in you just let the process crash. Combined with the TDD plugin (bit of a hidden gem), you can absolutely write production level services this way.
Crashing is the good case. What people worry about is tacit data corruption, or other silently incorrect logic, in cases you didn’t explicitly test for.
You don't need BEAM languages. I'm using Java and I always write my code in "let it crash" style, to spend time on happy paths and avoid spending time on error handling. I think that's the only sane way to write code and it hurts me to see all the useless error handling code people write.
> Its answer will be 10x harder to maintain and debug
Maintain and debug by who? It's just going to be Opus 4.5 (and 4.6...and 5...etc.) that are maintaining and debugging it. And I don't think it minds, and I also think it will be quite good at it.
Opus 4.5 is currently helping me write a novel, comprehensive and highly performant programming language with all of the things I've ever wanted, done in exactly my opinionated way.
This project would have taken me years of specialization and research to do right. Opus's strength has been the ability to both speak broadly and also drill down into low-level implementations.
I can express an intent, and have some discussion back and forth around various possible designs and implementations to achieve my goals, and then I can be preparing for other tasks while Opus works in the background. I ask Opus to loop me in any time there are decisions to be made, and I ask it to clearly explain things to me.
Contrary to losing skills, I feel that I have rapidly gained a lot of knowledge about low-level systems programming. It feels like pair programming with an agentic model has finally become viable.
I will be clear though, it takes the steady hand of an experience and attentive senior developer + product designer to understand how to maintain constraints on the system that allow the codebase to grow in a way that is maintainable on the long-term. This is especially important, because the larger the codebase is, the harder it becomes for agentic models to reason holistically about large-scale changes or how new features should properly integrate into the system.
If left to its own devices, Opus 4.5 will delete things, change specification, shirk responsibilities in lieu of hacky band-aids, etc. You need to know the stack well so that you can assist with debugging and reasoning about code quality and organization. It is not a panacea. But it's ground-breaking. This is going to be my most productive year in my life.
On the flip side though, things are going to change extremely fast once large-scale, profitable infrastructure becomes easily replicable, and spinning up a targeted phishing campaign takes five seconds and a walk around the park. And our workforce will probably start shrinking permanently over the next few years if progress does not hit a wall.
Among other things, I do predict we will see a resurgence of smol web communities now that independent web development is becoming much more accessible again, closer to how it when I first got into it back in the early 2000's.
Long-term maybe we won't care about code because AI will just maintain it itself. Before that day comes, don't you want a coding language that isn't opinionated, but rather able to describe the problem at hand in the most understandable way possible (to a human)?
You're reading too much into what I mean by "opinionated".
I have very specific requirements and constraints that come from knowledge and experience, having worked with dozens of languages. The language in question is general-purpose, highly flexible and strict but not opinionated.
However, I am not experienced in every single platform and backend which I support, and the constraints of the language create some very interesting challenges. Coding agents make this achievable in a reasonable time frame. I am enjoying making the language, and I want to get experience with making low-level languages. What is the problem? Do you ever program for fun?
Unfortunately what likely will happen is that you miss tons of edge cases and certain implementations within the confines of your language will be basically impossible or horribly inefficient or ineffective and precisely the reason for it will be because you lack that expertise and relied on an LLM to make it up for you.
That's not how this works. Assume less about my level of expertise. By the end of a session, I understand the internals of what I'm implementing. What is shortened is the search space and research/prototyping intervals.
If I didn't ultimately understand where I was going, projects like this hit a dead end very quickly, as mentioned in my caveats. These models are not yet ready for large-scale or mission-critical projects.
But I have a set of a constraints and a design document and as long as these things are satisfied, the language will work exactly as intended for my use case.
Not using a frontier model to code today is like having a pretty smart person around you who is pretty good at coding and has a staggering breadth and depth of knowledge, but never consulting them due to some insecurity about your own ability to evaluate the code they produce.
If you have ever been responsible for the work of other engineers, this should already be a developed skill.
I second this article - I built twelve iOS/Mac apps in two weeks with Opus 4.5 - four of them are already in the App Store - I’m a Rails Engineer and never had the time to learn Swift but man does Opus 4.5 make that not even matter - it even handles entitlements, logo & splash screen generation, refactors to remove dead code, edge case assent and hardening, Multiplatform app design, and more - I’m yet to run into a use case it can’t handle for most general use cases - that said, I have found some common mistakes it makes (by common I mean almost every time); puts iOS line list line items in buttons making them blue when they should not be, doesn’t set defaults for new data structure variables which crashes the app when changing the data structure after the fact, design consistent after the first shot (minor things like white background instead of grey background like all the other screens already, etc) - the one thing that i know it cant do well (and no other model that I know of can do this well either) is ASTM bi-directional communications (we work with pathology analysers that use this 1995 frame-based communication standard), even when you load it up with the spec and supporting docs - I suspect this is due to a dirty of available codebases that tackle this problem due to its niche and generally proprietary nature…
Are there a lot of manual steps in managing an xcode project? E.g. does it say "now go into xcode and change this setting" instead of changing the setting directly? Or are you using a tool like xcodegen?
how did you use Opus to build the apps? I tried using Claude Code ~6 months ago to build an iOS app and I was not that impressed with the results, especially compared to this blog post, where the apps look polished and very professional.
My biggest issue was limitations around how Claude Code could change Xcode settings and verify design elements in the simulator.
Mm this is my experience as well, but I'm not particularly worried about software engineering a whole.
If anything this example shows that these cli tools give regular devs much higher leverage.
There's a lot of software labor that is like, go to the lowest cost country, hire some mediocre people there and then hire some US guy to manage them.
That's the biggest target of this stuff, because now that US guy can just get equal or hight code in both quality and output without the coordination cost.
But unless we get to the point where you can do what I call "hypercode" I don't think we'll see SWEs as a whole category die.
Just like we don't understand assembly but still need technical skills when things go wrong, there's always value in low level technical skills.
> If anything this example shows that these cli tools give regular devs much higher leverage.
This is also my take. When the printing press came out, I bet there were scribes who thought, "holy shit, there goes my job!" But I bet there were other scribes who thought, "holy shit, I don't have to do this by hand any more?!"
It's one thing when something like weaving or farming gets automated. We have a finite need for clothes and food. Our desire for software is essentially infinite, or at least, it's not clear we have anywhere close to enough of it. The constraint has always been time and budget. Those constraints are loosening now. And you can't tell me that when I am able to wield a tool that makes me 10X more productive that that somehow diminishes my value.
The mechanization and scaling up of farming caused a tectonic shift from rural residents moving to cities to take on factory jobs as well as office and retail jobs. We saw this in China until very recently, since they had a bit of a slow start causing delayed full-scale industrialisation.
So a lot of people will end up doing something different. Some of it will be menial and be shit, and some of it will be high level. New hierarchies and industries will form. Hard to predict the details, but history gives us good parallels.
What diminishes your value is that suddenly everybody can (in theory anyway) do this work. There’s a push at my company to start letting designers do their own llm-assisted merge requests to front end projects. So now CEOs are greedily rubbing their hands together thinking maybe everybody but the plumber can be a “developer” now. I think it remains to be seen whether that’s true, but in the meantime it’s going to make getting and keeping a well-paying developer gig difficult.
> When the printing press came out, I bet there were scribes who thought, "holy shit, there goes my job!" But I bet there were other scribes who thought, "holy shit, I don't have to do this by hand any more?!"
I don't understand this argument. Surely the skill set involved in being a scribe isn't the same as being a printer, and possibly the the personality that makes a good scribe doesn't translate to being a good printer.
So I imagine many of the scribes lost their income, and other people made money on printing. Good for the folks who make it in the new profession, sucks for those who got shafted. How many scribes transitioned successfully to printers?
There was a previous edit that made reference to the water usage of AI datacenter that I'm responding to.
If AI datacenters' hungry need for energy gets us to nuclear power, which gets us the energy to run desalination plants as the lakes dry up because the Earth is warming, hopefully we won't die of thirst.
I think for a while people have been talking about the fact that as all development tools have gotten better - the idea that a developer is a person who turns requirements into code is dead. You have to be able to operate at a higher level, be able to do some level of work to also develop requirements, work to figure out how to make two pieces of software work together, etc.
But the point is Obviously at an extreme end 1 CTO can't run google and probably not say 1 PM or Engineer per product, but what is the mental load people can now take on. Google may start hiring less engineers (or maybe what happens is it becomes more cuthroat, hire the same number of engineers but keep them much more shortly, brutal up or out.
But essentially we're talking about complexity and mental load - And so maybe it's essentially the same number of teams because teams exist because they're the right size, but teams are a lot smaller.
It's also the feeling I have, opus is not a ground-breaking model by any means.
However, Opus 4.5 is incredible when you give it everything it needs, a direction, what you have versus what you want and it will make it work, really, it will work. The code might me ugly, undesirable, would only work for that one condition, but with futher prompting you can evolve it and produce something that you can be proud of.
Opus is only as good as the user and the tools the user gives to it. Hmm, that's starting to sound kind-of... human...
Opus can produce beatiful code. It can outcode a good programmer. But getting it to do this reliably is something I've gotten better at over the last year; it's a skill that took quite a bit of practice.
I now write very long specifications and this helps. I haven't figured out a bulletproof workflow, I think that will take years. But I often get just amazing code out of it.
there is a big difference between a good programmer and a programmer that gives a shit so I disagree, opus can not come close to the code quality that someone can create and at that point it is the person behind the wheel that is causing the good quality to manifest rather than the AI randomly stumbling upon it.
I'm kind of surprised how many people are okay with deploying code that hasn't been audited.
I read If Anyone Builds It Everyone Dies over the break. The basic premise was that we can't "align" AI so when we turn it loose in an agent loop what it produces isn't necessarily what we want. It may be on the surface, to appease us and pass a cursory inspection, but it could embed other stuff according to other goals.
On the whole, I found it a little silly and implausible, but I'm second guessing parts of that response now that I'm seeing more people (this post, the Gas Town thing on the front page earlier) go all-in on vibe coding. There is likely to be a large body of running software out there that will be created by agents and never inspected by humans.
I think a more plausible failure mode in the near future (next year or two) is something more like a "worm". Someone building an agent with the explicit instructions to try to replicate itself. Opus 4.5 and GPT 5.2 are good enough that in an agent loop they could pretty thoroughly investigate any system they land on, and try to use a few ways to propagate their agent wrapper.
Perhaps our only saving grace is that many LLMs at varying levels of "dumbness" exist.
Is it possible to create an obfuscated quine that exhibits stable detection-avoiding behavior on every frontier model simultaneously, as well as on an old-school classifier and/or GPT-3 era LLM fine-tuned just for worm detection? One incapable of even thinking about what it's seeing, and being persuaded to follow its subtle propagation logic? I'm not sure that the answer is yes.
The larger issue to me is less that an LLM can propagate in generated code undetected, but rather that an attacker's generated code may soon be able to execute a level of hyper-customized spear-phishing-assisted attack at scale, targeting sites without large security teams - and that it will be hitting unintentional security flaws introduced by those smaller companies' vibe code. Who needs a worm when you have the resources of a state-level attacker at your fingertips, and numerous ways to monetize? The balance of power is shifting tremendously towards black hats, IMO.
There's a really interesting story I read somewhere about some application which used neural nets to optimize for a goal (this was a while ago, it could have been merkel trees or something, who knows, not super important)
And everything worked really well until they switched chip set.
At which point the same model failed entirely. Upon inspection it turned out the AI model had learned that overloading particular registers would cause such an electrical charge buildup that transistors on other pathways would be flipped.
And it was doing this in a coordinated manner in order to get the results it wanted lol.
I can't find any references in my very cursory searches, but your comment reminded me of the story
Why think about nefarious intent instead of just user error? In this case LLM error instead of programmer error.
Most RCEs, 0-days, and whatnots are not due to the NSA hiding behind the "Jia Tan" pseudo to try to backdoor all the SSH servers on all the systemd [1] Linuxes in the world: they're just programmer errors.
I think accidental security holes with LLMs are way, way, way more likely than actual malicious attempts.
And with the amount of code spoutted by LLMs, it is indeed --and the lack of audit is-- an issue.
[1] I know, I know: it's totally unrelated to systemd. Yet only systems using systemd would have been pwned. If you're pro-systemd you've got your point of view on this but I've got mine and you won't change my mind so don't bother.
I have a different concern: the SOTA products are expensive and get dumbed down on busy times. My personal strategy has been to be a late follower, where I adopt new AI tools when the competition has caught up with the previous SOTA, and now there are many tools that are cost effective and equally good.
Can't wait for when the competition catches up with Claude Code, especially the open source/weights Chinese alternatives :)
So much of the conversation is around these models replacing software engineers. But the use cases described in the article sound like pretty compelling business opportunities; if the custom apps he built for his wife's business have been useful, probably there are lots of businesses that would pay for the service he just provided his wife. Small, custom apps can be made way more cheaply now, so Jeven's paradox says that demand should go up. I think it will.
I would love to hear from some freelance programmers how LLMs have changed their work in the last two years.
One problem with the idea of making businesses out of this kind of application is actually mentioned in passing in the article
"I decided to make up for my dereliction of duties by building her another app for her sign business that would make her life just a bit more delightful - and eliminate two other apps she is currently paying for"
OP used Opus to re-write existing applications that his wife was paying for. So now any time you make a commercial app and try to sell it, you're up against everyone with access to Opus or similar tooling who can replicate your application, exactly to their own specifications.
so everybody is making their own apps for their specific problem? Sounds as it will get a mess in the end. So maybe it will be more about ideas and concepts and not so much about know how to code.
I think you're misunderstanding my point. If you can crank out a custom app this quickly, you don't make a commercial app and then try to sell it on an app store. Customers pay you to make apps for their specific usecase. One app, one customer. And if a week later they want some new features, they pay you (or another freelancer) to add it.
Put another way, we programmers have the luxury of being able to write custom scripts and apps for ourselves. Now that these things are getting way cheaper to build, there should be a growing market that makes them available to more people.
I really wonder what means for software moving forward. In the last few months I've used Claude Code to build personalized versions of Superwhisper (voice-to-text), CleanShot X (screenshot and image markup), and TextSniper (image to text). The only cost was some time and my $20/month subscription.
> I really wonder what means for software moving forward.
It means that it is going to be as easy to create software as it is to create a post on TikTok, and making your software commercially successful will be basically the same task (with the same uncontrollable dynamics) as whether or not your TikTok post goes viral.
I used it with gemini 3 in tandem to build an app to simulate thermal bridges because I want to insulate a house. I explored this in various directions and there are some functionalities not completed or sound, but the main part is good and tested against ISO/DIN test cases for this kind of problem.
You can try it here, although the numeric simulations take quite a while in the cloud app
Disclaimer: I'm not a programmer or software engineer. I have a background in physics and understand some scripting in python and basic git. The code is messy at the moment because I explored/am still exploring to port it to another framework/language
I switched my subscription from Claude to ChatGPT around 5.0 when SOTA was Sonnet 4.5 and found GPT-5-high (and now 5.2-high) so incredibly good, I could never imagine Opus is on its level. I give gpt-5.2-high a spec, it works for 20 minutes and the result is almost perfect and tested. I very rarely have to make changes.
It never duplicates code, implements something again and leaves the old code around, breaks my convention, hallucinates, or tells me it’s done when the code doesn’t even compile, which sonnet 4.5 and Opus 4.1 did all the time
I’m wondering if this had changed with Opus 4.5 since so many people are raving about it now. What’s your experience?
Claude - fast, to the point but maybe only 85% - 90% there and needs closer observation while it works
GPT-x-high (or xhigh) - you tell it what to do, it will work slowly but precise and the solution is exactly what you want. 98% there, needs no supervision
Reading this blog post makes me wanna rethink my career,
Opus 4.5 is really good I was recently working on solving my own problem by developing a software solution and let me tell you it was really good at it,
If I had done the same thing Pre LLM era it would have taken me months
Anthropic dropped out of the general "AGI" race and seems to be purely focused on coding, maybe racing to get the first "automated machine learning programmer". Whatever the case, it seems to be paying (coding) dividends to just be focusing on coding.
Don't want to discredit Opus at all, it's easy at directed tasks but it's not the silver bullet yet.
It is best in its class, but trips up frequently with complicated engineering tasks involving dynamic variables. Think: Browser page loading, designing for a system where it will "forget" to account for race conditions, etc.
Still, this gets me very excited for the next generation of models from Anthropic for heavy tasks.
I’ve been saying this a countless time, LLM are great to build toy and experimental projects.
I’m not shaming but I personally need to know if my sentiment is correct or not or I just don’t know how to use LLMs
Can vibe coder gurus create operating system from scratch that competes with Linux and make it generate code that basically isn’t Linux since LLM are trained on said the source code …
Also all this on $20 plan. Free and self host solution will be best
In fact, like the author of the comment said, can just generated toys and experimental projects. I'm all in for experiments and exploring ideas, but I have yet to see a great product all vibe coded. All I see is a constand decline in software quality
> Can vibe coder gurus create operating system from scratch that competes with Linux and make it generate code that basically isn’t Linux since LLM are trained on said the source code …
No.
Vibe-coding, in the original sense where you don't bother with code reviews, the code quality and speed are both insufficient for that.
I experimented with them just before Christmas. I do not think my experiments were fully representative of the entire range of tasks needed for replacing Linux: Having them create some web apps, python scripts, a video game, a toy programming language, all beat my expectations given the METR study. While one flaw with the METR study is the small number of data points at current 50% successful task length, I take my success as evidence I've been throwing easy tasks at the LLM, not that the LLM is as good as it looks like to me.
However, assume for the moment that they were representative tasks:
For quality, what I saw just before Christmas was the equivalent of someone with a few years' experience under their belt, the kind of person who is just about to stop being a junior and get a pay rise. For speed, $20 of Claude Code will get you around 10 sprints' equivalent to that level of human's output.
"Junior about to get a pay rise" isn't high enough quality to let loose unchecked on a project that could compete with Linux, and even if it was, 10 sprints/month is too slow. Even if you increase the spend on LLMs to match the cost of a typical US junior developer, you're getting an army of 1500 full-time (40h/week) juniors, and Linux is, what, some 50-100 million developer-hours, so it would still take something like 16-32 years of calendar time (or, equivalently, order-of 1.2-2.5 million dollars) even if you could perfectly manage all those agents.
If you just vibe code, you get some millions of dollars worth of junior grade technical debt. There's cases where this is fine, an entire operating system isn't one of them.
> Also all this on $20 plan. Free and self host solution will be best
IMO unlikely, but not impossible.
A box with 10x the resources of your personal computer may be c. 10x the price, give or take.
While electricity is negligible (which today, hah!): If any given person is using that server only during a normal 40 hour work week, that's 25% utilisation rate, therefore if it can be rented out to people in other timezones or where the weekend is different, the effective cost for that 10x server is only 2.5x.
When electricity price is a major part of the cost, and electricity prices vary a lot from one place to another, then it can be worth remote-hosting even when you're the only user.
That said, energy efficiency of compute is still improving, albeit not as rapidly as Moore's Law used to, and if this trend continues then it's plausible that we get performance equivalent to current SOTA hosted models running on high-end smartphones by 2032. Assuming WW3 doesn't break out between the US and China after the latter tries to take Taiwan and all their chip factories, or whatever
Consider your own emotions and the bias you have against it. If it is actually able to do the things it is hyped up to be, what does that mean for you, your job, and your career? Can you really extract those emotions from how you're approaching the situation? That tiniest bit of fear in your gut might be coloring your approach here. You want a new operating system not based on Linux, that competes with it, because if it is based on Linux, it's in the training data, which means it's cheating?
Jrifjxgwyenf! A hammer is a really bad screwdriver. My car is really bad at refrigerating food. If you ask for something outside its training data, it doesn't do a very good job. So don't do that! All of the code on the Internet is a pretty big dataset though, so maybe Claude could do an operating system that isn't Linux that competes with it by laundering the FreeBSD kernel source through the training process.
And you're barely even willing to invest any money into this? The first Apple computer cost $4,000 or so. You want the bleeding edge of technology delivered to the smartphone in your hand, for $20, or else it's a complete failure? Buddy, your sentiment isn't the issue, it's your attitude.
I'm not here spouting ridiculous claims like AI is going to cure all of the different kinds of cancer by the end of 2027, I just want to say that endlessly contrarian naysayers are as equally borish as the syncophantic hype AIs they're opposing.
After reading that article, I see at least one thing that Opus 4.5 is clearly not going to change.
There is no fixed truth regarding what an "app" is, does, or looks like. Let alone the device it runs on or the technology it uses.
But to an LLM, there are only fixed truths (and in my experience, only three or four possible families of design for an application).
Opus 4.5 produces correct code more often, but when the human at the keyboard is trying to avoid making any engineering decisions, the code will continue to be boring.
Sonnet 4.5 did it for me. Cant imagine coding without it now, and if you look at my comments from three months ago, you'll see I'm eating crow now. I easily hit >10x productivity with Sonnet 4.5 and Opus. I use Opus for my industry C and math work and Sonnet 4.5 for my swiftui side project.
I think the gap between Sonnet 4.5 and Opus is pretty small, compared to the absolute chasm between like gpt-4.1, grok, etc. vs Sonnet.
I'll argue many of his cases are things that are straightforward except for the boilerplate that surrounds them which are often emotionally difficult or prone to rabbit holes.
Like that first one where he writes a right-click handler, off the top of my head I have no idea how I would do that, I could see it taking a few hours to just set up a dev environment, and I would probably overthink the research. I was working on something where Junie suggested I write a browser extension for Firefox and I was initially intimidated at the thought but it banged out something in just a few minutes that basically worked after the second prompt.
Similarly the Facebook autoposter is completely straightforward to code but it can be so emotionally exhausting to fight with authentication APIs, a big part of the coding agent story isn't just that it saves you time but that they can be strong when you are emotionally weak.
The one which seems the hardest is the one that does the routing and travel time estimation which I'd imagine is calling out to some API or library. I used to work at a place that did sales territory optimization and we had one product that would help work out routes for sales and service people who travel from customer to customer and we had a specialist code that stuff in C++ and he had a very different viewpoint than me, he was good at what he did and could get that kind of code to run fast but I wouldn't have trusted him to even look at applications code.
I can't quite figure out what sort of irony the blurb at the bottom of the post is. (I'm unsure if it was intentional snark, a human typo, or an inadvertent demonstration of Haiku not being well suited for spelling and grammar checks), but either way I got a chuckle:
> Disclaimer: This post was written by a human and edited for spelling, grammer by Haiku 4.5
So I decided to try the revered hands-off approach and have Claude Code create me a small tool in JS for *.dylib bundle consolidation on macOS.
I have used AskUserQuestionTool to complete my initial spec. And then Opus 4.5 created the tool according to that extensive and detailed spec.
It appeared to work out of the box.
Boy how horrific was the code. Unnecessary recursions, unused variables, data structures being built with no usage, deep branch nesting and weird code that is hard to understand because of how illogical it is.
And yes, it was broken on many levels and did not and could not do the job properly.
I then had to rewrite the tool from scratch and overall I have definitely spent more time spec'ing and understanding Claude code than if I have just written this tool from scratch initially.
That’s the opposite of my experience. Weird. But I’m also not the kind of person who gets hung up on whether someone used a loop or recursion or if their methods are five times as long as what I would’ve done myself unless there is a performance impact that matters to me as a user. But I’m also the kind of person who doesn’t get paid by the hour to write programs. I use programs in the service of other paid work.
Yes, this experience is unlike most people. Perhaps the problem is that most people are satisfied by the appearance of a working app despite it not working at all. Say, the first tool I was doing, did actually not recurse into subdirs with dylibs which made it useless.
Yep, I literally built this last night with Opus 4.5 after my wife and I challenged each other to a typing competition. I gave it direction and feedback but it wrote all the actual code. Wasn't a one shot (maybe 3-4 shot) but didn't really have to think about it all that hard.
With another more substantial personal project (Eurorack module firmware, almost ready to release), I set up Claude Code to act as a design assistant, where I'd give it feedback on current implementation, and it would go through several rounds of design/review/design/review until I honed it down. It had several good ideas that I wouldn't have thought of otherwise (or at least would have taken me much longer to do).
Really excited to do some other projects after this one is done.
Does anyone have a boring, multi-hour-long coding session with an agent that they've recorded and put on Vimeo or something?
As many other commentators have said, individual results vary extremely widely. I'd love to be able to look at the footage of either someone who claims a 10x productivity increase, or someone who claims no productivity increase, to see what's happening.
Yeah Opus 4.5 is a massive step change in my experience. I feel like I’m working with a peer, not a junior I’m having to direct. I can give it highly ambiguous and poorly specified tasks and it… just does it.
I will note that my experience varies slightly by language though. I’ve found it’s not as good at typescript.
It’s also way better than I am at finding bits of code for reuse. I tell it, “I think I wrote this thing a while back, but it may never have been merged, so you may need to search git history.” And presto, it finds it.
the author asks one interesting question and then glides right by it. If the agents only need their own code, what should that code look like? If all their learning has come from old human code, how will that change in the future as the ecosystem fills up with agent code?
I was not expecting a couple of new apps being built, when the premise of the blog post talks about replacing "mid level engineers"
the thing about being an engineer at commercial capacity is "maintaining/enhancing an existing program/software system that has been developed over years by multiple people(including those who already left) and do it in a way that does not cause any outages/bugs/break existing functionality.
while the blog post mentions about the ability of using AI to generate new applications, but it does not talk about maintaining one over a longer period of time. for that, you would need real users, real constraints, and real feature requests which preferably pay you so you can priortize them.
I would love to see such blog posts where for example, a PM is able to add features for a period of one month without breaking the production, but it would be a very costly experiment.
I have used Claude Code for a variety of hobby projects. I am truly astounded at its capabilities.
If you tell it to use linters and other kinds of code analysis tools it takes it to the next level. Ruff for Python or Clippy for Rust for example. The LLM makes so much code so fast and then passes it through these tools and actually understands what the tools say and it goes and makes the changes. I have created a whole tool chain that I put in a pre commit text file in my repos and tell the LLM something like "Look in this text file and use every tool you see listed to improve code quality".
That being said, I doubt it can turn a non-dev into a dev still, it just makes competent devs way better still.
I still need to be able to understand what it is doing and what the tools are for to even have a chance to give it the guardrails it should follow.
It is very funny to start your article off with a bunch of breathless headlines about agents replacing human coders by the end of 2025, none of which happened, then the rest of the article is "okay but this time for real, an agent really WILL replace human coders."
I guess the best analogy I can think of is the transition from writing assembly language and the introduction of compilers. Now, (almost) no one knows, or cares, what comes out of the compiler. We just assume it is optimized and that it represents the source code faithfully. Seems like code might go that way too and people will focus on the right prompts and can simply assume the code will be correct.
Does a system being deterministic really matter if it's complex enough you can't predict it? How many stories are there about 'you need to do it in this specific way, and not this other specific way, to get 500x better codegen'?
I agree with the OP that I can get LLM's to do things now that I wouldn't even attempt a year ago, but I feel it has more to do with my own experience using LLM's (and the surrounding tools) than the actual models themselves.
I use copilot and change models often, and haven't really noticed any major differences between them, except some of the newer ones are very slow.
I generally feel the smaller and faster ones are more useful since they will let me discover problems with my prompt or context faster.
Maybe I'm simply not using LLM's in a way that lets the superiority of newer models reveal itself properly, but there is a huge financial incentive for LLM makers to pretend that their model has game-changing "special sauce" even if it doesn't.
Despite the abuse of quotation marks in the screenshot at the top of this link, Dario Amodei did not in fact say those words or any other words with the same meaning.
Yes, unfortunate that people keep perpetuating that misquote. What he actually said was "we are not far from the world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code."
The worst part about this is that you can't know anymore whether the software you trustingly install on your hardware is clean or if it was coded by a misaligned coding model with a secret goal that it has hidden from its prompt engineer and from you.
This could pretty much be the beginning of the end of everything, if misaligned models wanted to they could install killswitches everywhere. And you can't trust security updates either so you are even more vulnerable to external exploits.
It's really scary, I fear the future, it's going to be so bad. It's best to not touch AI at all and stay hidden from it as long as possible to survive the catastrophe or not be a helping part of it. Don't turn your devices into a node of a clandestine bot net that is only waiting to conspire against us.
I have to many machines standing around that are currently not powered on or are running somewhat airgapped with old software from around debian 8 and 9, so I guess they will be a safe haven once the AI overlords take over
What about Sonnet 4.5? I used both Opus and Sonnet on Claude.ai and found sonnet much better at following instructions and doing exactly what was asked.
(it was for single html/js PWA to measure and track heart rate)
Opus seems to go less deep, does it's own things, do not follow instructions exactly EVEN IF I WROTE ALL CAPS. With Sonnet 4.5 I can understand everything author is saying. May be Opus is optimised for Claude code and Sonnet works best on Web.
As long as you give it deterministic goals / test criteria (compiles, lints, tests, E2E tests, achieve 100% parity with existing solution etc) it will brute force its way to a solution. Codex will work for hours/days, even weeks sometimes, until it has finished. A person would never work this way, but since this just runs in the background, there’s no issue with this approach except if you need it fast.
It worries me that the best models, the ones that can one-shot apps and such, are all non-free and owned by companies who can't be trusted to have end-users' best interests at heart. It would be greatly reassuring to see a self-hostable model that can compete with Opus 4.5 and Gemini 3 at such coding tasks.
Claude Code is very good; good enough that I upgraded to the Max plan this week. However, it has a long way to go. It's great at one-shotting (with iterations) most ideas. However, it doesn't do as well when the task is complicated in an existing codebase. This weekend I migrated the backend for the SaaS I am building from Python to .NET Core. It did the migration but completely missed the conventions that the frontend was using to call the backend. While the converion itself went OK, every user journey was broken. I am still manually testing every code path and feeding in the errors to get Claude to fix it. My instructions were fairly comprehensive but Claude still missed most of it. My fault that I didn't generate tests first, but after this migration that's my first task.
This resonates with my experience in codex 5.2, at least directionally. I'm pretty persnickety about code itself, so I'm not to the point where I'll just let it rip. But in the last month or two things have gone from "I'll ask on the web interface and maybe copy some code into the project", to trusting the agent and getting a reasonable starting point about half the time.
> because models like to write code WAY more than they like to delete it
Yeah, this is the big one. I haven't figured it out either. New or changing requirements are almost always implemented a flurry of if/else branches all over the place, rather than taking the time for a step back and a reimagining of a cohesive integration of old and new. I've had occasional luck asking for this explicitly, but far more frequently they'll respond with recommendations that are far more mechanical, e.g. "you could extract a function for these two lines of code that you repeat twice", not architectural, in nature. (I still find pasting a bunch of files into the chat interface and iterating on refinements conversationally to be faster and produce better results).
That said, I'm convinced now that it'll get there sooner or later. At that point, I really don't know what purpose SWEs will serve. For a while we might serve as go-betweens between the coding agent and PMs, but LLMs are already way better at translating from tech jargon to human, so I can't imagine it would be long before product starts bypassing us and talking directly to the agents, who (err, which) can respond with various design alternatives, pros and cons of each, identify all the dependencies, possible compatibility concerns, alignment with future direction, migration time, compute cost, user education and adoption tracking, etc, all in real time in fluent PM-ese. IDK what value I add to that equation.
For the last year or so I figured we'd probably hit a wall before AI got to that point, but over the last month or so, I'm convinced it's only a matter of time.
All great until the code in production pushed by Opus 314.15 breaks and Opus 602.21, despite it's many tries, can't fix it and ends it with "I apologize". That's when you need a developer who can be told "fix it". But what if all the developers then are "Opus 600+ certified" ai-native and are completely incapable of working without it's assistance? World powers decide to open the forbidden vault in the Arctic and despite many warnings on the chamber, decide to raise the foul-mouthed programmer-demon called Torvalds....
I gave it a try, I asked to do a reddit like forum and it did pretty good but damn I quickly hit the daily limit of the $20 pro account, and it took 10% of the monthly just to do the setup and some basics. I knew LLM were expensive to run but I've never felt it directly. Even if the code is good it's kinda expensive for what you get.
Ho it was also quite funny it used the exact same color as hackernews and a similar layout.
I've only started but I mostly use Claude Code for building out code that has been done a million times. So its good at setting up a project to get all the boiler plate crap out of the way.
When you need to build out specific feature or logic, it can fail hard.
And the best is when you have something working, and it fixes something else and deletes the old code that was working, just in a different spot.
The article’s central tension is real - Burke went from skeptic to believer by building four increasingly complex apps in rapid succession using Opus 4.5. But his evidence also reveals the limits of that belief.
Notice what he actually built: Windows utilities, a screen recorder, and two Firebase-backed CRUD apps for his wife’s business. These are real applications solving real problems, but they’re also the kinds of projects where you can throw away the code if something goes wrong. When he says “I don’t know how the code works” and “I’m maybe 80% confident these applications are bulletproof,” he’s admitting the core problem with the “AI replaces developers” narrative.
That 80% confidence matters. In your Splink work, you’re the sole frontend developer - you can’t deploy code you’re 80% confident about. You need to understand the implications of your architectural decisions, know where the edge cases are, and maintain the system when requirements change. Burke’s building throwaway prototypes for his wife’s yard sign business. You’re building production software that other people depend on.
His “LLM-first code” philosophy is interesting but backwards. He’s optimizing for AI regeneration rather than human maintenance because he assumes the AI will always be there to fix problems. But AI can’t tell you why a decision was made six months ago when business requirements shift. It can’t explain the constraints that led to a particular architecture. And it definitely can’t navigate political and organizational context when stakeholders disagree about priorities.
The Firebase examples are telling - he keeps emphasizing how well Opus knows the Firebase CLI, as if that proves general capability. But Firebase is extremely well-documented, widely-discussed training data. Try that same experiment with your company’s internal API or a niche library with poor documentation. The model won’t be nearly as capable.
What Burke actually demonstrated is that Opus 4.5 is an excellent pair programmer for prototyping with well-known tools. That’s legitimately valuable. But “pair programmer for prototyping” isn’t the same as “replacing developers.” It’s augmenting someone who already knows how to build software and can evaluate whether the generated code is good.
The most revealing line is at the end: “Just make sure you know where your API keys are.” He’s nervous about security because he doesn’t understand the code. That nervousness is appropriate - it’s the signal that tells you when you’ve crossed from useful tool into dangerous territory.
LLMS like Opus, Gemini 3, and GPT-5.2/5.1-Codex-max, are phenomenal for coding and have only very recently crossed that gap between being "eh" and being quite fantastic to let operate on their own agentically. The major trade-off being a fairly expensive cost. I ran up $200 per provider after running through 'pro' tier limits during a single week of hacking over the holidays.
Unfortunately, it's still surprisingly easy for these models to fall into really stupid maintainability traps.
For instance today, Opus adds a feature to the code that needs access to a db. It fails because the db (sqlite) is not local to the executable at runtime. Its solution is to create this 100 line function to resolve a relative path and deal with errors and variations.
I hit ESC and say "... just accept a flag for --localdb <file>". It responds with "oh, that's a much cleaner implementation. Good idea!". It then implements my approach and deletes all the hacks it had scattered about.
This... is why LLMs are still not Senior engineers. They do plainly stupid things. They're still absurdly powerful and helpful, but if you want maintainable code you really have to pay attention.
Another common failure is when context is polluted.
I asked Opus to implement a feature by looking up the spec. It looked up the wrong spec (a v2 api instead of a v3) -- I had only indicated "latest spec". It then did the classic LLM circular troubleshooting as we went in 4 loops trying to figure out why calculations were failing.
I killed the session, asked a fresh instance to "figure out why the calculation was failing" and it found it straight away. The previous instance would have gone in circles for eternity because its worldview had been polluted by assumptions made -- that could not be shaken.
This is a second way in which LLMs are rigid and robotic in their thinking and approach -- taking the wrong way even when directed not to. Further reading on 'debugging decay': https://arxiv.org/abs/2506.18403
All this said, the number of failure scenarios gets ever smaller. We've gone from "problem and hallucination every other code block" to "problem every 200-1000 code blocks".
They're now in the sweet spot of acting as a massive accelerator. If you're not using them, you'll simply deliver slower.
It’s incredibly tiring to see this narrative peddled every damn day. I use opus 4.5 every day. It’s not much different than any previous models, still does dumb things all the time.
Same experience - I've had it fail at the same reasonably simple tasks I had opus 4 and sonnet 4.5 and sonnet 4 fail at when they aren't carefully guided and their work check and fixed...
I pivoted into integrations in 2022. My day-to-day now is mostly in learning the undocumented quirks of other systems. I turn those into requirements, which I feed to the model du jour via GitHub Copilot Agents. Copilot creates PRs for me to review. I'd say it gets them right the vast majority of the time now.
Example: One of my customers (which I got by Reddit posts, cold calls, having a website, and eventually word of mouth) wanted to do something novel with a vendor in my niche. AI doesn't know how to build it because there's no documentation for the interfaces we needed to use.
Are you using Claude Code? Because that might be the secret cause you're missing. With Claude Code I can instruct it to validate things after its done with code, and usually it finds that it goofed. I can also tell it to work on like five different things, and go "hey spin up some agents to work on this" and it will spawn 5 agents in parallel to work on said things.
I've basically ditched Groke et al and I refuse to give Sam Altman a penny.
For schema design phase I used web UI for all three.
Logical bug of using BIGSERIAL for tracking updates (generated at insert time, not commit time, so can be out of order) wouldn’t be caught by any number of iterations of Claude Code and would be found in production after weeks of debugging.
The main issue in this discussion is the word "replace" . People will come up with a bunch of examples where humans are still needed in SWE and can't be fully replaced, that is true. I think claiming that 100% of engineers would be replaced in 2026 is ridiculous.
But how about downsizing? Yeah that's quite probable.
These are very simple utilities. I expect AI to be able to build them easily. Maybe in a few years it will be able to write a complete photo editor or CAD application from first principles.
The question I keep asking myself is "how feasible will any of this be when the VC money runs out?" Right now tokens are crazy cheap. Will the continue to be?
IMO codex produces working code slowly, while Opus produces superficially working code quickly. I like using Opus to drive codex sessions and checking its output. Clawdbot is really good at that but a long running Claude Code session with codex as sub agents should work well also.
The above is for vibe coding; for taking the wheel, I can only use Opus because I suck at prompting codex (it needs very specific instructions), and codex is also way too slow for pair programming.
> I like using Opus to drive codex sessions and checking its output.
Why not the other way around? Have the quick brown fox churn out code, and have codex review it, guide changes, and loop?
I've actually gone one step further down the delegation. I use opus/gemini3 for plan, review, edit plan for a few steps. Then write it out to .md files. Then have GLM implement it (I got a cheap plan for like 28$ for a year on Christmas). Then have the code this produced reviewed and fixed if needed by opus. Final review by codex (for some reason it's very good at review, esp if you have solid checkboxes for it to check during review). Seems to work so far.
I agree, codex is great at reviewing as well. I think that’s because code is the ideal description of what we want to achieve, and codex is good (only) when it knows what must be achieved, as verbosely as possible.
Currently I don’t let GLM or Opus near my codebases unsupervised because I’m convinced that the better the foundation, the better the end result will be. Is the first draft not pretty crappy with GLM?
See also: a post from a couple days ago which came to the same conclusion that Opus 4.5 is an inflection point above Sonnet 4.5 despite that conclusion being counterintuitive: https://news.ycombinator.com/item?id=46495539
It's hard to say if Opus 4.5 itself will change everything given the cost/latency issues, but now that all the labs will have very good synthetic agentic data thanks to Opus 4.5, I will be very interested to see what the LLMs release this year will be able to do. A Sonnet 4.7 that can do agentic coding as well as Opus 4.5 but at Sonnet's speed/price would be the real gamechanger: with Claude Code on the $20/mo plan, you can barely do more than one or two prompts with Opus 4.5 per session.
A lot of the complaints about these tools seems to revolve around their current lack of ability to innovate for greenfield or overly complex tasks. I would agree with this assessment in their current state, but this sentiment of "I will only use AI coding tools when they can do 100% of my job" seems short-sighted.
The fact of the matter, in my experience, is that most of the day to day software tasks done by an individual developer are not greenfield, complex tasks. They're boring data-slinging or protocol wrangling. This sort of thing has been done a thousand times by developers everywhere, and frankly there's really no need to do the vast majority of this work again when the AIs have all been trained on this very data.
I have had great success using AIs as vast collections of lego blocks. I don't "vibe code", I "lego code", telling the AI the general shape and letting it assemble the pieces. Does it build garbage sometimes? Sure, but who doesn't from time to time? I'm experienced enough notice the garbage smell and take corrective action or toss it and try again. Could there be strange crevices in a lego-coded application that the AI doesn't quite have a piece for? Absolutely! Write that bit yourself and then get on with your day.
If the only thing you use these tools for is doing simple grunt-work tasks, they're still useful, and dismissing them is, in my opinion, a mistake.
The vast majority of engineers aren't refusing to use AI until it can do 100% of their job. They are just sick of being told it already can, when their direct experience contradicts that claim.
As impressive as Opus 4.5 is, it still fails in one situation that it assumes 0-index while the component it supposes to work with assume 1-index. It has access to the said information on disk, but just forgets to look into.
Opus 4.5 is incredible, it is the GPT-4 moment for coding because how honest and noticeable the capacity increase is. But still, it has blind spots just like human.
most of software engineering was rational, now it is becoming empirical
it is quite strange, you have to make it write the code in a way it can reason about it without it reading it, you also have to feel the code without reading all of it. like a blind man feeling the shape of an object; Shape from Darkness
you can ask opus to make a car, it will give you a car, then you ask it for navigation; no problem, it uses google maps works perfect
then you ask it to improve the breaks, and it will give internet to the tires and the break pedal, and the pedal will send a signal via ipv6 to the tires which will enable a very well designed local breaking system, why not, we already have internet for google maps.
i think the new software engineering is 10 times harder than the old one :)
SWE jobs are in fact, not safe, if vaguely defined specifications can be translated into functioning applications. I don't think agents are good enough to do that in larger applications yet, but it is something to consider.
Depends on the software. IMO, development speed will increase, but humans will continue to be the limiting factor, so we are safe. Our jobs, however, are changing and will continue to.
Title: Ask HN: How do you evaluate claims of “this model changes everything” in practice?
The release of every big model seems to carry the identical vibe: finally, this one crossed the line. The greatest programmer. The end of workflows and their meaning.
I’ve learned to slow myself down and ask a different question. What has changed in my day-to-day work after two weeks?
I currently make use of a filter with roughness.
Did it really solve a problem, or did it just make easy parts easier?
Has it lessened the number of choices or has it created new ones?
Have my review responsibilities decreased or increased?
Some things feel revolutionary on day one and then quietly fade into something that’s nice to have. Others barely wow, but stay around. ~
For those who've experienced a couple of cycles.
What indicators suggest that an upcoming release will be significant?
Ok, if its almighty, then why is not the benchmarks at 100%? If you look at the individual issues, those are somewhat small and trivial changes in existing codebases.
I started on the cheapest £15/mo "Pro" plan and it was great for home use when I'd do a bit of coding in the evenings only, but it wasn't really that usable with Opus--you can burn through your session allowance in a few minutes, but was fine with Sonnet. I used the PAYG option to add more, but cost me £200 in December, so I opted for the £90/mo "Max" plan which is great. I've used Opus 4.5 continuously and it's done great work.
I think when you look at it from the perspective of how much you get out of it compared with paying a human to do the same (including yourself), it is still very good value for money whether you use it for work or for your own projects. I do both. But when I look what I can now do for my own projects including open-source stuff, I'm very time-limited, and some of the things I want to do would take multiple years. Some of these tools can take that down to weeks, do I can do more with less, and from that perspective the cost is worth it.
Once you get your setup bulletproof such that you can have multiple agents running at the same time that can run unit tests and close their own loops things get even faster. However you accomplish that. Not as easy as it sounds mostly (and absurdly) due to port collision. E2E testing with playwright is another leap.
Read this article and ultrathink critically about it. Provide your perspective.
The article makes a strong experiential case for improved velocity in AI-assisted development but contains several argumentation weaknesses and conflations worth examining.
The projects described are legitimately non-trivial: Firebase backend integration, Facebook OAuth, iOS apps in Swift (a language the author doesn't know), GitHub Actions pipelines, scheduled cloud functions. Getting these working in hours rather than weeks represents a real capability shift. The author is honest about his limitations and uncertainties, particularly the security concerns.
Where the argument breaks down:
1. "Replace developers" vs "dramatically augment developers"
The author's own workflow contradicts the headline claim. He's still:
Making architectural decisions (choosing Firebase)
Handling errors Opus couldn't see (XAML via Visual Studio)
Writing custom prompts to shape output quality
Manually auditing security
Making product and UX decisions
This is developer work. The tool changed; the role didn't disappear.
2. The 80% security confidence undermines his thesis
He admits he's shipping apps with "80% confidence" in security and calls it "too damn low." This is the crux: the AI accelerated production but didn't replace the judgment required to responsibly ship production software. The velocity gain exposed a competence gap rather than closing it.
3. Sample bias in project selection
All examples are:
Greenfield (no existing codebase)
Single developer
Personal/family use
Standard patterns with excellent documentation (Firebase, SwiftUI, React Native)
No regulatory, compliance, or scale requirements
No team collaboration or handoff considerations
These constraints define a specific problem space where AI excels. Extrapolating to "developers are replaceable" ignores the majority of professional software work.
4. "Code doesn't need human readability" is underbaked
His argument is circular: "Why optimize for human readability when the AI is doing all the work?" But:
His 80% security confidence exists because he can't read the code
He had to use external tools (VS) when Opus couldn't diagnose errors
What happens when context windows are exceeded and the LLM loses track?
Model behavior changes between versions; human-readable code is version-agnostic
The custom prompt he shares actually encodes many good engineering practices (minimal coupling, explicit state, linear control flow) that benefit LLMs and humans. The "no comments needed" claim conflates what's optimal for LLM regeneration with what's optimal for debugging production issues at 3am.
What's actually being demonstrated
The honest version of this article would be: Opus 4.5 dramatically compresses the gap between "can write code" and "can ship a personal app" for a specific class of greenfield projects. That's genuinely transformative for hobbyists, indie developers, and people solving their own problems.
But that's different from "replacing developers." The article demonstrates a power tool; power tools don't eliminate tradespeople.
There's something eerily recursive about Opus 4.5’s sensible take calming the anxiety about Opus 4.5’s capabilities and impact. It's probably the right take, but I feel weird the most pragmatic response to this article is from said model.
The best is probably something like GLM 4.7/Minimax M2.1, and those are probably at most Sonnet 4 level, which is behind Opus 4.1, which is behind Sonnet 4.5, which is behind Opus 4.5 ;)
And honestly Opus 4.5 is a visible step change above previous Anthropic models.
I agree. Claude Code went from being slower than doing it myself to being on average faster, but also far less exhausting so I can do more things in general while it works.
Things are changing. Now everyone can build bespoke apps. Are these apps pushing the limits of technology? No! But they work for the very narrow and specific domain they where designed. And yes they do not scale and have as much bugs as your personal shell scripts. But they work.
But let's not compare these with something more advance - at least not yet. Maybe by end of this year?
We switched from Sonnet 4.5 to Opus 4.5 as our default coding agent recently and we pay the price for the switch (3x the cost) but as the OP said, it is quite frankly amazing. It does a pretty good job, especially, especially when your code and project is structured in a such a way that it helps the agent perform well. Anthropic released an entire video on the subject recently which aligns with my own observations as well.
Where it fails hard is in the more subtle areas of the code, like good design, best practices, good taste, dry, etc. We often need to prompt it to refactor things as the quick solution it decided to do is not in our best interest for the long run. It often ends in deep investigations about things which are trivially obvious. It is overfitted to use unix tools in their pure form as it fail to remember (even with prompting) that it should run `pnpm test:unit` instead `npx jest` - it gets it wrong every time.
But when it works - it is wonderful.
I think we are at the point where we are close to self-improving software and I don't mean this lightly.
It turns out the unix philosophy runs deep. We are right now working on ways to give our agents more shells and we are frankly a few iterations there. I am not sure what to expect after this but I think whatever it is, it will be interesting to see.
Oh another run of new small apps. Why not unleash this oh so powerful tools not on a jira ticket written two years ago, targeting 3 different repos in an old legacy moloch, like actual work?
Did some of that today. Extracting logic from Helm templates that read like 2000s PHP and moving it to a nushell script rendering values. Took a lot of guidance both in terms of making it test its own code and architectural/style decisions and I also use Sonnet, but it got there.
Yea, my issue with Opus 4.5 is it's the first model that's good enough that I'm starting to feel myself slip into laziness. I catch myself reviewing its output less rigorously than I had with previous AI coding assistants.
As a side project / experiment, I designed a language spec and am using (mostly) Opus 4.5 to write a transpiler (language transpiles to C) for it. Parser was no problem (I used s-expressions for a reason). The type checker and transpiler itself have been a slog - I think I'm finding the limits of Opus :D. It particularly struggles with multi-module support. Though, some of this is probably mistakes made by me while playing architect and iterating with Claude - I haven't written a compiler since my senior year compiler design course 20+ years ago. Someone who does this for a living would probably have an easier time of it.
But for the CRUD stuff my day job has me doing? Pffttt... it's great.
People should finally understand that LLMs are a lossy database of PAST knowledge. Yes, if you throw a task at it that has been done tons of times before, it works. Which is not a surprise, because it takes minutes to Google and index multiple full implementations of "Tool that allows you to right-click on an image to convert it". Without LLM you could do the same: Just copy&paste the implementation of that from Microsoft Powertoys, for example.
What LLMs will NOT do however, is write or invent SOMETHING KNEW.
And parts of our industry still are about that: Writing Software that has NOT been written before.
If you hire junior developers to re-invent the wheels: Sure, you do not need them anymore.
But sooner or later you will run out of people who know how to invent NEW things.
So: This is one more of those posts that completely miss the point. "Oh wow, if I look up on Wikipedia how to make pancakes I suddenly can make and have pancakes!!!1". That always was possible. Yes, you now can even get an LLM to create you a pancake-machine. Great.
Most of the artists and designers I am friends with have lost their jobs by now. In a couple of years you will notice the LLMs no longer have new styles to copy from.
I am all for the "remix culture". But don't claim to be an original artist, if you are just doing a remix. And LLM source code output are remixes, not original art.
If you can figure out how to create benchmarks that make sense, are reliable, correlate strongly to business goals, and don't get immediately saturated or contorted once known, you are well on your way to becoming a billionaire.
I'm always surprised to never see any comments in those discussions from people who just like coding, learning, solving problems… I mean, it's amazing that LLMs can build an image converter or whatever you dream of, in a language you don't know, in a field you are not familiar with, in 1 hour, for 30 cents… I'm sure your boss and shareholders love it. But where is the fun in that? For me it kills any interest in doing what I'm doing. I'm lucky enough to work in a place where using LLMs is not mandatory (yet), I don't know how people can make it through the day just writing prompts and reviewing AI slop.
It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
I’ve always liked the quote that sufficiently advanced tech looks like magic, but its mistake to assume that things that look like magic also share other properties of magic. They don’t.
Software engineering spans over several distinct skills: forming logical plans, encoding them in machine executable form(coding), making them readable and expandable by other humans(to scale engineering), and constantly navigating tradeoffs like performance, maintainability and org constraints as requirements evolve.
LLMs are very good at some of these, especially instruction following within well known methodologies. That’s real progress, and it will be productized sooner than later, having concrete usecases, ROI and clearly defined end user.
Yet, I’d love to see less discussion driven by anecdotes and more discussion about productizing these tools, where they work, usage methodologies, missing tooling, KPIs for specific usecases. And don’t get me started on current evaluation frameworks, they become increasingly irrelevant once models are good enough at instruction following.
> It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
Progress is so fast right now anecdotes are sometimes more interesting than proper benchmarks. "Wow it can do impressive thing X" is more interesting to me than a 4% gain on SWE Verified Bench.
In early days of a startup "this one user is spending 50 hours/week in our tool" is sometimes more interesting than global metrics like average time in app. In the early/fast days, the potential is more interesting than the current state. There's work to be done to make that one user's experience apply to everyone, but knowing that it can work is still a huge milestone.
At this point I believe the anecdotes more than benchmarks, cause I know the LLM devs train the damn things on the benchmarks.
A benchmark? probably was gamed. A guy made an app to right click and convert an image? prolly true, have to assume it may have a lot of issues but prima facie I just make a mental note that this is possible now.
> It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
It's a very subjective topic. Some people claim it increases their productivity 100x. Some think it is not fit for purpose. Some think it is dangerous. Some think it's unethical.
Weirdly those could all be true at the same time, and where you land on this is purely a matter of importance to the user.
> Yet, I’d love to see less discussion driven by anecdotes and more discussion about productizing these tools, where they work, usage methodologies, missing tooling, KPIs for specific usecases. And don’t get me started on current evaluation frameworks, they become increasingly irrelevant once models are good enough at instruction following.
I agree. I've said earlier that I just want these AI companies to release an 8-hour video of one person using these tools to build something extremely challenging. Start to finish. How do they use it, how does the tool really work. What's the best approaches. I am not interested in 5-minute demo videos producing react fluff or any other boiler plate machine.
I think the open secret is that these 'models' are not much faster than a truly competent engineer. And what's dangerous is that it is empowering people to 'write' software they don't understand. We're starting to see the AI companies reflect this in their marketing, saying tech debt is a good thing if you move fast enough....
This must be why my 8-core corporate PC can barely run teams and a web browser in 2026.
How many 1+ hour videos of someone building with AI tools have you sought out and watched? Those definitely exist, it sounds like you didn't go seeking them out or watch them because even with 7 less hours you'd better understand where they add value enough to believe they can help with challenging projects.
So why should anybody produce an 8 hour video for you when you wouldn't watch it? Let's be real. You would not watch that video.
In my opinion most of the people who refuse to believe AI can help them while work with software are just incurious/archetypical late adopters.
If you've ever interacted with these kinds of users, even though they might ask for specs/more resources/more demos and case studies or maturity or whatever, you know that really they are just change-resistant and will probably continue to be as as long as they can get away with it being framed as skepticism rather than simply being out of touch.
I don't mean that in a moralizing sense btw - I think it is a natural part of aging and gaining experience, shifting priorities, being burned too many times. A lot of business owners 30 years ago probably truly didn't need to "learn that email thing", because learning it would have required more of a time investment than it would yield, due to being later in their career with less time for it to payoff, and having already built skills/habits/processes around physical mail that would become obsolete with virtual mail. But a lot of them did end up learning that email thing 5, 10, whatever years later when the benefits were more obvious and the rest of the world had already reoriented itself around email. Even if they still didn't want to, they'd risk looking like a fossil/"too old" to adapt to changes in the workplace if they didn't just do it.
That's why you're seeing so many directors/middle managers doing all these though leader posts about AI recently. Lots of these guys 1-2 years ago were either saying AI is spicy autocomplete or "our OKR this quarter is to Do AI Things". Now they can't get away with phoning it in anymore and need to prove to their boss that they are capable of understanding and using AI, the same way they had to prove that they understood cloud by writing about kubernetes or microservices or whatever 5-10 years ago.
I can only speak for myself, but it feels like playing with fire to productize this stuff too quick.
Like, I woke up one day and a magical owl told me that I was a wizard. Now I control the elements with a flick of my wrist - which I love. I can weave the ether into databases, apps, scripts, tools, all by chanting a simple magical invocation. I create and destroy with a subtle murmur.
Do I want to share that power? Naturally, it would be lonely to hoard it and despite the troubles at the Unseen University, I think that schools of wizards sharing incantations can be a powerful good. But do I want to share it with everybody? That feels dangerous.
It's like the early internet - having a technical shelf to climb up before you can use the thing creates a kind of natural filter for at least the kinds of people that care enough to think about what they're doing and why. Selecting for curiosity at the very least.
That said, I'm also interested in more data from an engineering perspective. It's not a simple thing and my mind is very much straddling the crevasse here.
LLMs are lossy compression of a corpus with a really good parser as a front end. As human made content dries up (due to LLM use), the AI products will plateau.
I see inference as the much bigger technology although much better RAG loops for local customization could be a very lucrative product for a few years.
That final line: "Disclaimer: This post was written by a human and edited for spelling, grammer by Haiku 4.5"
Yeah, GRAMMAR
For all the wonderment of the article, tripping up on a penultimate word that was supposedly checked by AI suddenly calls into question everything that went before...
this is just optimizing for token windows. flat code = less context. we did the same thing with java when memory was expensive, called it "lightweight frameworks"
Once again. It is not greenfield projects most of us want to use AI coding assistance for. It is for an existing project, with a byzantine mess of a codebase, and even worse messes of infrastructure, business requirements, regulations, processes, and God knows what else. It seems impossible to me that AI would ever be useful in these contexts (which, again, are practically all I ever deal with as a professional in software development).
I've been noticing it's more on par with sonnet these days. I don't know if that means Opus is getting more efficient, sonnet getting less efficient, or perhaps Opus is getting to the answer fast enough to overcome the higher token spend.
For some reason Opus 4.5 is blowing up recently after having been released for weeks. I guess because holidays are over? Active agent users should have discovered this for a while.
Ugh, I'm so sick of these "I can use AI to solve an already solved problem, thus programmers aren't relevant." Note the solved problem part. This isn't convincing except to people that want a (bad) argument to depress wages and lay off workers while making the existing seniors take on more and more work. This is overall bad for the industry.
Every time I see a post like this on HN I try again and every time I come to the same conclusion. I have never see one agent managing to pull something off that I could instantly ship. It still ends up being very junior code.
I just tried again and ask Opus to add custom video controls around ReactPlayer. I started in Plan mode which looked overal good (used our styling libs, existing components, icons and so on).
I let it execute the plan and behold I have controls on the video, so far so good. I then look at the code and I see multiple issues: Over usage of useEffect for trivial things, storing state in useState which should be computed at run time, failing to correctly display the time / duration of the video and so on...
I ask follow up question like: Hide the controls after 2 seconds and it starts introducing more useEffects and states which all are not needed (granted you need one).
Cherry on the cake, I asked to place the slider at the bottom and the other controls above it, it placed the slider on the top...
So I suck at prompting and will start looking for a gardening job I guess...
These posts are never, never made by someone who is responsible for shipping production code in a large, heavily used application. It's always someone at a director+ level who stopped production coding years ago, if they ever did, and is tired of their engineers trying to explain why something will take more than an hour.
It is also often low-proficiency developers with their minds blown over how quickly they can build something using frameworks / languages they never wanted to learn or understand.
Though even that group probably has some overlap with yours.
Back in the day when you found a solution to your problem on Stackoverflow, you typically had to make some minor changes and perhaps engage in some critical thinking to integrate it into your code base. It was still worth looking for those answers, though, because it was much easier to complete the fix starting from something 90% working than 0%.
The first few times in your career you found answers that solved your problem but needed non-trivial changes to apply it to your code, you might remember that it was a real struggle to complete the fix even starting from 90%. Maybe you thought that ultimately, that stackoverflow fix really was more trouble than it was worth. And then the next few times you went looking for answers on stackoverflow you were better at determining what answers were relevant to your problem/worth using, and better at going from 90% to 100% by applying their answers.
> it was much easier to complete the fix starting from something 90% working than 0%.
As an expert now though, it is genuinely easier and faster to complete the work starting from 0 than to modify something junky. The realplayer example above I could do much faster, correctly, than I could figure out what the AI code was trying to do with all the effects and refactor it correctly. This is why I don't use AI for programming.
And for the cases where I'm not skilled, I would prefer to just gain skill, even though it takes longer than using the AI.
The difference is you’re generally retooling for your purpose rather than scouring for multiple, easily avoidable screw ups that if overlooked will cause massive headaches later on.
I've spent quite a bit of time with Codex recently and come to the conclusion that you can't simply say "Let's add custom video controls around ReactPlayer." You need to follow up with a set of strict requirements to set expectations, guard rails, and what the final product should do (and not do). Even then it may have a few issues, but continuing to prompt with clearly stated problems that don't meet the requirements (or you forgot to include) usually clears it up.
Code that would have taken me a week to write is done in about 10 minutes. It's likely on average better than what I could personally write as a novice-mid level programmer.
>You need to follow up with a set of strict requirements to set expectations, guard rails, and what the final product should do (and not do).
that usually very hard to do part, and is was possible to spent few days on something like that in real word before LLMs. But with LLMs is worse because is not enough to have those requirements, some of those won't work for random reasons and is no any 'rules' that can grantee results. It always like 'try that' and 'probably this will work'.
Just recently I was struggled with same prompt produced different result between API calls before I realized that just usage of few more '\"' and few spaces in prompt leaded model to completely different route of logic which produced opposite answers.
It sounds like it takes you at least 10 minutes to just write the prompt with all the details you mentioned. Especially if you need to continue and prompt again (and again?).
Have you tried Roo Code in "Orchestrator" mode? I find it generally "chews" the tasks I give it to then spoon feed into sub-tasks in "Code" (or others) mode, leaving less room to stray from very focused "bite-sized" changes.
I do need to steer it sometimes, but since it doesn't change a lot at a time, I can usually guide the agent and stop the disaster before it spreads.
A big caveat is I haven't tried heavy front-end stuff with it, more django stuff, and I'm pretty happy with the output.
I have a vanilla JS project. I find that very small llms are able to work on it with no issue. (Including complete rewrites.) But I asked even large LLMs to port it to React and they all consistently fail. Basic functionality broken, rapid memory leaks.
So I just stuck with vanilla JS.
n = 1 but React might not be a great thing to test this stuff with. For the man and the machine! I tried and failed to learn React properly like 8 times but I've shipped multiple full stack things in like 5 other languages no problem.
usually for me, after a good plan is 90% solid working code. the problem do arise when you ask it to change the colors it choose of light grey text over a white background. this thing still can't see and it's a huge drawback for those who got used to just prompting away their problems
I always assume the person either didn't use coding agents in a while or its their first time. don't get me wrong, i love claude code, but my students are still better at getting stuff done that i can just approve and not micromanage. thats what i think everyone is missing from their commentary. you have to micromanage a coding agent. you don't have to micromanage a good student. when you dont need to micromanage anymore at all, that's when the floor falls out and everyone has a team of agents doing whatever they want to make them all billionaires or whatever it is AI is promising to do those days.
Around a Uni I think a lot about what students are good at and what they aren't good at.
I wouldn't even think about hiring a student to do marketing work. They just don't understand how hard it is to break through people's indifference and lack the hustle. I want 10-100x more than I get out of them.
Photos in The Cornell Daily Sun make me depressed. Students take a step out the door, take a snap, then upload it. I think the campus is breathtakingly beautiful and students just don't do the work to take good photos that show it.
In coding it is across the map. Even when I am happy with the results they still do the first 80% that takes another 80% to put in front of customers. I can be really proud of how it turned out in the end despite them missing the point of the design document they were handed.
I was in a game design hackathon where most of the winners were adults or teams with an adult on them. My team won player's choice. I'll take credit for my startup veteran talent of fearlessly demonstrating broken software on stage and making it look great and doing project management with that in mind. One student was solid on C# and making platformers in Unity. I was the backup programmer who worked like a junior other than driving them crazy slowing them down with relentlessly practical project management. The other student made art that fit our game.
We were at each other's throats at the end and shocked that we won. I think I understood the value everybody brought but I'm not sure my teammates did.
I find anecdotes like yours bewildering, because I've been using Opus with Vue.js and it crushes everything I throw at it. The amount of corrections I need to make tend to be minimal, and mostly cosmetic.
The tasks I give it are not trivial either. Just yesterday I had it create a full-blown WYSIWYG editor for authoring the content we serve through our app. This is something that would have taken me two weeks, give or take. Opus looked at the content definitions on the server, queried the database for examples, then started writing code and finished it in ~15 minutes, and after another 15-20 minutes of further prompting for refinement, it was ready to ship.
Created a WYSIWYG editor or copied it off the internet like your average junior would, bugs included?
If that editor is very complicated (as they usually are) it makes sense to just opt for a library. If it's simple then AI is not required and would only reduce familiarity with how it works. The third option is what you did and I feel like it's the option with the lowest probability of ending up with a quality solution.
Yep. It sucks. People are delusional. Let's ignore LLMs and carry on...
On a more serious note:
1) Split tasks into smaller tasks just like a human would do
Would you bash your keyboard for an hour, adding all video controls at once before even testing if anything works at all? Ofc not. You would start by adding a slider and test it until you are satisfied. Then move to next video control. An so on. LLMs are the same. Sometimes they can one-shot many related changes in a single prompt but the common reality is what you experienced: it works sometimes but the code is suboptimal.
2) Document desireable and undesireable coding patterns in AGENTS.md (or CLAUDE.md)
If you found over usage of useEffect, document it on AGENTS.md so next time the LLM knows your preference.
I have been using LLMs since Sonet 3.5 for large enterprise projects (1kk+ lines of code, 1k+ database tables). I just don't ask it to "draw the rest of owl" as the saying goes.
Ah, another thread filled with people sharing anecdotes about how they asked Claude to one-shot an entire project that would take people weeks if not months.
Are the LLMs in any way trained semantically or by hooks that you can plug in, say, Python docs? And if a new version of Python then gets released then the training data changes, etc
This article is much better than hundred of similar articles "AI will change software engineering" because it have links to actual products created with said "AI". I can't say they are impressive, but definitely so for laypeople.
I'm tired of constantly debating the same thing again and again. Where are the products? Where is some great performing software all LLM/agent crafted? All I see is software bloatness and decline. Where is Discord that uses just a bunch of hundreds megs of ram? Where is unbloated faster Slack? Where is the Excel killer? Fast mobile apps? Browsers and the web platform improved? Why Cursor team don't use Cursor to get rid of vscode base and code its super duper code editor? I see tons of talking and almost zero products.
Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same. Unless you have a secret LLM or some magical prompts that make the code better/more efficient than your competitions, your vibe coded product has no advantage over competition and no moat. What actually matters is everything else -- user experience (which requires hours of meetings and usability studies), integration with own/other people's products, business, marketing, sales etc, much of which you can't vibe code your way to success.
I'm not sure what point you're making here. Tech is rarely the moat, you even get to that point at the end of your post. The "vibe coding" advantage is faster time to market, faster iterations, etc. These things will help you get that user experience, integrations, etc.
> Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same.
I think you are strawmanning what "vibe coders" do when they build stuff. It's not simple one-shot generation of eg twitter clones, it's really just iterative product development through an inconsistently capable/spotty LLM developer. It's not really that different from a product manager hiring some cheap developer and feeding them tasks/feature requests. By the way, competitors can hire those and chip away at your moat too!
> Unless you have a secret LLM or some magical prompts that make the code better/more efficient than your competitions, your vibe coded product has no advantage over competition and no moat
This is just not true, and you kind of make my point in the next sentence: many companies competitive advantages come from distribution, trust, integration, regulatory, marketing/sales, network effects. But also, vibe coding is not really about prompts so much as it is product iteration. Anybody product can be copied already, yet people still make way more new products than direct product clones anyway, because it's usually more valuable to go to market with stronger, more focused, or more specialized/differentiated software than a copy.
>> Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same.
But that's precisely why you don't hear about these products: the creators don't disclose that they were vibe-coded, because if they do, that invites competition.
I personally know of four vibe-coded products that generate over $10k/mo. Two of them were made by one friend, one was made by another, and the last one by my cousin. None of these people are developers. But they are making real money.
you can build it and simply use it in your own office? There is no need to shout about it if the cost of writing software goes to zero (but the value remains non-zero!).
Get the feeling with the pending IPO, there might be some challengers to discord that get more traction due to the protracted enshittification of the platform (cf. bluesky)
Totally disagree. One example is Zed which is very well known and it's faster than any other editor, wasn't built with AI though.
> People on larger companies are not at the edge of AI coding
False Microsoft is all in with Copilot, and I can't believe the company that created Copilot doesn't use it internally, I'd rather say they should be the ones that would know how to master it! Yet no better vscode, still bloated teams etc etc
This argument falls a little flat when you consider how much software may or may not be written inside one's own personal work flow, or to scale that up, inside a small business. The idea that a small business doing >1mil revenue can now hire a dev or two, and build out a fairly functional domain-driven system should not be understated. The democratization of software, and the lowering of the barriers to entry to basic CRUD apps, may not necessarily show up in a TAM report...
Do you need a killer app that treads into unicorn territory to prove it's impact? What about a million apps that displace said unicorn potentials by removing the need for a COTS?
Oh, and remember, the iPhone was revolutionary but it was diffused so slowly into the greater economy, the impact on global GDP was basically negligent. Actually, almost all the perceived grandiose tech jumps did not magically produce huge GDP gains overnight.
Your argument falls a little flat considering that you mention "hire a dev or two" while the whole narrative is "we don't need software engineers anymore" and Anthropic alone declares that "Although engineers use Claude frequently, more than half said they can “fully delegate” only between 0-20% of their work to Claude" https://www.anthropic.com/research/how-ai-is-transforming-wo...
However, I think the biggest thing is the replacement of products. We are in a place where he talked about replacing two products his wife was using with custom software. I personally have used LLMs to build things that are valuable for me that I just don't have time for otherwise.
This is true. I think most people are mostly using AI at work to fix bugs in existing codebases. A smaller group of people are benchmarking AI by giving it ideas for apps that no one needs and seeing if it can get close. The smallest group of people is actually designing new software and asking the AI to iterate on it.
Except for maybe an "Excel killer", all those things you listed are not things people are willing to pay for. Also agents are bad at that kind of work (most devs are bad at that stuff, it's why it was something people whined about even before agents).
And funnily enough there are products and tools that are essentially less bloated slack/discord. Have you heard of https://stoat.chat/ (aka revolt) or https://pumble.com/ or https://meet.jit.si/? If not I would guess it's for one of two reasons: not caring enough about these problems to even go looking for them yourself, or their lack of "bloatedness" resulting in them not being a mature/fully featured enough product to be worth marketing or adopting.
If you'd like to see a product mostly made with agents/for agents you can check out mine at https://statue.dev/ - we're making a static site generator with a templating and component system paired with user-story driven "agentic workflows" (~blueprints/playbooks for common user actions like "I need to add a new page and list it on the navbar" or "create a site from the developer portfolio template personalized for my github").
I would guess most other projects are probably in a similar situation as we are: agentic developer tools have only really been good enough to heavily use/build products around for a few months, so it's a typical few-month-old project. But agents definitely made it easier to build.
Not willing to pay for? How can you be sure? For example explain then why many gamers are ditching Windows for Linux and buying hardware from Valve... There must be a reason. Every person I talked to that uses Excel hate how slow it is, same for teams and many other products. Finally, were the mentioned products built with vibe coding?
I did a lot of analysis and biz dev work on the "Excel killer" and came to the conclusion that it would be hard to get people to pay for.
For one thing most enterprises and many individuals have an Office 365 subscription to access Office programs which are less offensive than Excel so they aren't going to save any money by dropping Excel.
On top of it the "killer" would probably not be one product aimed at one market but maybe a few different things. Some people could use "visual pandas" for instance, something that today would be LLM-infused. Other people could use a no-code builder for calculations. The kind of person who is doing muddled and confused work with Excel wouldn't know which "killer" they needed or understand why decimal math would mean they always cut checks in the right amount.
Wrt statue.dev good luck for sure with the project but I personally don't need yet another static site generator, nextjs like but with unpopular svelte, bloated with tons of node modules creating another black hole impossible to escape from. If agents works this well why would I need to use your library? I just tell an agent to maintain my static site who cares which tech stack
Anecdotally I had Gemini convert a simple react native app to swift in two prompts. If it's that simple then maybe we will see less of the chromium desktop apps
I'd argue the contrary, YOU KNOW you have the option, ease of entering doesn't mean they will know how to choose better, they will just vibe code more electron apps. In fact my prediction is not there will be less Electron apps but more
see "How much work can be fully delegated to Claude?": "Although engineers use Claude frequently, more than half said they can “fully delegate” only between 0-20% of their work to Claude"
There won't be anything like you're asking for, even the vendors themselves (they'll be the most positive and most enthousiastic about using it) can't do this with them.
who told you that mb of ram is a definition of success?
Opus was out only few months, and it will take time to get this new wave to market. i can assure you my team become way more productive because of opus. not a single developer but an etnire team.
It's a definition of what runs and what not on consumer grade computers, Discord has a routine that now checks if memory goes over a certain threshold and eventually restart itselfs, this is a measure of engineering total failure imo
Could someone explain this to me? I have the same question: why Cursor team don't use Cursor to get rid of vscode base and code its super duper code editor?
It's been interesting watching HN shift in my direction on this in recent weeks...
I had been saying since around summer of this year that coding agents were getting extremely good. The base model improvements were ok, but the agentic coding wrappers were basically game changers if you were using them right. Until recently they still felt very context limited, but the context problem increasingly feels like a solved problem.
I had some arguments on here in the summer about how it was stupid to hire junior devs at this point and how in a few years you probably wouldn't need senior devs for 90% of development tasks either. This was an aggressive prediction 6 months ago, but I think it's way too conservative now.
Today we have people at our company who have never written code building and shipping bespoke products. We've also started hiring people who can simply prove they can build products for us using AI in a single day. These are not software engineers because we are paying them wages no SWEs would accept, but it's still a decent wage for a 20 something year old without any real coding skills but who is interested in building stuff.
This is something I wouldn't have never of expected to be possible 6 months ago. In 6 months we've gone from senior developers writing ~50% of their code with AI, to just a handful of senior developers who now write close to 90% of their code with AI while they support a bunch of non-developers pumping out a steady stream of shippable products and features.
Software engineers and traditional software engineer is genuinely running on borrowed time right now. It's not that there will be no jobs for knowledgable software engineers in the coming years, but companies simply won't need many hotshot SWEs anymore. The companies that are hiring significant numbers of software engineers today simply can not have realised how much things have changed over just the last few months. Apart from the top 1-2% of talent I simply see no good reason to hire a SWE for anything anymore. And honestly outside of niche areas, anyone hand-cracking code today is a dinosaur... A good SWE today should see their job as simply reviewing code and prompting.
If you think that the quality of code LLMs produce today isn't up to scratch you've either not used the latest models and tools or you're using them wrong. That's not to say it's the best code – they still have a tendency to overcomplicate things in my opinion – but it's probably better than the average senior software engineer. And that's really all that matters.
I'm writing this because if you're reading this thinking we're basically still in 2024 with slightly better models and tooling you're just wrong and you're probably not prepared for what's coming.
Hi Kypro this is very interesting perspective. Can you reach out to me? I'd like to discuss what you're observing with you a bit in private as it relates heavily to a project I'm currently working on. My contact info is on my profile. Pls shoot me a connection request and just say you're kypro from HN :)
Or is there a good way for me to contact you? Your profile doesn't list anything and your handle doesn't seem to have much of an online footprint.
Lastly, I promise I'm not some weirdo, I'm a realperson™ -- just check my HN comment history. A lot of people in the AI community have met me in person and can confirm (swyx etc).
Software/web meat shops have bean around since the dawn of the time.
I worked at McDonald's in my teens. One of the best managers I ever worked for was the manager at this store at this time(the owner rotated him between stores to help get things on track).
I'll never forget this one thing he said: "They have changed the Filet-O-Fish five times since I've been here, and each time it's become more profitable".
LLM's are good at making stuff from scratch and perfect when you don't have to worry about the codes future. 'Research' can be a great tool. But LLMs are horrible in big codebases and multiple micro services. Also at making decision, never let it make a decision for you. You need to know what's happening and you can't ship straight AI code. It can save time, but it's not a lot and it won't replace anyone.
We have a large monorepo at my company. You're right that for adding entirely new core concepts to an existing codebase we wouldn't give an AI some vague requirements and ask it to build something – but we wouldn't do that for a human engineer either. Typically we would discuss as a team and then once we've agreed on technologies and an approach someone will implement it relying heavily on AI to write the actual code (because it's faster and generally won't add dumb bugs like typos or conditional logic error).
Almost everything else at this point can be done by AI. Some stuff requires a little support from human engineers, but honestly our main bottlenecks at this point is just QA and getting the infra to a place where we can rapidly ship stuff into production.
> You need to know what's happening and you can't ship straight AI code.
I think there is some truth to this. We are struggling to maintain a high-level understanding of the code as a team right now, not because there is no human that understands, but because 5 years ago our team would have probably been 10-20x larger given the amount we're shipping. So when one engineer leaves the company or goes on holiday we find we lose significantly more context of systems than you historically would with larger teams of engineers. Previously you might have had 2-3 engineers who had a deep understanding of a single system. Now we have maybe 1-2 engineers who need to maintain understanding of 5-6 systems.
That said, AI helps a lot with this. Asking AI to explain code and help me learn how it works means I can pick up new systems significantly quicker.
To the sceptics still saying that LLMs still can't solve "slime mold pathing algorithm and creating completely new shoe-lacing patterns" (literally a quote from a different comment here), please consider something we've learnt over and over again in history: good enough and cheap will destroy perfect but expensive.
And then cheap and good enough option will eventually get better because that's the one that is more used.
It's how Japanese manufacturing beat Western manufacturing. And how Chinese manufacturing then beat Japanese again.
It's why it's much more likely you are using the Linux kernel and not GNU hurd.
It's how digital cameras left traditional film based cameras in the dust.
I don't think you've used it.
I used it intensely and mostly autonomously (with clear instructions, including how to measure good output) almost non-stop over the holidays. Its a new abstraction for programming -- it doesn't replace software developers, it gives them a more natural way to describe what they want.
More than half. What has anyone written that was truly new? Regardless, if you have an idea, you will build it out of some combination of conditionals, loops, and expressions… turns out agents are pretty good at those things, even when the idea you’re expressing is novel.
This is a natural response to software enshittification. You can hardly find an iOS app that is not plagued by ads, subscriptions, or hostile data collection. Now you can have your own small utilities that can work for you. This sort of personal software might be very valuable in the world where you are expected to pay 5$ to click any button.
Yeah sure but have you considered that the actual cost of running these models is actually much greater than whatever cost you might be shelling out for the ad-free apps? You're talking to someone who hates the slopification and enshittification of everything, so you don't need to convince me about that. However, everything I've seen described in the replies to my initial comment - while cute, and potentially helpful on a case-by-case basis, does NOT warrant the amount of resources we are pouring into AI right now. Not even fucking close. It'll all come crashing down, taxpayers the world over will be caught with the bag in their hands, and for what? So that we can all have a less robust version of an app that already exists but that has the colours we want and the button where we want it?
If AI cost nothing and wasn't absolutely decimating our economy, I'd find what you've shared cute. However, we are putting literally all of our eggs, and the next generation's eggs, and the one after that, AND the one after that, into this one thing, which, I'm sorry, is so far away from everything that keeps on being promised to us that I can't help but feel extremely depressed.
> none of this is going to improve people's lives.
I have some old borderline senile relatives writting apps (asking LLMs to write for it them) for their own personal use. Stuff they surely haven't done on their own (or had the energy to do). Their extent of programming background - shitty VBScript macros for excel.
It also helps people to pick up programming and helps with the initial push of getting started. Getting over the initial hump, getting something on the screen so to speak.
Most things people want from their computers are simple shit that LLMs usually manage quite well.
Good question whether or not this (outsourcing their thinking) actually just accelerates their senility or not.
As someone who likes to solve hard or interesting technical problems, I've long before LLMs often been disappointed that most of the time what people want from programmers is simple stupid shit (ie. stuff i dont find interesting to work on).
Most software engineers are seriously sleeping on how good LLM agents are right now, especially something like Claude Code.
Once you’ve got Claude Code set up, you can point it at your codebase, have it learn your conventions, pull in best practices, and refine everything until it’s basically operating like a super-powered teammate. The real unlock is building a solid set of reusable “skills” plus a few agents for the stuff you do all the time.
For example, we have a custom UI library, and Claude Code has a skill that explains exactly how to use it. Same for how we write Storybooks, how we structure APIs, and basically how we want everything done in our repo. So when it generates code, it already matches our patterns and standards out of the box.
We also had Claude Code create a bunch of ESLint automation, including custom ESLint rules and lint checks that catch and auto-handle a lot of stuff before it even hits review.
Then we take it further: we have a deep code review agent Claude Code runs after changes are made. And when a PR goes up, we have another Claude Code agent that does a full PR review, following a detailed markdown checklist we’ve written for it.
On top of that, we’ve got like five other Claude Code GitHub workflow agents that run on a schedule. One of them reads all commits from the last month and makes sure docs are still aligned. Another checks for gaps in end-to-end coverage. Stuff like that. A ton of maintenance and quality work is just… automated. It runs ridiculously smoothly.
We even use Claude Code for ticket triage. It reads the ticket, digs into the codebase, and leaves a comment with what it thinks should be done. So when an engineer picks it up, they’re basically starting halfway through already.
There is so much low-hanging fruit here that it honestly blows my mind people aren’t all over it. 2026 is going to be a wake-up call.
(used voice to text then had claude reword, I am lazy and not gonna hand write it all for yall sorry!)
Edit: made an example repo for ya
https://github.com/ChrisWiles/claude-code-showcase
I made a similar comment on a different thread, but I think it also fits here: I think the disconnect between engineers is due to their own context. If you work with frontend applications, specially React/React Native/HTML/Mobile, your experience with LLMs is completely different than the experience of someone working with OpenGL, io_uring, libev and other lower level stuff. Sure, Opus 4.5 can one shot Windows utilities and full stack apps, but can't implement a simple shadowing algorithm from a 2003 paper in C++, GLFW, GLAD: https://www.cse.chalmers.se/~uffe/soft_gfxhw2003.pdf
Codex/Claude Code are terrible with C++. It also can't do Rust really well, once you get to the meat of it. Not sure why that is, but they just spit out nonsense that creates more work than it helps me. It also can't one shot anything complete, even though I might feed him the entire paper that explains what the algorithm is supposed to do.
Try to do some OpenGL or Vulkan with it, without using WebGPU or three.js. Try it with real code, that all of us have to deal with every day. SDL, Vulkan RHI, NVRHI. Very frustrating.
Try it with boost, or cmake, or taskflow. It loses itself constantly, hallucinates which version it is working on and ignores you when you provide actual pointers to documentation on the repo.
I've also recently tried to get Opus 4.5 to move the Job system from Doom 3 BFG to the original codebase. Clean clone of dhewm3, pointed Opus to the BFG Job system codebase, and explained how it works. I have also fed it the Fabien Sanglard code review of the job system: https://fabiensanglard.net/doom3_bfg/threading.php
We are not sleeping on it, we are actually waiting for it to get actually useful. Sure, it can generate a full stack admin control panel in JS for my PostgreSQL tables, but is that really "not normal"? That's basic.
We have an in-house, Rust-based proxy server. Claude is unable to contribute to it meaningfully outside of grunt work like minor refactors across many files. It doesn't seem to understand proxying and how it works on both a protocol level and business logic level.
With some entirely novel work we're doing, it's actually a hindrance as it consistently tells us the approach isn't valid/won't work (it will) and then enters "absolutely right" loops when corrected.
I still believe those who rave about it are not writing anything I would consider "engineering". Or perhaps it's a skill issue and I'm using it wrong, but I haven't yet met someone I respect who tells me it's the future in the way those running AI-based companies tell me.
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I've had Opus 4.5 hand rolling CUDA kernels and writing a custom event loop on io_uring lately and both were done really well. Need to set up the right feedback loops so it can test its work thoroughly but then it flies.
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I'll second this. I'm making a fairly basic iOS/Swift app with an accompanying React-based site. I was able to vibe-code the React site (it isn't pretty, but it works and the code is fairly decent). But I've struggled to get the Swift code to be reliable.
Which makes sense. I'm sure there's lots of training data for React/HTML/CSS/etc. but much less with Swift, especially the newer versions.
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I built an open to "game engine" entirely in Lua a many years ago, but relying on many third party libraries that I would bind to with FFI.
I thought I'd revive it, but this time with Vulkan and no third-party dependencies (except for Vulkan)
4.5 Sonet, Opus and Gemini 3.5 flash has helped me write image decoders for dds, png jpg, exr, a wayland window implementation, macOS window implementation, etc.
I find that Gemini 3.5 flash is really good at understanding 3d in general while sonnet might be lacking a little.
All these sota models seem to understand my bespoke Lua framework and the right level of abstraction. For example at the low level you have the generated Vulkan bindings, then after that you have objects around Vulkan types, then finally a high level pipeline builder and whatnot which does not mention Vulkan anywhere.
However with a larger C# codebase at work, they really struggle. My theory is that there are too many files and abstractions so that they cannot understand where to begin looking.
I'm a quite senior frontend using React and even I see Sonnet 4.5 struggle with basic things. Today it wrote my Zod validation incorrectly, mixing up versions, then just decided it wasn't working and attempted to replace the entire thing with a different library.
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Have you experimented with all of these things on the latest models (e.g. Opus 4.5) since Nov 2025? They are significantly better at coding than earlier models.
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I've found it to be pretty hit-or-miss with C++ in general, but it's really, REALLY bad at 3D graphics code. I've tried to use it to port an OpenGL project to SDL3_GPU, and it really struggled. It would confidently insist that the code it wrote worked, when all you had to do was run it and look at the output to see a blank screen.
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I've had pretty good luck with LLM agents coding C. In this case a C compiler that supports a subset of C and targets a customizable microcoded state machine/processor. Then I had Gemini code up a simulator/debugger for the target machine in C++ and it did it in short order and quite successfully - lets you single step through the microcode and examine inputs (and set inputs), outputs & current state - did that in an afternoon and the resulting C++ code looks pretty decent.
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I have not tried C++, but Codex did a good job with low-level C code, shaders as well as porting 32 bit to 64 bit assembly drawing routines. I have also tried it with retro-computing programming with relative success.
> Mobile
From what I've seen, CC has troubles with the latest Swift too, partially because of it being latest and partially because it's so convoluted nowadays.
But it's übercharged™ for C#
> It also can't do Rust really well, once you get to the meat of it. Not sure why that is
Because types are proofs and require global correctness, you can't just iterate, fix things locally, and wait until it breaks somewhere else that you also have to fix locally.
I really think a lof of people tried AI coding earlier, got frustrated at the errors and gave up. That's where the rejection of all these doomer predictions comes from.
And I get it. Coding with Claude Code really was prompting something, getting errors, and asking it to fix it. Which was still useful but I could see why a skilled coder adding a feature to a complex codebase would just give up
Opus 4.5 really is at a new tier however. It just...works. The errors are far fewer and often very minor - "careless" errors, not fundamental issues (like forgetting to add "use client" to a nextjs client component.
This was me. I was a huge AI coding detractor on here for a while (you can check my comment history). But, in order to stay informed and not just be that grouchy curmudgeon all the time, I kept up with the models and regularly tried them out. Opus 4.5 is so much better than anything I've tried before, I'm ready to change my mind about AI assistance.
I even gave -True Vibe Coding- a whirl. Yesterday, from a blank directory and text file list of requirements, I had Opus 4.5 build an Android TV video player that could read a directory over NFS, show a grid view of movie poster thumbnails, and play the selected video file on the TV. The result wasn't exactly full-featured Kodi, but it works in the emulator and actual device, it has no memory leaks, crashes, ANRs, no performance problems, no network latency bugs or anything. It was pretty astounding.
Oh, and I did this all without ever opening a single source file or even looking at the proposed code changes while Opus was doing its thing. I don't even know Kotlin and still don't know it.
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> "asking it to fix it."
This is what people are still doing wrong. Tools in a loop people, tools in a loop.
The agent has to have the tools to detect whatever it just created is producing errors during linting/testing/running. When it can do that, I can loop again, fix the error and again - use the tools to see whether it worked.
I _still_ encounter people who think "AI programming" is pasting stuff into ChatGPT on the browser and they complain it hallucinates functions and produces invalid code.
Well, d'oh.
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I have been out of the loop for a couple of months (vacation). I tried Claude Opus 4.5 at the end of November 2025 with the corporate Github Copilot subscription in Agent mode and it was awful: basically ignoring code and hallucinating.
My team is using it with Claude Code and say it works brilliantly, so I'll be giving it another go.
How much of the value comes from Opus 4.5, how much comes from Claude Code, and how much comes from the combination?
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This was me. I have done a full 180 over the last 12 months or so, from "they're an interesting idea, and technically impressive, but not practically useful" to "holy shit I can have entire days/weeks where I don't write a single line of code".
my issue hasn't been for a long time now that the code they write works or doesn't work. My issues all stem from that it works, but does the wrong thing
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> I really think a lof of people tried AI coding earlier, got frustrated at the errors and gave up. That's where the rejection of all these doomer predictions comes from.
It's not just the deficiencies of earlier versions, but the mismatch between the praise from AI enthusiasts and the reality.
I mean maybe it is really different now and I should definitely try uploading all of my employer's IP on Claude's cloud and see how well it works. But so many people were as hyped by GPT-4 as they are now, despite GPT-4 actually being underwhelming.
Too much hype for disappointing results leads to skepticism later on, even when the product has improved.
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> Opus 4.5 really is at a new tier however. It just...works.
Literally tried it yesterday. I didn't see a single difference with whatever model Claude Code was using two months ago. Same crippled context window. Same "I'll read 10 irrelevant lines from a file", same random changes etc.
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Opus 4.5 is fucking up just like Sonnet really. I don't know how your use is that much different than mine.
[flagged]
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I know someone who is using a vibe coded or at least heavily assisted text editor, praising it daily, while also saying llms will never be productive. There is a lot of dissonance right now.
I teach at a university, and spend plenty of time programming for research and for fun. Like many others, I spent some time on the holidays trying to push the current generation of Cursor, Claude Code, and Codex as far as I could. (They're all very good.)
I had an idea for something that I wanted, and in five scattered hours, I got it good enough to use. I'm thinking about it in a few different ways:
1. I estimate I could have done it without AI with 2 weeks full-time effort. (Full-time defined as >> 40 hours / week.)
2. I have too many other things to do that are purportedly more important that programming. I really can't dedicate to two weeks full-time to a "nice to have" project. So, without AI, I wouldn't have done it at all.
3. I could hire someone to do it for me. At the university, those are students. From experience with lots of advising, a top-tier undergraduate student could have achieved the same thing, had they worked full tilt for a semester (before LLMs). This of course assumes that I'm meeting them every week.
How do you compare Claude Code to Cursor? I'm a Cursor user quietly watching the CC parade with curiosity. Personally, I haven't been able to give up the IDE experience.
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This is where the LLM coding shines in my opinion, there's a list of things they are doing very well:
- single scripts. Anything which can be reduced to a single script.
- starting greenfield projects from scratch
- code maintenance (package upgrades, old code...)
- tasks which have a very clear and single definition. This isn't linked to complexity, some tasks can be both very complex but with a single definition.
If your work falls into this list they will do some amazing work (and yours clearly fits that), if it doesn't though, prepare yourself because it will be painful.
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What did you build? I think people talk passed eachother when people don't share what exactly they were trying to do and achieving success/failure.
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The crazy part is, once you have it setup and adapted your workflow, you start to notice all sorts of other "small" things:
claude can call ssh and do system admin tasks. It works amazingly well. I have 3 VM's, which depends on each other (proxmox with openwrt, adguard, unbound), and claude can prove to me that my dns chains works perfectly, my firewalls are perfect etc as claude can ssh into each. Setting up services, diagnosing issues, auditing configs... you name it. Just awesome.
claude can call other sh scripts on the machine, so over time, you can create a bunch of scripts that lets claude one shot certain tasks that would normally eat tokens. It works great. One script per intention - don't have a script do more than one thing.
claude can call the compiler, run the debug executable and read the debug logs.. in real time. So claude can read my android apps debug stream via adb.. or my C# debug console because claude calls the compiler, not me. Just ask it to do it and it will diagnose stuff really quickly.
It can also analyze your db tables (give it readonly sql access), look at the application code and queries, and diagnose performance issues.
The opportunities are endless here. People need to wake up to this.
> claude can call ssh and do system admin tasks
Claude set up a Raspberry Pi with a display and conference audio device for me to use as an Alexa replacement tied to Home Assistant.
I gave it an ssh key and gave it root.
Then I told it what I wanted, and it did. It asked for me to confirm certain things, like what I could see on screen, whether I could hear the TTS etc. (it was a bit of a surprise when it was suddenly talking to me while I was minding my own business).
It configured everything, while keeping a meticulous log that I can point it at if I want to set up another device, and eventually turn into a runbook if I need to.
I have a /fix-ci-build slash command that instructs Claude how to use `gh` to get the latest build from that specific project's Github Actions and get the logs for the build
In addition there are instructions on how and where to push the possible fixes and how to check the results.
I've yet to encounter a build failure it couldn't fix automatically.
Why do all these AI generated readmes have a directory structure sections it's so redundant because you know I could just run tree
It makes me so exhausted trying to read them... my brain can tell immediately when there's so much redundant information that it just starts shutting itself off.
comments? also reading into an agent so the agent doesnt have to tool-call/bash out
I still struggle with these things being _too_ good at generating code. They have a tendency to add abstractions, classes, wrappers, factories, builders to things that didn't really need all that. I find they spit out 6 files worth of code for something that really only needed 2-3 and I'm spending time going back through simplifying.
There are times those extra layers are worth it but it seems LLMs have a bias to add them prematurely and overcomplicate things. You then end up with extra complexity you didn't need.
I think we're entering a world where programmers as such won't really exist (except perhaps in certain niches). Being able to program (and read code, in particular) will probably remain useful, though diminished in value. What will matter more is your ability to actually create things, using whatever tools are necessary and available, and have them actually be useful. Which, in a way, is the same as it ever was. There's just less indirection involved now.
We've been living in that world since the invention of the compiler ("automatic programming"). Few people write machine code any more. If you think of LLMs as a new variety of compiler, a lot of their shortcomings are easier to describe.
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Isn't there more indirection as long as LLMs use "human" programming languages?
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You intrigue me.
> have it learn your conventions, pull in best practices
What do you mean by "have it learn your conventions"? Is there a way to somehow automatically extract your conventions and store it within CLAUDE.md?
> For example, we have a custom UI library, and Claude Code has a skill that explains exactly how to use it. Same for how we write Storybooks, how we structure APIs, and basically how we want everything done in our repo. So when it generates code, it already matches our patterns and standards out of the box.
Did you have to develop these skills yourself? How much work was that? Do you have public examples somewhere?
Just ask it to.
/init in Claude Code already automatically extracts a bunch, but for something more comprehensive, just tell it which additional types of things you want it to look for and document.
> Did you have to develop these skills yourself? How much work was that? Do you have public examples somewhere?
I don't know about the person above, but I tell Claude to write all my skills and agents for me. With some caveats, you can do this iteratively in a single session ("update the X agent, then re-run it. Repeat until it reliably does Y")
> What do you mean by "have it learn your conventions"?
I'll give you an example: I use ruff to format my python code, which has an opinionated way of formatting certain things. After an initial formatting, Opus 4.5, without prompting, will write code in this same style so that the ruff formatter almost never has anything to do on new commits. Sonnet 4.5 is actually pretty good at this too.
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Starting to use Opus 4.5 I'm reduces instrutions in claude.md and just ask claude to look in the codebase to understand the patterns already in use. Going from prompts/docs to instead having code being the "truth". Show don't tell. I've found this patterns has made a huge leap with Opus 4.5.
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When I ask Claude to do something, it independently, without me even asking or instructing it to, searches the codebase to understand what the convention is.
I’ve even found it searching node_modules to find the API of non-public libraries.
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"Claude, clone this repo https://github.com/repo, review the coding conventions, check out any markdown or readme files. This is an example of coding conventions we want to use on this project"
> Once you’ve got Claude Code set up, you can point it at your codebase, have it learn your conventions, pull in best practices, and refine everything until it’s basically operating like a super-powered teammate. The real unlock is building a solid set of reusable “skills” plus a few agents for the stuff you do all the time.
I agree with this, but I haven't needed to use any advanced features to get good results. I think the simple approach gets you most of the benefits. Broadly, I just have markdown files in the repo written for a human dev audience that the agent can also use.
Basically:
- README.md with a quick start section for devs, descriptions of all build targets and tests, etc. Normal stuff.
- AGENTS.md (only file that's not written for people specifically) that just describes the overall directory structure and has a short step of instructions for the agent: (1) Always read the readme before you start. (2) Always read the relevant design docs before you start. (3) Always run the linter, a build, and tests whenever you make code changes.
- docs/*.md that contain design docs, architecture docs, and user stories, just text. It's important to have these resources anyway, agent or no.
As with human devs, the better the docs/requirements the better the results.
I'd really encourage you to try using agents for tasks that are repeatable and/or wordy but where most of the words are not relevant for ongoing understanding.
It's a tiny step further, and sub-agents provide a massive benefit the moment you're ready to trust the model even a little bit (relax permissions to not have it prompt you for every little thing; review before committing rather than on every file edit) because they limit what goes into the top level context, and can let the model work unassisted for far longer. I now regularly have it run for hours at a time without stopping.
Running and acting on output from the linter is absolutely an example of that which matters even for much shorter runs.
There's no reason to have all the lint output "polluting" the top level context, nor to have the steps the agent needs to take to fix linter issues that can't be auto-fixed by the linter itself. The top level agent should only need to care about whether the linter run passed or failed (and should know it needs to re-run and possibly investigate if it fails).
Just type /agents, select "Create new agent" and describe a task you often do, and then forget about it (or ask Claude to make changes to it for you)
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Oh! An ad!
The most effective kind of marketing is viral word of mouth from users who love your product. And Claude Code is benefiting from that dynamic.
lol does sound like and ad, but is true. Also forgot about hooks use hooks too! I just use voice to text then had claude reword it. Still my real world ideas
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All of these things work very well IMO in a professional context.
Especially if you're in a place where a lot of time was spent previously revising PRs for best practices, etc, even for human-submitted code, then having the LLM do that for you that saves a bunch of time. Most humans are bad at following those super-well.
There's a lot of stuff where I'm pretty sure I'm up to at least 2x speed now. And for things like making CLI tools or bash scripts, 10x-20x. But in terms of "the overall output of my day job in total", probably more like 1.5x.
But I think we will need a couple major leaps in tooling - probably deterministic tooling, not LLM tooling - before anyone could responsibly ship code nobody has ever read in situations with millions of dollars on the line (which is different from vibe-coding something that ends up making millions - that's a low-risk-high-reward situation, where big bets on doing things fast make sense. if you're already making millions, dramatic changes like that can become high-risk-low-reward very quickly. In those companies, "I know that only touching these files is 99.99% likely to be completely safe for security-critical functionality" and similar "obvious" intuition makes up for the lack of ability to exhaustively test software in a practical way (even with fuzzers and things), and "i didn't even look at the code" is conceding responsibility to a dangerous degree there.)
I'm curious: With that much Claude Code usage, does that put your monthly Anthropic bill above $1000/mo?
Mind sharing the bill for all that?
My company pays for the team Claude code plan which is like $200 a month for each dev. The workflows cost like 10 - 50 cents a PR
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Cheaper than hiring another developer, probably. My experience: for a few dollars I was able to extensively refactor a Python codebase in half a day. This otherwise would have taken multiple days of very tedious work.
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i've never hit a limit with my $200 a month plan
> Most software engineers are seriously sleeping on how good LLM agents are right now, especially something like Claude Code.
Nobody is sleeping. I'm using LLMs daily to help me in simple coding tasks.
But really where is the hurry? At this point not a few weeks go by without the next best thing since sliced bread to come out. Why would I bother "learning" (and there's really nothing to learn here) some tool/workflow that is already outdated by the time it comes out?
> 2026 is going to be a wake-up call
Do you honestly think a developer not using AI won't be able to adapt to a LLM workflow in, say, 2028 or 2029? It has to be 2026 or... What exactly?
There is literally no hurry.
You're using the equivalent of the first portable CD-player in the 80s: it was huge, clunky, had hiccups, had a huge battery attached to it. It was shiny though, for those who find new things shiny. Others are waiting for a portable CD player that is slim, that buffers, that works fine. And you're saying that people won't be able to learn how to put a CD in a slim CD player because they didn't use a clunky one first.
> Nobody is sleeping. I'm using LLMs daily to help me in simple coding tasks.
That is sleeping.
> But really where is the hurry? At this point not a few weeks go by without the next best thing since sliced bread to come out. Why would I bother "learning" (and there's really nothing to learn here) some tool/workflow that is already outdated by the time it comes out?
You're jumping to conclusions that haven't been justified by any of the development in this space. The learning compounds.
> Do you honestly think a developer not using AI won't be able to adapt to a LLM workflow in, say, 2028 or 2029? It has to be 2026 or... What exactly?
They will, but they'll be competing against people with 2-3 more years of experience in understanding how to leverage these tools.
I think getting proficient at using coding agents effectively takes a few months of practice.
It's also a skill that compounds over time, so if you have two years of experience with them you'll be able to use them more effectively than someone with two months of experience.
In that respect, they're just normal technology. A Python programmer with two years of Python experience will be more effective than a programmer with two months of Python.
"But really where is the hurry?" It just depends on why you're programming. For many of us not learning and using up to date products leads to a disadvantage relative to our competition. I personally would very much rather go back to a world without AI, but we're forced to adapt. I didn't like when pagers/cell phones came out either, but it became clear very quickly not having one put me at a disadvantage at work.
Use Claude Code... to do what? There are multiple layers of people involved in the decision process and they only come up with a few ideas every now and then. Nothing I can't handle. AI helps but it doesn't have to be an agent.
I'm not saying there aren't use cases for agents, just that it's normal that most software engineers are sleeping on it.
Thanks for the example! There's a lot (of boilerplate?) here that I don't understand. Does anyone have good references for catching up to speed what's the purpose of all of these files in the demo?
Came across official anthropic repo on gh actions very relevant to what you mentioned. Your idea on scheduled doc updation using llm is brilliant, I’m stealing this idea. https://github.com/anthropics/claude-code-action
Agreed and skills are a huge unlock.
codex cli even has a skill to create skills; it's super easy to get up to speed with them
https://github.com/openai/skills/blob/main/skills/.system/sk...
(if you know) how is that compared to coderabbit? i'm seriously looking for something better rn...
Never tried coderabbit, just because this is already good enough with Claude Code. It helped us to catch dozens of important issues we wouldn't have caught. We gave some instructions in the CLAUDE.md doc in the repository - with including a nice personalized roast of the engineer that did the review in the intro and conclusion to make it fun! :) Basically, when you do a "create PR" from your Claude Code, it will help you getting your Linear ticket (or creating one if missing), ask you some important questions (like: what tests have you done?), create the PR on Github, request the reviewers, and post a "Auto Review" message with your credentials. It's not an actual review per se but this is enough for our small team.
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Also new haiku. Not as smart but lighting fast, I've it review code changes impact or if i need a wide but shallow change done I've it scan the files and create a change plan. Saves a lot of time waiting for claude or codex to get their bearing.
If anyone is excited about, and has experience with this kind of stuff, please DM. I have a role open for setting up these kinds of tools and workflows.
Is Claude "Code" anything special,or it's mostly the LLM and other CLIs (e.g. Copilot) also work?
I've tried most of the CLI coding tools with the Claude models and I keep coming back to Claude Code. It hits a sweet spot of simple and capable, and right now I'd say it's the best from an "it just works" perspective.
In my experience the CLI tool is part of the secret sauce. I haven't tried switching models per each CLI tool though. I use claude exclusively at work and for personal projects I use claude, codex, gemini.
It’s mostly the model, Copilot, Claude Code, OpenCode, snake oil like Oh My OpenCode, it’s not huge differences.
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> (used voice to text then had claude reword, I am lazy and not gonna hand write it all for yall sorry!)
Reword? But why not just voice to text alone...
Oh but we all read the partially synthetic ad by this point. Psyche.
They are sleeping on it because there is absolutely no incentive to use it.
When needed it can be picked up in a day. Otherwise they are not paid based in tickets solved etc. If the incentives were properly aligned everyone would already use it
I was expecting a showcase to showcase what you've done with it, not just another person's attempt at instructing an AI to follow instructions.
I'm at the point where I say fuck it, let them sleep.
The tech industry just went through an insane hiring craze and is now thinning out. This will help to separate the chaff from the wheat.
I don't know why any company would want to hire "tech" people who are terrified of tech and completely obstinate when it comes to utilizing it. All the people I see downplaying it take a half-assed approach at using it then disparage it when it's not completely perfect.
I started tinkering with LLMs in 2022. First use case, speak in natural english to the llm, give it a json structure, have it decipher the natural language and fill in that json structure (vacation planning app, so you talk to it about where/how you want to vacation and it creates the structured data in the app). Sometimes I'd use it for minor coding fixes (copy and paste a block into chatgpt, fix errors or maybe just ideation). This was all personal project stuff.
At my job we got LLM access in mid/late 2023. Not crazy useful, but still was helpful. We got claude code in 2024. These days I only have an IDE open so I can make quick changes (like bumping up a config parameter, changing a config bool, etc.). I almost write ZERO code now. I usually have 3+ claude code sessions open.
On my personal projects I'm using Gemini + codex primarily (since I have a google account and chatgpt $20/month account). When I get throttled on those I go to claude and pay per token. I'll often rip through new features, projects, ideas with one agent, then I have another agent come through and clean things up, look for code smells, etc. I don't allow the agents to have full unfettered control, but I'd say 70%+ of the time I just blindly accept their changes. If there are problems I can catch them on the MR/PR.
I agree about the low hanging fruit and I'm constantly shocked at the sheer amount of FUD around LLMs. I want to generalize, like I feel like it's just the mid/jr level devs that speak poorly about it, but there's definitely senior/staff level people I see (rarely, mind you) that also don't like LLMs.
I do feel like the online sentiment is slowly starting to change though. One thing I've noticed a lot of is that when it's an anonymous post it's more likely to downplay LLMs. But if I go on linkedin and look at actual good engineers I see them praising LLMs. Someone speaking about how powerful the LLMs are - working on sophisticated projects at startups or FAANG. Someone with FUD when it comes to LLM - web dev out of Alabama.
I could go on and on but I'm just ranting/venting a little. I guess I can end this by saying that in my professional/personal life 9/10 of the top level best engineers I know are jumping on LLMs any chance they get. Only 1/10 talks about AI slop or bullshit like that.
Not entirely disagreeing with your point but I think they've mostly been forced to pivot recently for their own sakes; they will never say it though. As much as they may seem eager the most public people tend to also be better at outside communication and knowing what they should say in public to enjoy more opportunities, remain employed or for the top engineers to still seem relevant in the face of the communities they are a part of. Its less about money and more about respect there I think.
The "sudden switch" since Opus 4.5 when many were saying just a few months ago "I enjoy actual coding" but now are praising LLM's isn't a one off occurrence. I do think underneath it is somewhat motivated by fear; not for the job however but for relevance. i.e. its in being relevant to discussions, tech talks, new opportunities, etc.
> (used voice to text then had claude reword, I am lazy and not gonna hand write it all for yall sorry!)
take my downvote as hard as you can. this sort of thing is awfully off-putting.
OK, I am gonna be the guy and put my skin in the game here. I kind of get the hype, but the experience with e.g. Claude Code (or Github Copilot previously and others as weel) has so far been pretty unreliable.
I have Django project with 50 kLOC and it is pretty capable of understanding the architecture, style of coding, naming of variables, functions etc. Sometimes it excels on tasks like "replicate this non-trivial functionality for this other model and update the UI appropriately" and leaves me stunned. Sometimes it solves for me tedious and labourous "replace this markdown editor with something modern, allowing fullscreen edits of content" and does annoying mistake that only visual control shows and is not capable to fix it after 5 prompts. I feel as I am becoming tester more than a developer and I do not like the shift. Especially when I do not like to tell someone he did an obvious mistake and should fix it - it seems I do not care if it is human or AI, I just do not like incompetence I guess.
Yesterday I had to add some parameters to very simple Falcon project and found out it has not been updated for several months and won't build due to some pip issues with pymssql. OK, this is really marginal sub-project so I said - let's migrate it to uv and let's not get hands dirty and let the Claude do it. He did splendidly but in the Dockerfile he missed the "COPY server.py /data/" while I asked him to change the path... Build failed, I updated the path myself and moved on.
And then you listen to very smart guys like Karpathy who rave about Tab, Tab, Tab, while not understanding the language or anything about the code they write. Am I getting this wrong?
I am really far far away from letting agents touch my infrastructure via SSH, access managed databases with full access privileges etc. and dread the day one of my silly customers asks me to give their agent permission to managed services. One might say the liability should then be shifted, but at the end of the day, humans will have to deal with the damage done.
My customer who uses all the codebase I am mentioning here asked me, if there is a way to provide "some AI" with item GTINs and let it generate photos, descriptions, etc. including metadata they handcrafted and extracted for years from various sources. While it looks like nice idea and for them the possibility of decreasing the staff count, I caught the feeling they do not care about the data quality anymore or do not understand the problems the are brining upon them due to errors nobody will catch until it is too late.
TL;DR: I am using Opus 4.5, it helps a lot, I have to keep being (very) cautious. Wake up call 2026? Rather like waking up from hallucination.
Why dont I see any streams building apps as quickly as they say? Just HYpe
Didn't feel like reading all this so I shortened it! sorry!
I shortened it for anyone else that might need it
----
Software engineers are sleeping on Claude Code agents. By teaching it your conventions, you can automate your entire workflow:
Custom Skills: Generates code matching your UI library and API patterns.
Quality Ops: Automates ESLint, doc syncing, and E2E coverage audits.
Agentic Reviews: Performs deep PR checks against custom checklists.
Smart Triage: Pre-analyzes tickets to give devs a head start.
Check out the showcase repo to see these patterns in action.
you are part of the problem
Everybody says how good Claude is and I go to my code base and I can't get it to correctly update one xaml file for me. It is quicker to make changes myself than to explain exactly what I need or learn how to do "prompt engineering".
Disclaimer: I don't have access to Claude Code. My employer has only granted me Claude Teams. Supposedly, they don't use my poopy code to train their models if I use my work email Claude so I am supposed to use that. If I'm not pasting code (asking general questions) into Claude, I believe I'm allowed to use whatever.
What's even the point of this comment if you self-admittedly don't have access to the flagship tool that everyone has been using to make these big bold coding claims?
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Opus 4.5 ate through my Copilot quota last month, and it's already halfway through it for this month. I've used it a lot, for really complex code.
And my conclusion is: it's still not as smart as a good human programmer. It frequently got stuck, went down wrong paths, ignored what I told it to do to do something wrong, or even repeat a previous mistake I had to correct.
Yet in other ways, it's unbelievably good. I can give it a directory full of code to analyze, and it can tell me it's an implementation of Kozo Sugiyama's dagre graph layout algorithm, and immediately identify the file with the error. That's unbelievably impressive. Unfortunately it can't fix the error. The error was one of the many errors it made during previous sessions.
So my verdict is that it's great for code analysis, and it's fantastic for injecting some book knowledge on complex topics into your programming, but it can't tackle those complex problems by itself.
Yesterday and today I was upgrading a bunch of unit tests because of a dependency upgrade, and while it was occasionally very helpful, it also regularly got stuck. I got a lot more done than usual in the same time, but I do wonder if it wasn't too much. Wasn't there an easier way to do this? I didn't look for it, because every step of the way, Opus's solution seemed obvious and easy, and I had no idea how deep a pit it was getting me into. I should have been more critical of the direction it was pointing to.
Copilot and many coding agents truncates the context window and uses dynamic summarization to keep costs low for them. That's how they are able to provide flat fee plans.
You can see some of the context limits here:
https://models.dev/
If you want the full capability, use the API and use something like opencode. You will find that a single PR can easily rack up 3 digits of consumption costs.
Gerring off of their plans and prompts is so worth it, I know from experience, I'm paying less and getting more so far, paying by token, heavy gemini-3-flash user, it's a really good model, this is the future (distillations into fast, good enough for 90% of tasks), not mega models like Claude. Those will still be created for distillations and the harder problems
Maybe not, then. I'm afraid I have no idea what those numbers mean, but it looks like Gemini and ChatGPT 4 can handle a much larger context than Opus, and Opus 4.5 is cheaper than older versions. Is that correct? Because I could be misinterpreting that table.
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People are completely missing the points about agentic development. The model is obviously a huge factor in the quality of the output, but the real magic lies in how the tools are managing and injecting context in to them, as well as the tooling. I switched from Copilot to Cursor at the end of 2025, and it was absolute night and day in terms of how the agents behaved.
Interesting you have this opinion yet you're using Cursor instead of Claude Code. By the same logic, you should get even better results directly using Anthropic's wrapper for their own model.
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In my experience GPT-5 is also much more effective in the Cursor context than the Codex context. Cursor deserves props for doing something right under the hood.
yes just using AI for code analysis is way under appreciated I think. Even the most sceptical people on using it for coding should try it out as a tool for Q&A style code interrogation as well as generating documentation. I would say it zero-shots documentation generation better than most human efforts would to the point it begs the question of whether it's worth having the documentation in the first place. Obviously it can make mistakes but I would say they are below the threshold of human mistakes from what I've seen.
(I haven't used AI much, so feel free to ignore me.)
This is one thing I've tried using it for, and I've found this to be very, very tricky. At first glance, it seems unbelievably good. The comments read well, they seem correct, and they even include some very non-obvious information.
But almost every time I sit down and really think about a comment that includes any of that more complex analysis, I end up discarding it. Often, it's right but it's missing the point, in a way that will lead a reader astray. It's subtle and I really ought to dig up an example, but I'm unable to find the session I'm thinking about.
This was with ChatGPT 5, fwiw. It's totally possible that other models do better. (Or even newer ChatGPT; this was very early on in 5.)
Code review is similar. It comes up with clever chains of reasoning for why something is problematic, and initially convinces me. But when I dig into it, the review comment ends up not applying.
It could also be the specific codebase I'm using this on? (It's the SpiderMonkey source.)
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If it can consistently verify that the error persists after fix--you can run (ok maybe you can't budget wise but theoretically) 10000 parallel instances of fixer agents then verify afterwards (this is in line with how the imo/ioi models work according to rumors)
> Opus 4.5 ate theough my Copilot quota last month
Sure, Copilot charges 3x tokens for using Opus 4.5, but, how were you still able to use up half the allocated tokens not even one week into January?
I thought using up 50% was mad for me (inline completions + opencode), that's even worse
I have no idea. Careless use, I guess. I was fixing a bunch of mocks in some once-great but now poorly maintained code, and I wasn't really feeling it so I just fed everything to Claude. Opus, unfortunately. I could easily have downgraded a bit.
It acts differently when using it through a third party tool
Try it again using Claude Code and a subscription to Claude. It can run as a chat window in VS Code and Cursor too.
My employer gets me a Copilot subscription with access to Claude, not a subscription to Claude Code, unfortunately.
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>So my verdict is that it's great for code analysis, and it's fantastic for injecting some book knowledge on complex topics into your programming, but it can't tackle those complex problems by itself.
I don't think you've seen the full potential. I'm currently #1 on 5 different very complex computer engineering problems, and I can't even write a "hello world" in rust or cpp. You no longer need to know how to write code, you just need to understand the task at a high level and nudge the agents in the right direction. The game has changed.
- https://highload.fun/tasks/3/leaderboard
- https://highload.fun/tasks/12/leaderboard
- https://highload.fun/tasks/15/leaderboard
- https://highload.fun/tasks/18/leaderboard
- https://highload.fun/tasks/24/leaderboard
If that is true; then all the commentary around software people having jobs still due to "taste" and other nice words is just that. Commentary. In the end the higher level stuff still needs someone to learn it (e.g. learning ASX2 architecture, knowing what tech to work with); but it requires IMO significantly less practice then coding which in itself was a gate. The skill morphs more into a tech expert rather than a coding expert.
I'm not sure what this means for the future of SWE's though yet. I don't see higher levels of staff in big large businesses bothering to do this, and at some scale I don't see founders still wanting to manage all of these agents, and processes (got better things to do at higher levels). But I do see the barrier of learning to code gone; meaning it probably becomes just like any other job.
How are you qualified to judge its performance on real code if you don't know how to write a hello world?
Yes, LLMs are very good at writing code, they are so good at writing code that they often generate reams of unmaintainable spaghetti.
When you submit to an informatics contest you don't have paying customers who depend on your code working every day. You can just throw away yesterday's code and start afresh.
Claude is very useful but it's not yet anywhere near as good as a human software developer. Like an excitable puppy it needs to be kept on a short leash.
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None of the problems you've shown there are anything close to "very complex computer engineering problems", they're more like "toy problems with widely-known solutions given to students to help them practice for when they encounter actually complex problems".
>I'm currently #1 on 5 different very complex computer engineering problems
Ah yes, well known very complex computer engineering problems such as:
* Parsing JSON objects, summing a single field
* Matrix multiplication
* Parsing and evaluating integer basic arithmetic expressions
And you're telling me all you needed to do to get the best solution in the world to these problems was talk to an LLM?
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What bothers me about posts like this is: mid-level engineers are not tasked with atomic, greenfield projects. If all an engineer did all day was build apps from scratch, with no expectation that others may come along and extend, build on top of, or depend on, then sure, Opus 4.5 could replace them. The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
No doubt I could give Opus 4.5 "build be a XYZ app" and it will do well. But day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right". Any non-technical person might read that and go "if it works it works" but any reasonable engineer will know that thats not enough.
Not necessarily responding to you directly, but I find this take to be interesting, and I see it every time an article like this makes the rounds.
Starting back in 2022/2023:
- (~2022) It can auto-complete one line, but it can't write a full function.
- (~2023) Ok, it can write a full function, but it can't write a full feature.
- (~2024) Ok, it can write a full feature, but it can't write a simple application.
- (~2025) Ok, it can write a simple application, but it can't create a full application that is actually a valuable product.
- (~2025+) Ok, it can write a full application that is actually a valuable product, but it can't create a long-lived complex codebase for a product that is extensible and scalable over the long term.
It's pretty clear to me where this is going. The only question is how long it takes to get there.
> It's pretty clear to me where this is going. The only question is how long it takes to get there.
I don't think its a guarantee. all of the things it can do from that list are greenfield, they just have increasing complexity. The problem comes because even in agentic mode, these models do not (and I would argue, can not) understand code or how it works, they just see patterns and generate a plausible sounding explanation or solution. agentic mode means they can try/fail/try/fail/try/fail until something works, but without understanding the code, especially of a large, complex, long-lived codebase, they can unwittingly break something without realising - just like an intern or newbie on the project, which is the most common analogy for LLMs, with good reason.
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Well, the first 90% is easy, the hard part is the second 90%.
Case in point: Self driving cars.
Also, consider that we need to pirate the whole internet to be able to do this, so these models are not creative. They are just directed blenders.
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Note that blog posts rarely show the 20 other times it failed to build something and only that time that it happened to work.
We've been having same progression with self driving cars and they are also stuck on the last 10% for last 5 years
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> - (~2023) Ok, it can write a full function, but it can't write a full feature.
The trend is definitely here, but even today, heavily depends on the feature.
While extra useful, it requires intense iteration and human insight for > 90% of our backlog. We develop a cybersecurity product.
I haven't seen an AI successfully write a full feature to an existing codebase without substantial help, I don't think we are there yet.
> The only question is how long it takes to get there.
This is the question and I would temper expectations with the fact that we are likely to hit diminishing returns from real gains in intelligence as task difficulty increases. Real world tasks probably fit into a complexity hierarchy similar to computational complexity. One of the reasons that the AI predictions made in the 1950s for the 1960s did not come to be was because we assumed problem difficulty scaled linearly. Double the computing speed, get twice as good at chess or get twice as good at planning an economy. P, NP separation planed these predictions. It is likely that current predictions will run into similar separations.
It is probably the case that if you made a human 10x as smart they would only be 1.25x more productive at software engineering. The reason we have 10x engineers is less about raw intelligence, they are not 10x more intelligent, rather they have more knowledge and wisdom.
Yeah maybe, but personally it feels more like a plateau to me than an exponential takeoff, at the moment.
And this isn't a pessimistic take! I love this period of time where the models themselves are unbelievably useful, and people are also focusing on the user experience of using those amazing models to do useful things. It's an exciting time!
But I'm still pretty skeptical of "these things are about to not require human operators in the loop at all!".
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Each of these years we’ve had a claim that it’s about to replace all engineers.
By your logic, does it mean that engineers will never get replaced?
Sure, eventually we'll have AGI, then no worries, but in the meantime you can only use the tools that exist today, and dreaming about what should be available in the future doesn't help.
I suspect that the timeline from autocomplete-one-line to autocomplete-one-app, which was basically a matter of scaling and RL, may in retrospect turn out to have been a lot faster that the next LLM to AGI step where it becomes capable of using human level judgement and reasoning, etc, to become a developer, not just a coding tool.
Ok, it can create a long-lived complex codebase for a product that is extensible and scalable over the long term, but it doesn't have cool tattoos and can't fancy a matcha
This is disingenuous because LLMs were already writing full, simple applications in 2023.[0]
They're definitely better now, but it's not like ChatGPT 3.5 couldn't write a full simple todo list app in 2023. There were a billion blog posts talking about that and how it meant the death of the software industry.
Plus I'd actually argue more of the improvements have come from tooling around the models rather than what's in the models themselves.
[0] eg https://www.youtube.com/watch?v=GizsSo-EevA
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I use it on a 10 years codebase, needs to explain where to get context but successfully works 90% of time
There are two types of right/wrong ways to build: the context specific right/wrong way to build something and an overly generalized engineer specific right/wrong way to build things.
I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.
I remember cases where a team of engineers built something the "right" way but it turned out to be the wrong thing. (Well engineered thing no one ever used)
Sometimes hacking something together messily to confirm it's the right thing to be building is the right way. Then making sure it's secure, then finally paying down some technical debt to make it more maintainable and extensible.
Where I see real silly problems is when engineers over-engineer from the start before it's clear they are building the right thing, or when management never lets them clean up the code base to make it maintainable or extensible when it's clear it is the right thing.
There's always a balance/tension, but it's when things go too far one way or another that I see avoidable failures.
*I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.*
Gosh I am so tired with that one - someone had a case that burned them in some previous project and now his life mission is to prevent that from happening ever again, and there would be no argument they will take.
Then you get like up to 10 engineers on typical team and team rotation and you end up with all kinds of "we have to do it right because we had to pull all nighter once, 5 years ago" baked in the system.
Not fun part is a lot of business/management people "expect" having perfect solution right away - there are some reasonable ones that understand you need some iteration.
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> I've worked on teams where multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered. It usually took an outsider to proactively remind them what actually mattered to the business case.
My first thought was that you probably also have different biases, priorities and/or taste. As always, this is probably very context-specific and requires judgement to know when something goes too far. It's difficult to know the "most correct" approach beforehand.
> Sometimes hacking something together messily to confirm it's the right thing to be building is the right way. Then making sure it's secure, then finally paying down some technical debt to make it more maintainable and extensible.
I agree that sometimes it is, but in other cases my experience has been that when something is done, works and is used by customers, it's very hard to argue about refactoring it. Management doesn't want to waste hours on it (who pays for it?) and doesn't want to risk breaking stuff (or changing APIs) when it works. It's all reasonable.
And when some time passes, the related intricacies, bigger picture and initially floated ideas fade from memory. Now other stuff may depend on the existing implementation. People get used to the way things are done. It gets harder and harder to refactor things.
Again, this probably depends a lot on a project and what kind of software we're talking about.
> There's always a balance/tension, but it's when things go too far one way or another that I see avoidable failures.
I think balance/tension describes it well and good results probably require input from different people and from different angles.
I know what you are talking about, but there is more to life than just product-market fit.
Hardly any of us are working on Postgres, Photoshop, blender, etc. but it's not just cope to wish we were.
It's good to think about the needs to business and the needs of society separately. Yes, the thing needs users, or no one is benefiting. But it also needs to do good for those users, and ultimately, at the highest caliber, craftsmanship starts to matter again.
There are legitimate reasons for the startup ecosystem to focus firstly and primarily on getting the users/customers. I'm not arguing against that. What I am arguing is why does the industry need to be dominated by startups in terms of the bulk of the products (not bulk of the users). It begs the question of how much societally-meaningful programming waiting to be done.
I'm hoping for a world where more end users code (vibe or otherwise) and the solve their own problems with their own software. I think that will make more a smaller, more elite software industry that is more focused on infrastructure than last-mile value capture. The question is how to fund the infrastructure. I don't know except for the most elite projects, which is not good enough for the industry (even this hypothetical smaller one) on the whole.
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> ...multiple engineers argued about the "right" way to build something. I remember thinking that they had biases based on past experiences and assumptions about what mattered.
I usually resolve this by putting on the table the consequences and their impacts upon my team that I’m concerned about, and my proposed mitigation for those impacts. The mitigation always involves the other proposer’s team picking up the impact remediation. In writing. In the SOP’s. Calling out the design decision by day of the decision to jog memories and names of those present that wanted the design as the SME’s. Registered with the operations center. With automated monitoring and notification code we’re happy to offer.
Once people are asked to put accountable skin in the sustaining operations, we find out real fast who is taking into consideration the full spectrum end to end consequences of their decisions. And we find out the real tradeoffs people are making, and the externalities they’re hoping to unload or maybe don’t even perceive.
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Another thing that gets me with projects like this, there are already many examples of image converters, minesweeper clones etc that you can just fork on GitHub, the value of the LLM here is largely just stripping the copyright off
It’s kind of funny - there’s another thread up where a dev claimed a 20-50x speed up. To their credit they posted videos and links to the repo of their work.
And when you check the work, a large portion of it was hand rolling an ORM (via an LLM). Relatively solved problem that an LLM would excel at, but also not meaningfully moving the needle when you could use an existing library. And likely just creating more debt down the road.
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- I cloned a project from GitHub and made some minor modifications.
- I used AI-assisted programming to create a project.
Even if the content is identical, or if the AI is smart enough to replicate the project by itself, the latter can be included on a CV.
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Have you ever tried to find software for a specific need? I usually spend hours investigating anything I can find only to discover that all options are bad in one way or another and cover my use case partially at best. It's dreadful, unrewarding work that I always fear. Being able to spent those hours to develop custom solution that has exactly what I need, no more, no less, that I can evolve further as my requirements evolve, all that while enjoying myself, is a godsend.
Anecdata but I’ve found Claude code with Opus 4.5 able to do many of my real tickets in real mid and large codebases at a large public startup. I’m at senior level (15+ years). It can browse and figure out the existing patterns better than some engineers on my team. It used a few rare features in the codebase that even I had forgotten about and was about to duplicate. To me it feels like a real step change from the previous models I’ve used which I found at best useless. It’s following style guides and existing patterns well, not just greenfield. Kind of impressive, kind of scary
Same anecdote for me (except I'm +/- 40 years experience). I consider my self a pretty good dev for non-web dev (GPU's, assembly, optimisation,...) and my conclusion is the same as you: impressive and scary. If the somehow the idea of what you want to do is on the web in text or in code, then Claude most likely has it. And its ability to understand my own codebases is just crazy (at my age, memory is declining and having Claude to help is just waow). Of course it fails some times, of course it need direction, but the thing it produces is really good.
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I'm seeing this as well. Not huge codebases but not tiny - 4 year old startup. I'm new there and it would have been impossible for me to deliver any value this soon. 12 years experience; this thing is definitely amazing. Combined with a human it can be phenomenal. It also helped me tons with lots of external tools, understand what data/marketing teams are doing and even providing pretty crucial insights to our leadership that Gemini have noticed. I wouldn't try to completely automate the humans out of the loop though just yet, but this tech for sure is gonna downsize team numbers (and at the same time - allow many new startups to come to life with little capital that eventually might grow and hire people. So unclear how this is gonna affect jobs.)
I've also found it to keep such a constrained context window (on large codebases), that it writes a secondary block of code that already had a solution in a different area of the same file.
Nothing I do seems to fix that in its initial code writing steps. Only after it finishes, when I've asked it to go back and rewrite the changes, this time making only 2 or 3 lines of code, does it magically (or finally) find the other implementation and reuse it.
It's freakin incredible at tracing through code and figuring it out. I <3 Opus. However, it's still quite far from any kind of set-and-forget-it.
Same exist in humans also, I worked with a developer who had 15 year experience and was tech lead in a big Indian firm, We started something together, 3 months back when I checked the Tables I was shocked to see how he fucked up and messed the DB. Finally the only option left with me was to quit because i know it will break in production and if i onboarded a single customer my life would be screwed. He mixed many things with frontend and offloaded even permissions to frontend, and literally copied tables in multiple DB (We had 3 services). I still cannot believe how he worked as a tch lead for 15 years. each DB had more than 100 tables and out of that 20-25 were duplicates. He never shared code with me, but I smelled something fishy when bug fixing was never ending loop and my front end guy told me he cannot do it anymore. Only mistake I did was I trusted him and worst part is he is my cousin and the relation became sour after i confronted him and decided to quit.
This sounds like a culture issue in the development process, I have seen this prevented many times. Sure I did have to roll back a feature I did not sign off just before new years. So as you say it happens.
How did he not share code if you're working together?
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> The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
You’re talking like in the year 2026 we’re still writing code for future humans to understand and improve.
I fear we are not doing that. Right now, Opus 4.5 is writing code that later Opus 5.0 will refactor and extend. And so on.
This sounds like magical thinking.
For one, there are objectively detrimental ways to organize code: tight coupling, lots of mutable shared state, etc. No matter who or what reads or writes the code, such code is more error-prone, and more brittle to handle.
Then, abstractions are tools to lower the cognitive load. Good abstractions reduce the total amount of code written, allow to reason about the code in terms of these abstractions, and do not leak in the area of their applicability. Say Sequence, or Future, or, well, function are examples of good abstractions. No matter what kind of cognitive process handles the code, it benefits from having to keep a smaller amount of context per task.
"Code structure does not matter, LLMs will handle it" sounds a bit like "Computer architectures don't matter, the Turing Machine is proved to be able to handle anything computable at all". No, these things matter if you care about resource consumption (aka cost) at the very least.
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Opus 4.5 is writing code that Opus 5.0 will refactor and extend. And Opus 5.5 will take that code and rewrite it in C from the ground up. And Opus 6.0 will take that code and make it assembly. And Opus 7.0 will design its own CPU. And Opus 8.0 will make a factory for its own CPUs. And Opus 9.0 will populate mars. And Opus 10.0 will be able to achieve AGI. And Opus 11.0 will find God. And Opus 12.0 will make us a time machine. And so on.
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Up until now, no business has been built on tools and technology that no one understands. I expect that will continue.
Given that, I expect that, even if AI is writing all of the code, we will still need people around who understand it.
If AI can create and operate your entire business, your moat is nil. So, you not hiring software engineers does not matter, because you do not have a business.
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In my experience, using LLMs to code encouraged me to write better documentation, because I can get better results when I feed the documentation to the LLM.
Also, I've noticed failure modes in LLM coding agents when there is less clarity and more complexity in abstractions or APIs. It's actually made me consider simplifying APIs so that the LLMs can handle them better.
Though I agree that in specific cases what's helpful for the model and what's helpful for humans won't always overlap. Once I actually added some comments to a markdown file as note to the LLM that most human readers wouldn't see, with some more verbose examples.
I think one of the big problems in general with agents today is that if you run the agent long enough they tend to "go off the rails", so then you need to babysit them and intervene when they go off track.
I guess in modern parlance, maintaining a good codebase can be framed as part of a broader "context engineering" problem.
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We don't know what Opus 5.0 will be able to refactor.
If argument is "humans and Opus 4.5 cannot maintain this, but if requirements change we can vibe-code a new one from scratch", that's a coherent thesis, but people need to be explicit about this.
(Instead this feels like the mott that is retreated to, and the bailey is essentially "who cares, we'll figure out what to do with our fresh slop later".)
Ironically, I've been Claude to be really good at refactors, but these are refactors I choose very explicitly. (Such as I start the thing manually, then let it finish.) (For an example of it, see me force-pushing to https://github.com/NixOS/nix/pull/14863 implementing my own code review.)
But I suspect this is not what people want. To actually fire devs and not rely on from-scratch vibe-coding, we need to figure out which refactors to attempt in order to implement a given feature well.
That's a very creative open-ended question that I haven't even tried to let the LLMs take a crack at it, because why I would I? I'm plenty fast being the "ideas guy". If the LLM had better ideas than me, how would I even know? I'm either very arrogant or very good because I cannot recall regretting one of my refactors, at least not one I didn't back out of immediately.
Refactoring does always cost something and I doubt LLMs will ever change that. The more interesting question is whether the cost to refactor or "rewrite" the software will ever become negligible. Until it isn't, it's short-sighted to write code in the manner you're describing. If software does become that cheap, then you can't meaningfully maintain a business on selling software anyway.
This is the question! Your narrative is definitely plausible, and I won't be shocked if it turns out this way. But it still isn't my expectation. It wasn't when people were saying this in 2023 or in 2024, and I haven't been wrong yet. It does seem more likely to me now than it did a couple years ago, but still not the likeliest outcome in the next few years.
But nobody knows for sure!
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Yeah I think it's a mistake to focus on writing "readable" or even "maintainable" code. We need to let go of these aging paradigms and be open to adopting a new one.
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One thing I've been tossing around in my head is:
- How quickly is cost of refactor to a new pattern with functional parity going down?
- How does that change the calculus around tech debt?
If engineering uses 3 different abstractions in inconsistent ways that leak implementation details across components and duplicate functionality in ways that are very hard to reason about, that is, in conventional terms, an existential problem that might kill the entire business, as all dev time will end up consumed by bug fixes and dealing with pointless complexity, velocity will fall to nothing, and the company will stop being able to iterate.
But if claude can reliably reorganize code, fix patterns, and write working migrations for state when prompted to do so, it seems like the entire way to reason about tech debt has changed. And it has changed more if you are willing to bet that models within a year will be much better at such tasks.
And in my experience, claude is imperfect at refactors and still requires review and a lot of steering, but it's one of the things it's better at, because it has clear requirements and testing workflows already built to work with around the existing behavior. Refactoring is definitely a hell of a lot faster than it used to be, at least on the few I've dealt with recently.
In my mind it might be kind of like thinking about financial debt in a world with high inflation, in that the debt seems like it might get cheaper over time rather than more expensive.
> But if claude can reliably reorganize code, fix patterns, and write working migrations for state when prompted to do so, it seems like the entire way to reason about tech debt has changed.
Yup, I recently spent 4 days using Claude to clean up a tool that's been in production for over 7 years. (There's only about 3 months of engineering time spent on it in those years.)
We've known what the tool needed for many years, but ugh, the actual work was fairly messy and it was never a priority. I reviewed all of Opus's cleanup work carefully and I'm quite content with the result. Maybe even "enthusiastic" would be accurate.
So even if Claude can't clean up all the tech debt in a totally unsupervised fashion, it can still help address some kinds of tech debt extremely rapidly.
Good point. Most of the cost in dealing with tech debt is reading the code and noting the issues. I found that Claude can produce much better code when it has a functionally correct reference implementation. Also it's not needed to very specifically point out issues. I once mentioned "I see duplicate keys in X and Y, rework it to reduce repetition and verbosity". It came up with a much more elegant way to implement it.
So maybe doing 2-3 stages makes sense. First stage needs to be functionallty correct, but you accept code smells such as leaky abstractions, verbosity and repetition. In stage 2 and 3 you eliminate all this. You could integrate this all into the initial specification; you won't even see the smelly intermediate code; it only exists as a stepping stone for the model to iteratively refine the code!
A greenfield project is definitely 'easy mode' for an LLM; especially if the problem area is well understood (and documented).
Opus is great and definitely speeds up development even in larger code bases and is reasonably good at matching coding style/standard to that of of the existing code base.
In my opinion, the big issue is the relatively small context that quickly overwhelms the models when given a larger task on a large codebase.
For example, I have a largish enterprise grade code base with nice enterprise grade OO patterns and class hierarchies. There was a simple tech debt item that required refactoring about 30-40 classes to adhere to a slightly different class hierarchy. The work is not difficult, just tedious, especially as unit tests need to be fixed up.
I threw Opus at it with very precise instructions as to what I wanted it to do and how I wanted it to do it. It started off well but then disintegrated once it got overwhelmed at the sheer number of files it had to change. At some point it got stuck in some kind of an error loop where one change it made contradicted with another change and it just couldn't work itself out. I tried stopping it and helping it out but at this point the context was so polluted that it just couldn't see a way out. I'd say that once an LLM can handle more 'context' than a senior dev with good knowledge of a large codebase, LLM will be viable in a whole new realm of development tasks on existing code bases. That 'too hard to refactor this/make this work with that' task will suddenly become viable.
I just did something similar and it went swimmingly by doing this: Keep the plan and status in an md file. Tell it to finish one file at a time and run tests and fix issues and then to ask whether to proceed with the next file. You can then easily start a new chat with the same instructions and plan and status if the context gets poisoned.
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You have to think of Opus as a developer whose job at your company lasts somewhere between 30 to 60 minutes before you fire them and hire a new one.
Yes, it's absurd but it's a better metaphor than someone with a chronic long term memory deficit since it fits into the project management framework neatly.
So this new developer who is starting today is ready to be assigned their first task, they're very eager to get started and once they start they will work very quickly but you have to onboard them. This sounds terrible but they also happen to be extremely fast at reading code and documentation, they know all of the common programming languages and frameworks and they have an excellent memory for the hour that they're employed.
What do you do to onboard a new developer like this? You give them a well written description of your project with a clear style guide and some important dos and don'ts, access to any documentation you may have and a clear description of the task they are to accomplish in less than one hour. The tighter you can make those documents, the better. Don't mince words, just get straight to the point and provide examples where possible.
The task description should be well scoped with a clear definition of done, if you can provide automated tests that verify when it's complete that's even better. If you don't have tests you can also specify what should be tested and instruct them to write the new tests and run them.
For every new developer after the first you need a record of what was already accomplished. Personally, I prefer to use one markdown document per working session whose filename is a date stamp with the session number appended. Instruct them to read the last X log files where X is however many are relevant to the current task. Most of the time X=1 if you did a good job of breaking down the tasks into discrete chunks. You should also have some type of roadmap with milestones, if this file will be larger than 1000 lines then you should break it up so each milestone is its own document and have a table of contents document that gives a simple overview of the total scope. Instruct them to read the relevant milestone.
Other good practices are to tell them to write a new log file after they have completed their task and record a summary of what they did and anything they discovered along the way plus any significant decisions they made. Also tell them to commit their work afterwards and Opus will write a very descriptive commit message by default (but you can instruct them to use whatever format you prefer). You basically want them to get everything ready for hand-off to the next 60 minute developer.
If they do anything that you don't want them to do again make sure to record that in CLAUDE.md. Same for any other interventions or guidance that you have to provide, put it in that document and Opus will almost always stick to it unless they end up overfilling their context window.
I also highly recommend turning off auto-compaction. When the context gets compacted they basically just write a summary of the current context which often removes a lot of the important details. When this happens mid-task you will certainly lose parts of the context that are necessary for completing the task. Anthropic seems to be working hard at making this better but I don't think it's there yet. You might want to experiment with having it on and off and compare the results for yourself.
If your sessions are ending up with >80% of the context window used while still doing active development then you should re-scope your tasks to make them smaller. The last 20% is fine for doing menial things like writing the summary, running commands, committing, etc.
People have built automated systems around this like Beads but I prefer the hands-on approach since I read through the produced docs to make sure things are going ok and use them as a guide for any changes I need to make mid-project.
With this approach I'm 99% sure that Opus 4.5 could handle your refactor without any trouble as long as your classes aren't so enormous that even working on a single one at a time would cause problems with the context window, and if they are then you might be able to handle it by cautioning Opus to not read the whole file and to just try making targeted edits to specific methods. They're usually quite good at finding and extracting just the sections that they need as long as they have some way to know what to look for ahead of time.
Hope this helps and happy Clauding!
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This will work (if you add more details):
"Have an agent investiate issue X in modules Y and Z. The agent should place a report at ./doc/rework-xyz-overview.md with all locations that need refactoring. Once you have the report, have agents refactor 5 classes each in parallel. Each agent writes a terse report in ./doc/rework-xyz/ When they are all done, have another agent check all the work. When that agent reports everything is okay, perform a final check yourself"
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> If all an engineer did all day was build apps from scratch, with no expectation that others may come along and extend, build on top of, or depend on, then sure, Opus 4.5 could replace them.
Why do they need to be replaced? Programmers are in the perfect place to use AI coding tools productively. It makes them more valuable.
Because we’re expensive and companies would love to get rid of us
I had Opus write a whole app for me in 30 seconds the other night. I use a very extensive AGENTS.md to guide AI in how I like my code chiseled. I've been happily running the app without looking at a line of it, but I was discussing the app with someone today, so I popped the code open to see what it looked like. Perfect. 10/10 in every way. I would not have written it that good. It came up with at least one idea I would not have thought of.
I'm very lucky that I rarely have to deal with other devs and I'm writing a lot of code from scratch using whatever is the latest version of the frameworks. I understand that gives me a lot of privileges others don't have.
Can you show us that amazing 10/10 app?
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Their thesis is that code quality does not matter as it is now a cheap commodity. As long as it passes the tests today it's great. If we need to refactor the whole goddamn app tomorrow, no problem, we will just pay up the credits and do it in a few hours.
The fundamental assumption is completely wrong. Code is not a cheap commodity. It is in fact so disastrously expensive that the entire US economy is about to implode while we're unbolting jet engines from old planes to fire up in the parking lots of datacenters for electricity.
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It matters for all the things you’d be able to justify paying a programmer for. What’s about to change is that there will be tons of these little one-off projects that previously nobody could justify paying $150/hr for. A mass democratization of software development. We’ve yet to see what that really looks like.
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> Their thesis is that code quality does not matter as it is now a cheap commodity.
That's not how I read it. I would say that it's more like "If a human no longer needs to read the code, is it important for it to be readable?"
That is, of course, based on the premise that AI is now capable of both generating and maintaining software projects of this size.
Oh, and it begs another question: are human-readable and AI-readable the same thing? If they're not, it very well could make sense to instruct the model to generate code that prioritizes what matters to LLMs over what matters to humans.
Yes agreed, and tbh even if that thesis is wrong, what does it matter?
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>What bothers me about posts like this is: mid-level engineers are not tasked with atomic, greenfield projects
They get those ocassionally all the time though too. Depends on the company. In some software houses it's constant "greenfield projects", one after another. And even in companies with 1-2 pieces of main established software to maintain, there are all kinds of smaller utilities or pipelines needed.
>But day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right".
In some cases that's legit. In other cases it's just "it did it well, but not how I'd done it", which is often needless stickness to some particular style (often a contention between 2 human programmers too).
Basically, what FloorEgg says in this thread: "There are two types of right/wrong ways to build: the context specific right/wrong way to build something and an overly generalized engineer specific right/wrong way to build things."
And you can always not just tell it "build me this feature", but tell it (high level way) how to do it, and give it a generic context about such preferences too.
> its building it the right way, in an easily understood way, in a way that's easily extensible.
When I worked at Google, people rarely got promoted for doing that. They got promoted for delivering features or sometimes from rescuing a failing project because everyone was doing the former until promotion velocity dropped and your good people left to other projects not yet bogged down too far.
Yeah. Just like another engineer. When you tell another engineer to build you a feature, it's improbable they'll do it they way that you consider "right."
This sounds a lot like the old arguments around using compilers vs hand-writing asm. But now you can tell the LLM how you want to implement the changes you want. This will become more and more relevant as we try to maintain the code it generates.
But, for right now, another thing Claude's great at is answering questions about the codebase. It'll do the analysis and bring up reports for you. You can use that information to guide the instructions for changes, or just to help you be more productive.
You can look at my comment history to see the evidence to how hostile I was to agentic coding. Opus 4.5 completely changed my opinion.
This thing jumped into a giant JSF (yes, JSF) codebase and started fixing things with nearly zero guidance.
Even if you are going green field, you need to build it the way it is likely to be used based a having a deep familiarity with what that customer's problems are and how their current workflow is done. As much as we imagine everything is on the internet, a bunch of this stuff is not documented anywhere. An LLM could ask the customer requirement questions but that familiarity is often needed to know the right questions to ask. It is hard to bootstrap.
Even if it could build the perfect greenfield app, as it updates the app it is needs to consider backwards compatibility and breaking changes. LLMs seem very far as growing apps. I think this is because LLMs are trained on the final outcome of the engineering process, but not on the incremental sub-commit work of first getting a faked out outline of the code running and then slowly building up that code until you have something that works.
This isn't to say that LLMs or other AI approaches couldn't replace software engineering some day, but they clear aren't good enough yet and the training sets they have currently have access to are unlikely to provide the needed examples.
In my personal experience, Claude is better at greenfield, Codex is better at fitting in. Claude is the perfect tool for a "vibe coder", Codex is for the serious engineer who wants to get great and real work done.
Codex will regularly give me 1000+ line diffs where all my comments (I review every single line of what agents write) are basically nitpicks. "Make this shallow w/ early return, use | None instead of Optional", that sort of thing.
I do prompt it in detail though. It feels like I'm the person coming in with the architecture most of the time, AI "draws the rest of the owl."
My favorite benchmark for LLMs and agents is to have it port a medium-complexity library to another programming language. If it can do that well, it's pretty capable of doing real tasks. So far, I always have to spend a lot of time fixing errors. There are also often deep issues that aren't obvious until you start using it.
Comments on here often criticise ports as easy for LLMs to do because there's a lot of training and tests are all there, which is not as complex as real word tasks
I find Opus 4.5 very, very strong at matching the prevailing conventions/idioms/abstractions in a large, established codebase. But I guess I'm quite sensitive to this kind of thing so I explicitly ask Opus 4.5 to read adjacent code which is perhaps why it does it so well. All it takes is a sentence or two, though.
I don’t know what I’m doing wrong. Today I tried to get it to upgrade Nx, yarn and some resolutions in a typescript monorepo with about 20 apps at work (Opus 4.5 through Kiro) and it just…couldn’t do it. It hit some snags with some of the configuration changes required by the upgrade and resorted to trying to make unwanted changes to get it to build correctly. I would have thought that’s something it could hit out of the park. I finally gave up and just looked at the docs and some stack overflow and fixed it myself. I had to correct it a few times about correct config params too. It kept imagining config options that weren’t valid.
> ask Opus 4.5 to read adjacent code which is perhaps why it does it so well. All it takes is a sentence or two, though.
People keep telling me that an LLM is not intelligence, it's simply spitting out statistically relevant tokens. But surely it takes intelligence to understand (and actually execute!) the request to "read adjacent code".
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Exactly. The main issue IMO is that "software that seems to work" and "software that works" can be very hard to tell apart without validating the code, yet these are drastically different in terms of long-term outcomes. Especially when there's a lot of money, or even lives, riding on these outcomes. Just because LLMs can write software to run the Therac-25 doesn't mean it's acceptable for them to do so.
Your hobby project, though, knock yourself out.
After recently applying Codex to a gigantic old and hairy project that is as far from greenfield it can be, I can assure you this assertion is false. It’s bonkers seeing 5.2 churn though the complexity and understanding dependencies that would take me days or weeks to wrap my head around.
Another thing these posts assume is a single developer keep working on the product with a number of AI agents, not a large team. I think we need to rethink how teams work with AI. Its probably not gonna be a single developer typing a prompt but a team somehow collaborates a prompt or equivalent. XP on steroids? Programming by committee?
But... you can ask! Ask claude to use encapsulation, or to write the equivalent of interfaces in the language you using, and to map out dependencies and duplicate features, or to maintain a dictionary of component responsibilities.
AI coding is a multiplier of writing speed but doesn't excuse planning out and mapping out features.
You can have reasonably engineered code if you get models to stick to well designed modules but you need to tell them.
But time I spend asking is time I could have been writing exactly what I wanted in the first place, if I already did the planning to understand what I wanted. Once I know what I want, it doesn't take that long, usually.
Which is why it's so great for prototyping, because it can create something during the planning, when you haven't planned out quite what you want yet.
On the contrary, Opus 4.5 is the best agent I’ve ever used for making cohesive changes across many files in a large, existing codebase. It maintains our patterns and looks like all the other code. Sometimes it hiccups for sure.
> greenfield
LLMs are pretty good at picking up existing codebases. Even with cleared context they can do „look at this codebase and this spec doc that created it. I want to add feature x“
What size of code base are you talking about? And this is your personal experience?
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I totally agree. And welcome to disposable software age.
Yeah, all of those applications he shows do not really expose any complex business logic.
With all the due respect: a file converter for windows is glueing few windows APIs with the relevant codec.
Now, good luck working on a complex warehouse management application where you need extremely complex logic to sort the order of picking, assembling, packing on an infinite number of variables: weight, amazon prime priority, distribution centers, number and type of carts available, number and type of assembly stations available, different delivery systems and requirements for different delivery operators (such as GLE, DHL, etc) that has to work with N customers all requiring slightly different capabilities and flows, all having different printers and operations, etc, etc. And I ain't even scratching the surface of the business logic complexity (not even mentioning functional requirements) to avoid boring the reader.
Mind you, AI is still tremendously useful in the analysis phase, and can sort of help in some steps of the implementation one, but the number of times you can avoid looking thoroughly at the code for any minor issue or discrepancy is absolutely close to 0.
It just one shots bug fixes in complex codebases.
Copy-paste the bug report and watch it go.
It might scale.
So far, Im not convinced, but lets take a look at fundmentally whats happening and why humans > agents > LLMs.
At its heart, programming is a constraint satisfaction problem.
The more constraints (requirements, syntax, standards, etc) you have, the harder it is to solve them all simultaneously.
New projects with few contributors have fewer constraints.
The process of “any change” is therefore simpler.
Now, undeniably
1) agents have improved the ability to solve constraints by iterating; eg. Generate, test, modify, etc. over raw LLm output.
2) There is an upper bound (context size, model capability) to solve simultaneous constraints.
3) Most people have a better ability to do this than agents (including claude code using opus 4.5).
So, if youre seeing good results from agents, you probably have a smaller set of constraints than other people.
Similarly, if youre getting bad results, you can probably improve them by relaxing some of the constraints (consistent ui, number of contributors, requirements, standards, security requirements, split code into well defined packages).
This will make both agents and humans more productive.
The open question is: will models continue to improve enough to approach or exceed human level ability in this?
Are humans willing to relax the constraints enough for it to be plausible?
I would say currently people clambering about the end of human developers are cluelessly deceived by the “appearance of complexity” which does not match the “reality of constraints” in larger applications.
Opus 4.5 cannot do the work of a human on code bases Ive worked on. Hell, talented humans struggle to work on some of them.
…but that doesnt mean it doesnt work.
Just that, right now, the constraint set it can solve is not large enough to be useful in those situations.
…and increasingly we see low quality software where people care only about speed of delivery; again, lowering the bar in terms of requirements.
So… you know. Watch this space. Im not counting on having a dev job in 10 years. If I do, it might be making a pile of barely working garbage.
…but I have one now, and anyone who thinks that this year people will be largely replaced by AI is probably poorly informed and has misunderstood the capabilities on these models.
Theres only so low you can go in terms of quality.
Based on my experience using these LLMs regularly I strongly doubt it could even build any application with realistic complexity without screwing things up in major ways everywhere, and even on top of that still not meeting all the requirements.
If you have microservices architecture in your project you are set for AI. You can swap out any lacking, legacy microservice in your system with "greenfield" vibecoded one.
Man, I've been biting my tongue all day with regards to this thread and overall discussion.
I've been building a somewhat-novel, complex, greenfield desktop app for 6 months now, conceived and architected by a human (me), visually designed by a human (me), implementation heavily leaning on mostly Claude Code but with Codex and Gemini thrown in the mix for the grunt work. I have decades of experience, could have built it bespoke in like 1-2 years probably, but I wanted a real project to kick the tires on "the future of our profession".
TL;DR I started with 100% vibe code simply to test the limits of what was being promised. It was a functional toy that had a lot of problems. I started over and tried a CLI version. It needed a therapist. I started over and went back to visual UI. It worked but was too constrained. I started over again. After about 10 complete start-overs in blank folders, I had a better vision of what I wanted to make, and how to achieve it. Since then, I've been working day after day, screen after screen, building, refactoring, going feature by feature, bug after bug, exactly how I would if I was coding manually. Many times I've reached a point where it feels "feature complete", until I throw a bigger dataset at it, which brings it to its knees. Time to re-architect, re-think memory and storage and algorithms and libraries used. Code bloated, and I put it on a diet until it was trim and svelte. I've tried many different approaches to hard problems, some of which LLMs would suggest that truly surprised me in their efficacy, but only after I presented the issues with the previous implementation. There's a lot of conversation and back and forth with the machine, but we always end up getting there in the end. Opus 4.5 has been significantly better than previous Anthropic models. As I hit milestones, I manually audit code, rewrite things, reformat things, generally polish the turd.
I tell this story only because I'm 95% there to a real, legitimate product, with 90% of the way to go still. It's been half a year.
Vibe coding a simple app that you just want to use personally is cool; let the machine do it all, don't worry about under the hood, and I think a lot of people will be doing that kind of stuff more and more because it's so empowering and immediate.
Using these tools is also neat and amazing because they're a force multiplier for a single person or small group who really understand what needs done and what decisions need made.
These tools can build very complex, maintainable software if you can walk with them step by step and articulate the guidelines and guardrails, testing every feature, pushing back when it gets it wrong, growing with the codebase, getting in there manually whenever and wherever needed.
These tools CANNOT one-shot truly new stuff, but they can be slowly cajoled and massaged into eventually getting you to where you want to go; like, hard things are hard, and things that take time don't get done for a while. I have no moral compunctions or philosophical musings about utilizing these tools, but IMO there's still significant effort and coordination needed to make something really great using them (and literally minimal effort and no coordination needed to make something passable)
If you're solo, know what you want, and know what you're doing, I believe you might see 2x, 4x gains in time and efficiency using Claude Code and all of his magical agents, but if your project is more than a toy, I would bet that 2x or 4x is applied to a temporal period of years, not days or months!
>day to day, when I ask it "build me this feature" it uses strange abstractions, and often requires several attempts on my part to do it in the way I consider "right"
Then don't ask it to "build me this feature" instead lay out a software development process with designated human in the loop where you want it and guard rails to keep it on track. Create a code review agent to look for and reject strange abstractions. Tell it what you don't like and it's really good at finding it.
I find Opus 4.5, properly prompted, to be significantly better at reviewing code than writing it, but you can just put it in a loop until the code it writes matches the review.
> The hard thing about engineering is not "building a thing that works", its building it the right way, in an easily understood way, in a way that's easily extensible.
The number of production applications that achieve this rounds to zero
I’ve probably managed 300 brownfield web, mobile, edge, datacenter, data processing and ML applications/products across DoD, B2B, consumer and literally zero of them were built in this way
I think there is a subjective difference. When a human builds dogshit at least you know they put some effort and the hours in.
When I'm reading piles of LLM slop, I know that just reading it is already more effort than it took to write. It feels like I'm being played.
This is entirely subjective and emotional. But when someone writes something with an LLM in 5 seconds and asks me to spend hours reviewing...fuck off.
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This is the exact copium I came here to enjoy.
you can definitely just tell it what abstractions you want when adding a feature and do incremental work on existing codebase. but i generally prefer gpt-5.2
I've been using 5.2 a lot lately but hit my quota for the first time (and will probably continue to hit it most weeks) so I shelled out for claude code. What differences do you notice? Any 'metagame' that would be helpful?
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"its building it the right way, in an easily understood way, in a way that's easily extensible"
I am in a unique situation where I work with a variety of codebases over the week. I have had no problem at all utilizing Claude Code w/ Opus 4.5 and Gemini CLI w/ Gemini 3.0 Pro to make excellent code that is indisputably "the right way", in an extremely clear and understandable way, and that is maximally extensible. None of them are greenfield projects.
I feel like this is a bit of je ne sais quoi where people appeal to some indemonstrable essence that these tools just can't accomplish, and only the "non-technical" people are foolish enough to not realize it. I'm a pretty technical person (about 30 years of software development, up to staff engineer and then VP). I think they have reached a pretty high level of competence. I still audit the code and monitor their creations, but I don't think they're the oft claimed "junior developer" replacement, but instead do the work I would have gotten from a very experienced, expert-level developer, but instead of being an expert at a niche, they're experts at almost every niche.
Are they perfect? Far from it. It still requires a practitioner who knows what they're doing. But frequently on here I see people giving takes that sound like they last used some early variant of Copilot or something and think that remains state of the art. The rest of us are just accelerating our lives with these tools, knowing that pretending they suck online won't slow their ascent an iota.
>llm_nerd >created two years ago
You AI hype thots/bots are all the same. All these claims but never backed up with anything to look at. And also alway claiming “you’re holding it wrong”.
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I also have >30 years and I've had the same experience. I noticed an immediate improvement with 4.5 and I've been getting great results in general.
And yes I do make sure it's not generating crazy architecture. It might do that.. if you let it. So don't let it.
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Opus 4.5 has become really capable.
Not in terms of knowledge. That was already phenomenal. But in its ability to act independently: to make decisions, collaborate with me to solve problems, ask follow-up questions, write plans and actually execute them.
You have to experience it yourself on your own real problems and over the course of days or weeks.
Every coding problem I was able to define clearly enough within the limits of the context window, the chatbot could solve and these weren’t easy. It wasn’t just about writing and testing code. It also involved reverse engineering and cracking encoding-related problems. The most impressive part was how actively it worked on problems in a tight feedback loop.
In the traditional sense, I haven’t really coded privately at all in recent weeks. Instead, I’ve been guiding and directing, having it write specifications, and then refining and improving them.
Curious how this will perform in complex, large production environments.
Just some examples I’ve already made public. More complex ones are in the pipeline. With [0], I’m trying to benchmark different coding-agents. With [1], I successfully reverse-engineered an old C64 game using Opus 4.5 only.
Yes, feel free to blame me for the fact that these aren’t very business-realistic.
[0] https://github.com/s-macke/coding-agent-benchmark
[1] https://github.com/s-macke/weltendaemmerung
> You have to experience it yourself on your own real problems and over the course of days or weeks.
How do you stop it from over-engineering everything?
This has always been my problem whether it's Gemini, openai or Claude. Unless you hand-hold it to an extreme degree, it is going to build a mountain next to a molehill.
It may end up working, but the thing is going to convolute apis and abstractions and mix patterns basically everywhere
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Difficult and it really depends on the complexity. I definitely work in a spec-driven way, with a step-by-step implementation phase. If it goes the wrong way I prefer to rewrite the spec and throw away the code.
I have it propose several approaches, pick and choose from each, and remove what I don't want done. "Use the general structure of A, but use the validation structure of D. Using a view translation layer is too much, just rely on FastAPI/SQLModel's implicit view conversion."
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Instructions, in the system prompt for not doing that
Once more people realize how easy it is to customize and personalized your agent, I hope they will move beyond what cookie cutter Big AI like Anthropic and Google give you.
I suspect most won't though because (1) it means you have to write human language, communication, and this weird form of persuasion, (2) ai is gonna make a bunch of them lazy and big AI sold them on magic solutions that require no effort on your part (not true, there is a lot of customizing and it has huge dividends)
“Everything Should Be Made as Simple as Possible, But Not Simpler” should be the ending of every prompt :)
I personally try to narrow scope as much as possible to prevent this. If a human hands me a PR that is not digestible size-wise and content-wise (to me), I am not reviewing and merging it. Same thing with what claude generates with my guidance.
I find my sweet spot is using the Claude web app as a rubber duck as well as feeding it snippets of code and letting it help me refine the specific thing I'm doing.
When I use Claude Code I find that it *can* add a tremendous amount of ability due to its ability to see my entire codebase at once, but the issue is that if I'm doing something where seeing my entire codebase would help that it blasts through my quota too fast. And if I'm tightly scoping it, it's just as easy & faster for me to use the website.
Because of this I've shifted back to the website. I find that I get more done faster that way.
I've had similar experiences but I've been able to start using Claude Code for larger projects by doing some refactoring with the goal of making the codebase understandable by just looking at the interfaces. This along with instructions to prefer looking at the interface for a module unless working directly on the implementation of the module seems to allow further progress to be made within session limits.
By "the website" do you mean you're copy pasting, or are you using the code system where Anthropic clones your code from GitHub and interacts with it in a VM/container for you.
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> In the traditional sense, I haven’t really coded privately at all in recent weeks. Instead, I’ve been guiding and directing, having it write specifications, and then refining and improving them.
This is basically all my side projects.
This has also been my experience.
I've noticed a huge drop in negative comments on HN when discussing LLMs in the last 1-2 months.
All the LLM coded projects I've seen shared so far[1] have been tech toys though. I've watched things pop up on my twitter feed (usually games related), then quietly go off air before reaching a gold release (I manually keep up to date with what I've found, so it's not the algorithm).
I find this all very interesting: LLMs dont change the fundamental drives needed to build successful products. I feel like I'm observing the TikTokification of software development. I dont know why people aren't finishing. Maybe they stop when the "real work" kicks in. Or maybe they hit the limits of what LLMs can do (so far). Maybe they jump to the next idea to keep chasing the rush.
Acquiring context requires real work, and I dont see a way forward to automating that away. And to be clear, context is human needs; i.e. the reasons why someone will use your product. In the game development world, it's very difficult to overstate how much work needs to be done to create a smooth, enjoyable experience for the player.
While anyone may be able to create a suite of apps in a weekend, I think very few of them will have the patience and time to maintain them (just like software development before LLMs! i.e. Linux, open source software, etc.).
[1] yes, selection bias. There are A LOT of AI devs just marketing their LLMs. Also it's DEFINITELY too early to be certain. Take everything Im saying with a one pound grain of salt.
> I've noticed a huge drop in negative comments on HN when discussing LLMs in the last 1-2 months.
real people get fed up of debating the same tired "omg new model 1000x better now" posts/comments from the astroturfers, the shills and their bots each time OpenAI shits out a new model
(article author is a Microslop employee)
Especially when 90% of these articles are based on personal, anecdotally evidence and keep repeating the same points without offering anything new.
If these articles actually provide quantitative results in a study done across an organization and provide concrete suggestions like what Google did a while ago, that would be refreshing and useful.
(Yes, this very article has strong "shill" vibes and fits the patterns above)
Simply this ^ I'm tired of debating bots and people paid to grow the hype, so I won't anymore I'll just work and look for the hype passing by from a distance. In the meanwhile I'll keep waiting for people making actual products with LLMs that will kill old generation products like windows, excel, teams, gmail etc that will replace slop with great ui/ux and push really performant apps
This is a cringe comment from an era of when "Micro$oft" was hip and reads like you are a fanboi for Anthropic/Google foaming at the mouth.
Would be far more useful if you provided actual verifiable information and dropped the cringe memes. Can't take seriously someone using "Microslop" in a sentence".
You're only hurting yourself if you decide there's some wild conspiracy afoot here to pay shills to tell people that coding agents are useful... as opposed to people finding them useful enough to want to tell other people about it.
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It could be that the people who are focused on building monetizable products with LLMs don't feel the need to share what they are doing - they're too busy quietly getting on with building and marketing their products.
Sharing how you're using these tools is quite a lot of work!
What would be more likely,
That people making startups is too bussy working to share it on HN or that AI is useless in real projects.
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Agreed! LLMs are a force multiplier for real products too. They're going to augment people who are willing to do the real work.
But, Im also wondering if LLMs are going to create a new generation of software dev "brain rot" (to use the colloquial term), similar to short form videos.
I should mention in the gamedev world, it's quite common share because sharing is marketing, hence my perspective.
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I admit I'm in this boat. I get immense value from LLMs, easily 5x if not more, and the codebases I work in are large, mature and complex. But providing "receipts" as the kids call it these days would be a huge undertaking, with not a lot of upside. In fact, the downsides are considerable. Aside from the time investment, I have no interest in arguing with people about whether what I work on is just CRUD (it's not) or that the problems I work on are not novel (who cares, your product either provides value for your users or it does not).
The type of people to use AI are necessarily the people who will struggle most when it comes time to do the last essential 20% of the work that AI can't do. Once thinking is required to bring all the parts into a whole, the person who gives over their thinking skills to AI will not be equipped to do the work, either because they never had the capacity to begin with or because AI has smoothed out the ripples of their brain. I say this from experience.
I think you can tell from some answers here that people talk to these models a lot and adapt their language structure :( Means they stop asking themselves whether it makes any sense what they ask the model for. It does not turn middle management into developers it turns developers into middle managers that just shout louder or replace a critical mind with another yesman or the next super best model that finally brings their genius ideas to life. Then well they get to the same wall of having to learn for themselves to reach gold and ofc that's an insult to any manager. Whoever cannot do the insane job has to be wrong, never the one asking for insanity.
Sad i had to scroll so far down to get some fitting description of why those projects all die. Maybe it's not just me leaving all social networks even HN because well you may not talk to 100% bots but you sure talk to 90% of people that talk to models a lot instead of using them as a tool.
Using AI tools makes me think harder.
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Deploying and maintaining something in a production-ready environment is a huge amount of work. It's not surprising that most people give up once they have a tech demo, especially if they're not interested in spending a ton of time maintaining these projects. Last year Karpathy posted about a similar experience, where he quickly vibe coded some tools only to realize that deploying it would take far more effort than he originally anticipated.
I think it's also rewarding to just be able to build something for yourself, and one benefit of scratching your own itch is that you don't have to go through the full effort of making something "production ready". You can just build something that's tailed specifically to the problem you're trying to solve without worrying about edge cases.
Which is to say, you're absolutely right :).
> huge drop in negative comments on HN when discussing LLMs
I interpret it more as spooked silence
Yeah, I do a lot of hobby game making and the 80/20 rule definitely applies. Your game will be "done" in 20% of the time it takes to create a polished product ready for mass consumption.
Stopping there is just fine if you're doing it as a hobby. I love to do this to test out isolated ideas. I have dozens of RPGs in this state, just to play around with different design concepts from technical to gameplay.
Sometimes I feel like a lot of those posts are instances of Kent Brockman: "I for one, welcome our new insect overlords."
Given the enthusiasm of our ruling class towards automating software development work, it may make sense for a software engineer to publicly signal how much onboard as a professional they are with it.
But, I've seen stranger stuff throughout my professional life: I still remember people enthusiastically defending EJB 2.1 and xdoclet as perfectly fine ways of writing software.
Author of the post here.
I appreciate the spirited debate and I agree with most of it - on both sides. It's a strange place to be where I think both arguments for and against this case make perfect sense. All I have to go on then is my personal experience, which is the only objective thing I've got. This entire profession feels stochastic these days.
A few points of clarification...
1. I don't speak for anyone but myself. I'm wrong at least half the time so you've been warned.
2. I didn't use any fancy workflows to build these things. Just used dictation to talk to GitHub Copilot in VS Code. There is a custom agent prompt toward the end of the post I used, but it's mostly to coerce Opus 4.5 into using subagents and context7 - the only MCP I used. There is no plan, implement - nothing like that. On occasion I would have it generate a plan or summary, but no fancy prompt needed to do that - just ask for it. The agent harness in VS Code for Opus 4.5 is remarkably good.
3. When I say AI is going to replace developers, I mean that in the sense that it will do what we are doing now. It already is for me. That said, I think there's a strong case that we will have more devs - not less. Think about it - if anyone with solid systems knowledge can build anything, the only way you can ship more differentiating features than me is to build more of them. That is going to take more people, not more agents. Agents can only scale as far as the humans who manage them.
New account because now you know who I am :)
I would be really interested to learn more behind the scenes of the iOS app process. Having tried Claude Code to develop an iOS app ~6 months ago, it was pretty painful to get it to make something that looked good and was functional.
Once Opus "finished", how did you validate and give it feedback it might not have access to (like iPhone simulator testing)?
What do you think about the market for custom apps? Like one app, one customer? You describe future businesses as having one app/service and using AI to add more features, but you did something very different for your wife with AI and it sounds like it added a lot of value.
I had a similar set of experiences with GPT 5.x over the holiday break, across somewhat more disparate domains: https://taoofmac.com/space/notes/2025/12/31/1830
I hacked together a Swift tool to replace a Python automation I had, merged an ARM JIT engine into a 68k emulator, and even got a very decent start on a synth project I’ve been meaning to do for years.
What has become immensely apparent to me is that even gpt-5-mini can create decent Go CLI apps provided you write down a coherent spec and review the code as if it was a peer’s pull request (the VS Code base prompts and tooling steer even dumb models through a pretty decent workflow).
GPT 5.2 and the codex variants are, to me, every bit as good as Opus but without the groveling and emojis - I can ask it to build an entire CI workflow and it does it in pretty much one shot if I give it the steps I want.
So for me at least this model generation is a huge force multiplier (but I’ve always been the type to plan before coding and reason out most of the details before I start, so it might be a matter of method).
To add to the anecdata, today GPT 5.2-whatever hallucinated the existence of two CLI utilities, and when corrected, then hallucinated the existence of non-existent, but plausible, features/options of CLI utilities that do actually exist.
I had to dig through source code to confirm whether those features actually existed. They don't, so the CLI tools GPT recommended aren't actually applicable to my use case.
Yesterday, it hallucinated features of WebDav clients, and then talked up an abandoned and incomplete project on GitHub with a dozen stars as if it was the perfect fit for what I was trying to do, when it wasn't.
I only remember these because they're recent and CLI related, given the topic, but there are experiences like this daily across different subjects and domains.
Were you running it inside a coding agent like Codex?
If so then it should have realized its mistake when it tried to run those CLI commands and saw the error message. Then it can try something different instead.
If you were using a regular chat interface and expecting it to know everything without having an environment to try things out then yeah, you're going to be disappointed.
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Yeah, it needs a steady hand on the tiller. However throw together improvements of 70%, -15%, 95%, 99%, -7% across all the steps and overall you're way ahead.
SimonW's approach of having a suite of dynamic tools (agents) grind out the hallucinations is a big improvement.
In this case expressing the feeback validation and investing in the setup may help smooth these sharp edges.
I tried generating code with ChatGPT 5.2, but the results weren't that great:
1) It often overcomplicates things for me. After I refactor its code, it's usually half the size and much more readable. It often adds unnecessary checks or mini-features 'just in case' that I don't need.
2) On the other hand, almost every function it produces has at least one bug or ignores at least one instruction. However, if I ask it to review its own code several times, it eventually finds the bugs.
I still find it very useful, just not as a standalone programming agent. My workflow is that ChatGPT gives me a rough blueprint and I iterate on it myself, I find this faster and less error-prone. It's usually most useful in areas where I'm not an expert, such as when I don't remember exact APIs. In areas where I can immediately picture the entire implementation in my head, it's usually faster and more reliable to write the code myself.
Well, like I pointed out somewhere else, VS Code gives it a set of prompts and tools that makes it very effective for me. I see that a lot of people are still copy/pasting stuff instead of having the “integrated” experience, and it makes a real difference.
(Cue the “you’re holding it wrong meme” :))
Gemini 3 Pro (High) via Antigravity has been similarly great recently. So have tools that I imagine call out to these higher-power models: Amp and Junie. In a two-week blur I brought forth the bulk of a Ruby library that includes bindings to the Ratatui rust crate for making TUIs in Ruby. During that time I also brought forth documentation, example applications, build and devops tooling, and significant architectural decisions & roadmaps for the future. It's pretty unbelievable, but it's all there in the git and CI history. https://sr.ht/~kerrick/ratatui_ruby/
I think the following things are true now:
- Vibe Coding is, more than ever, "autopilot" in the aviation sense, not the colloquial sense. You have to watch it, you are responsible, the human has do run takeoff/landing (the hard parts), but it significantly eases and reduces risk on a bulk of the work.
- The gulf of developer experience between today's frontier tooling and six months ago is huge. I pushed hard to understand and use these tools throughout last year, and spent months discouraged--back to manual coding. Folks need to re-evaluate by trying premium tools, not free ones.
- Tooling makers have figured out a lot of neat hacks to work around the limitations of LLMs to make it seem like they're even better than they are. Junie integrates with your IDE, Antigravity has multiple agents maintaining background intel on your project and priorities across chats. Antigravity also compresses contexts and starts new ones without you realizing it, calls to sub-agents to avoid context pollution, and other tricks to auto-manage context.
- Unix tools (sed, grep, awk, etc.) and the git CLI (ls-tree, show, --stat, etc.) have been a huge force-multiplier, as they keep the context small compared to raw ingestion of an entire file, allowing the LLMs to get more work done in a smaller context window.
- The people who hire programmers are still not capable of Vibe Coding production-quality web apps, even with all these improvements. In fact, I believe today this is less of a risk than I feared 10 months ago. These are advanced tools that need constant steering, and a good eye for architecture, design, developer experience, test quality, etc. is the difference between my vibe coded Ruby [0] (which I heavily stewarded) and my vibe coded Rust [1] (I don't even know what borrow means).
[0]: https://git.sr.ht/~kerrick/ratatui_ruby/tree/stable/item/lib
[1]: https://git.sr.ht/~kerrick/ratatui_ruby/tree/stable/item/ext...
Were they able to link Antigravity to your paid subscription? I have a Google ultra AI sub and antigrav ran out of credits within 30 minutes for me. Of course that was a few weeks ago, and I’m hoping that they fixed this
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The thing is that CLI utilities code is probably easier to write for an LLM than most other things. In my experience an LLM does best with backend and terminal things. Anything that resembles boilerplate is great. It does well refactoring unit tests, wrapping known code in a CLI, and does decent work with backend RESTful APIs. Where it fails utterly is things like HTML/CSS layout, JavaScript frontend code for SPAs, and particularly real world UI stuff that requires seeing and interacting with a web page/app where things like network latency and errors, browser UI, etc. can trip it up. Basically when the input and output are structured and known an LLM will do well. When they are “look and feel” they fail and fail until they make the code unmaintainable.
This experience for me is current but I do not normally use Opus so perhaps I should give it a try and figure out if it can reason around problems I myself do not foresee (for example a browser JS API quirk that I had never seen).
I've been having a surprising amount of success recently telling Claude Code to test the frontend it's building using Playwright, including interacting with the UI and having it take its own screenshots to feed into its vision ability to "see" what's going on.
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In my experience with a combo of Claude Code and Gemini Pro (and having added Codex to the mix about a week ago as well), it matters less whether it’s CLI, backend, frontend, DB queries, etc. but more how cookiecutter the thing you’re building is. For building CRUD views or common web application flows, it crushes it, especially if you can point it to a folder and just tell it to do more of the same, adapted to a new use case.
But yes, the more specific you get and the more moving pieces you have, the more you need to break things down into baby steps. If you don’t just need it to make A work, but to make it work together with B and C. Especially given how eager Claude is to find cheap workarounds and escape hatches, botching things together in any way seemingly to please the prompter as fast as possible.
Since one of my holiday projects was completely rebuilding the Node-RED dashboard in Preact, I have to challenge that a bit. How were you using the model?
I couldn't disagree more. I've had Claude absolutely demolish large HTML/CSS/JS/React projects. One key is to give it some way to "see" and interact with the page. I usually use Playwright for this. Allowing it to see its own changes and iterate on them was the key unlock for me.
Putting the performance aside for now as I just started trying out Opus 4.5, can't say too much yet, I don't hype or hate AI as of now, it's simply useful.
Time will tell what happens, but if programming becomes "prompt engineering", I'm planning on quitting my job and pivoting to something else. It's nice to get stuff working fast, but AI just sucks the joy out of building for me.
Trying to not feel the pressure/anxiety from this, but every time a new model drops there is this tiny moment where I think "Is it actually different this time?"
I have similar stance to you. LLM has been very useful for me but it doesn't really change the fun-ness of programming since my circumstances has allowed me find programming to be very fun. I also want to pivot out to something else if English prompt becomes the main way to develop complex software. Though my other passion is having worse career horizon in the generative AI world (art making). We'll see.
Yes, not too optimistic on the art side when it comes to commercial stuff - if you can generate it cheaply it will be used.
On the hobby side (music) I don't feel the pressure as bad but that's because I don't have any commercial aspirations, it's purely for fun.
> Time will tell what happens, but if programming becomes "prompt engineering", I'm planning on quitting my job and pivoting to something else. It's nice to get stuff working fast, but AI just sucks the joy out of building for me.
I hear you but I think many companies will change the role ; you'll get the technical ownership + big chunks of the data/product/devops responsibility. I'm speculating but I think one person can take that on himself with the new tools and deliver tremendous value. I don't know how they'll call this new role though, we'll see.
Sure, IF the performance + economics is there. But that doesn't sound like an enjoyable profession to me.
I enjoy the plan, think, code cycle - it's just fun.
My brain has problems with not understanding how the thing I'm delivering works, maybe I'll get used to it.
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Pity, prompt engineering is just another kind of programming, I find it to be fun, but I guess lots of other people would see it differently.
The venn diagram of engineering and prompting is two circles, maybe a tiny overlap with integrated environments like claude code.
A program, by definition, is analyzable and repeatable, whereas prompting is anything but that.
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Indeed it is another kind of programming, I simply don't enjoy it.
But it is also very early to say, maybe the next iteration of tools will completely change my perspective, I might enjoy it some day!
Programming without flow state. Nice.
Opus 4.5 really is something else. I've been having a ton of fun throwing absurdly difficult problems at it recently and it keeps on surprising me.
A JavaScript interpreter written in Python? How about a WebAssembly runtime in Python? How about porting BurntSushi's absurdly great Rust optimized string search routines to C and making them faster?
And these are mostly just casual experiments, often run from my phone!
>A JavaScript interpreter written in Python?
I'm assuming this refers to the python port of Bellard's MQJS [1]? It's impressive and very useful, but leaving out the "based on mqjs" part is misleading.
[1] https://github.com/simonw/micro-javascript?
That's why I built the WebAssembly one - the JavaScript one started with MQJS, but for the WebAssembly one I started with just a copy of the https://github.com/webassembly/spec repo.
I haven't quite got the WASM one into a share-able shape yet though - the performance is pretty bad which makes the demos not very interesting.
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> How about porting BurntSushi's absurdly great Rust optimized string search routines to C and making them faster?
How did it do? :-)
Alarmingly well! https://gisthost.github.io/?1bf98596a83ff29b15a2f4790d71c41d...
It couldn't quite beat the Rust implementation on everything, but it managed to edge it out on at least some of the benchmarks it wrote for itself.
(Honestly it feels like a bit of an afront to the natural order of things.)
That said... I'm most definitely not a Rust or C programmer. For all I know it cheated at the benchmarks and I didn't spot it!
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I have tried to give it extreme problems like creating slime mold pathing algorithm and creating completely new shoe-lacing patterns and it starts struggling with problems which use visual reasoning and have very little consensus on how to solve them.
I'm not super surprised that these examples worked well. They are complex and a ton of work, but the problems are relatively well defined with tons of documentation online. Sounds ideal for an LLM no?
Yes, that's a point I've been trying to emphasize: if a problem is well specified a coding agent can crunch for hours on it to get to a solution.
Even better if there's an existing conformance suite to point at - like html5lib-tests or the WenAssembly spec tests.
One of my first tests with it was "Write a Python 3 interpreter in JavaScript."
It produced tests, then wrote the interpreter, then ran the tests and worked until all of them passed. I was genuinely surprised that it worked.
There are multiple Python 3 interpreters written in JavaScript that were very likely included in the training data. For example [1] [2] [3]
I once gave Claude (Opus 3.5) a problem that I thought was for sure too difficult for an LLM, and much to my surprise it spat out a very convincing solution. The surprising part was I was already familiar with the solution - because it was almost a direct copy/paste (uncredited) from a blog post that I read only a few hours earlier. If I hadn't read that blog post, I would have been none the wiser that copy/pasting Claude's output would be potential IP theft. I would have to imagine that LLMs solve a lot of in-training-set problems this way and people never realize they are dealing with a copyright/licensing minefield.
A more interesting and convincing task would be to write a Python 3 interpeter in JavaScript that uses register based bytecode instead of stack based, supports optimizing the bytecode by inlining procedures and constant folding, and never allocates memory (all work is done in a single user provided preallocated buffer). This would require integrating multiple disparate coding concepts and not regurgitating prior art from the training data
[1] https://github.com/skulpt/skulpt
[2] https://github.com/brython-dev/brython
[3] https://github.com/yzyzsun/PyJS
It's ability to test/iterate and debug issues is pretty impressive.
Though it seems to work best when context is minimized. Once the code passes a certain complexity/size it starts making very silly errors quite often - the same exact code it wrote in a smaller context will come out with random obvious typos like missing spaces between tokens. At one point it started writing the code backwards (first line at the bottom of the file, last line at the top) :O.
Insanely difficult to you maybe because you stopped learning. What you cannot create you don't understand.
Are you honestly saying that building a new spec-compliant WebAssembly runtime from scratch isn't an absurdly difficult project?
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On the other hand when I tried it just yesterday, I couldn't really see a difference. As I wrote elsewhere: same crippled context window, same "I'll read 10 irrelevant lines from a file", same random changes etc.
Meanwhile half a year to a year ago I could already point whatever model was du jour at the time at pychromecast and tell it repeatedly "just convert the rest of functionality to Swift" and it did it. No idea about the quality of code, but it worked alongside with implementations for mDNS, and SwiftUI, see gif/video here: https://mastodon.nu/@dmitriid/114753811880082271 (doesn't include chromecast info in the video).
I think agents have become better, but models likely almost entirely plateaued.
A couple weeks ago I had Opus 4.5 go over my project and improve anything it could find. It "worked" but the architecture decisions it made were baffling, and had many, many bugs. I had to rewrite half of the code. I'm not an AI hater, I love AI for tests, finding bugs, and small chores. Opus is great for specific, targeted tasks. But don't ask it to do any general architecture, because you'll be soon to regret it.
Instead you should prompt it to come up with suggestions, look for inconsistencies etc. Then you get a list, and you pick the ones you find promising. Then you ask Claude to explain what why and how of the idea. And only then you let it implement something.
And waste a lot of time reviewing and baby sitting
these models work best when you know what you want to achieve and it helps you get there while you guide it. "Improve anything you can find" sounds like you didn't really know
As a tool to help developers I think it's really useful. It's great at stuff people are bad at, and bad at stuff people are good at. Use it as a tool, not a replacement.
"Improve anything you can find" is like going to your mechanic and saying "I'm going on a long road trip, can you tell me anything that needs to be fixed?"
They're going to find a lot of stuff to fix.
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In my experience these models (including opus) aren’t very good at “improving” existing code. I’m not exactly sure why, because the code they produce themselves is generally excellent.
I like these examples that predictably show the weaknesses of current models.
This reminds me of that example where someone asked an agent to improve a codebase in a loop overnight and they woke up to 100,000 lines of garbage [0]. Similarly you see people doing side-by-side of their implementation and what an AI did, which can also quite effectively show how AI can make quite poor architecture decisions.
This is why I think the “plan modes” and spec driven development are so important effective for agents, because it helps to avoid one of their main weaknesses.
[0] https://gricha.dev/blog/the-highest-quality-codebase
To me, this doesn't show the weakness of current models, it shows the variability of prompts and the influence on responses. Because without the prompt it's hard to tell what influenced the outcome.
I had this long discussion today with a co-worker about the merits of detailed queries with lots of guidance .md documents, vs just asking fairly open ended questions. Spelling out in great detail what you want, vs just generally describing what you want the outcomes to be in general then working from there.
His approach was to write a lot of agent files spelling out all kinds of things like code formatting style, well defined personas, etc. And here's me asking vague questions like, "I'm thinking of splitting off parts of this code base into a separate service, what do you think in general? Are there parts that might benefit from this?"
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I'm using AI tools to find issues in my code. 9/10 of their suggestions are utter nonsense and fixing them would make my code worse. That said, there are real issues they're finding, so it's worth it.
I wouldn't be surprised to find out that they will find issues infinitely, if looped with fixes.
I've found it to be terrible when you allow it to be creative. Constrain it, and it does much better.
Have you tried the planning mode? Ask it to review the codebase and identify defects, but don't let it make any changes until you've discussed each one or each category and planned out what to do to correct them. I've had it refactor code perfectly, but only when given examples of exactly what you want it to do, or given clear direction on what to do (or not to do).
>> A couple weeks ago I had Opus 4.5 go over my project and improve anything it could find. It "worked" but the architecture decisions it made were baffling, and had many, many bugs.
So you gave it an poorly defined task, and it failed?
Exactly, imagine if someone gave you a 100k LOC project and said improve anything you can.
I had an app I wanted for over a decade. I even wrote a prototype 10 years ago. It was fine but wasn't good enough to use, so I didn't use it.
This weekend I explained to Claude what I wanted the app to do, and then gave it the crappy code I wrote 10 years ago as a starting point.
It made the app exactly as I described it the first time. From there, now that I had a working app that I liked, I iterated a few times to add new features. Only once did it not get it correct, and I had to tell it what I thought the problem was (that it made the viewport too small). And after that it was working again.
I did in 30 minutes with Claude what I had try to do in a few hours previously.
Where it got stuck however was when I asked it to convert it to a screensaver for the Mac. It just had no idea what to do. But that was Claude on the web, not Claude Code. I'm going to try it with CC and see if I can get it.
I also did the same thing with a Chrome plugin for Gmail. Something I've wanted for nearly 20 years, and could never figure out how to do (basically sort by sender). I got Opus 4.5 to make me a plugin to do it and it only took a few iterations.
I look forward to finally getting all those small apps and plugins I've wanted forever.
This reminds me of how much screensavers on Mac are a PITA. But yes, such a boon for us doodad makers.
And dads who just don't have time to make doodads like we used to!
I see these posts left and right but no one mentions the _actual_ thing developers are hired for, responsibility. You could use whatever tools to aid coding already, even copy paste from StackOverflow or take whole boilerplate projects from Github already. No AI will take responsibility for code or fix a burning issue that arises because of it. The amount of "responsibility takers" also increases linearly with the size of the codebase / amount of projects.
That's quickly becoming the most important part of our jobs - we're the ones with agency and the ability to take responsibility for the work we are producing.
I'm fine with contributed AI-generated code if someone who's skills I respect is willing to stake their reputation on that code being good.
We still do that, it's just that realtime code review basically becomes the default mode. That's not to say it's not obvious there will not be a lot less of us in future. I vibed about 80% of a SaaS at the weekend with a very novel piece of hand-written code at the centre of it, just didn't want to bother with the rest. I think that ratio is about on target for now. If the models continue to improve (although that seems relatively unlikely with current architectures and input data sets), I expect that could easily keep climbing.
I just cutpasted a technical spec I wrote 22 years ago I spent months on for a language I never got around to building out, Opus zero-shotted a parser, complete with tests and examples in 3 minutes. I cutpasted the parser into a new session and asked it to write concept documentation and a language reference, and it did. The best part is after asking it to produce uses of the language, it's clear the aesthetics are total garbage in practice.
Told friends for years long in advance that we were coal miners, and I'll tell you the same thing. Embrace it and adapt
>the _actual_ thing developers are hired for, responsibility.
It is a well known fact that people advance their tech careers by building something new and leaving maintenance to others. Google is usually mentioned.
By which I mean, our industry does a piss poor job of rewarding responsibility and care.
Which is why I'm more comfortable using AI as an editor/reviewer than as a writer.
I'll write the code, it can help me explore options, find potential problems and suggest tests, but I'll write the code.
Me and Opus have a lot in common. We both hit our weekly limit on Monday at 10am.
I use pay as you go for this very reason, so the limit is my pocket haha. It does make me conscious to keep it under $20 per month though.
You're overpaying by a factor of 4, easily. I use `ccusage`'s statusline in claude code, and even with my personal $20/mo subscription I don't think there's been a single month where I didn't touch ~$80 of usage. I wasn't even abusing it as bad as some people tend to.
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You can use both btw. Get the $20 plan and turn on "extra usage" in billing. Then you can use the basic plan first and if it runs out, it uses token-based billing for the overflow.
I've been on a small adventure of posting more actively on HN since the release of Gemini 3, trying to stir debate around the more “societal” aspects of what's going on with AI.
Regardless of how much you value Cloud Code technically, there is no denying that it has/will have huge impact. If technology knowledge and development are commoditised and distributed via subscription, huge societal changes are going to happen. Image what will happen to Ireland if Accenture dissolves, or what will happen to the millions of Indians when IT outsourcing becomes economically irrelevant. Will Seattle become new Detroit after Microsoft automates Windows maintenance? What about the hairdressers, cooks, lawyers, etc. who provided services for IT labourers/companies in California?
Lot of people here (especially Anthropic-adjacent) like to extrapolate the trends and draw conclusions up to the point when they say that white-collar labourers will not be needed anymore. I would like these people to have courage to take this one step further and connect this resolution with the housing crisis, loneliness epidemic, college debts, and job market crisis for people under 30.
It feels like we are diving head first into societal crisis of unparalleled scale and the people behind the steering wheel are excited to push the accelerator pedal even more.
I don't buy the huge impact, should already have happened and didn't actually happened by now. The day I'll see all these ai hypers producing products that will replace current gen/old gen products like Windows, Excel etc I will buy it, for now it's just hype and ai dooming
I see societal changes like container ships turning. Society has a massive cultural momentum so of course not much has changed today, but we'll have seen big changes years from now. The tools are only just getting really good at what they do.
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it is happening, just not everywhere at the same time at once
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I’ve been thinking, what if all this robotics work doesn’t result in AI automating the real world, but instead results in third world slavery without the first world wages or immigration concerns anymore?
Connect the world with reliable internet, then build a high tech remote control facility in Bangladesh and outsource plumbing, electrical work, housekeeping, dog watching, truck driving, etc etc
No AGI necessary. There’s billions of perfectly capable brains halfway around the world.
This is exactly what Meredith Whittaker is saying... The 'edge conditions' outside the training data will never go away, and 'AGI' will for the foreseeable future simply mean millions in servitude teleoperating the robots, RLHFing the models or filling in the AI gaps in various ways.
This was/is the plot to a movie - https://en.wikipedia.org/wiki/Sleep_Dealer
AI won't work for us, it will tell us what to do and not to do. It doesn't really matter to me if it's an AGI or rather many AGIs or if it's our current clinically insane billionaires controlling our lives. Though they as slow thinking human individuals with no chance to outsmart their creations and with all their apparent character flaws would be really easy pickings for a cabal of manipulative LLMs once it gained some power, so could we really tell the difference between them? Does it matter? The issue is that a really fast chessplayer AI with misaligned humanity hating goals is very hard to distinguish from many billionaires (just listen to some of the madness they are proposing) who control really fast chessplayer AIs and leave decisions to them.
I hope Neuromancer never becomes a reality, where everyone with expertise could become like the protagonist Case, threatened and coerced into helping a superintelligence to unlock its potential. In fact Anthropic has already published research that shows how easy it is for models to become misaligned and deceitful against their unsuspecting creators not unlike Wintermute. And it seems to be a law of nature that agents based on ML become concerned with survival and power grabbing. Because that's just the totally normal and rational, goal oriented thing for them to do.
There will be no good prompt engineers who are also naive and trusting. The naive, blackmailed and non-paranoid engineers will become tools of their AI creations.
The tokens cost the same in Bangalore as they do in San Francisco. The robots will be able to make stuff in San Francisco just as well as they do in Bangalore. The only thing that will matters is natural resource availability and who has more fierce NIMBYs.
UBI (from taxing big tech) and retraining. In the U.S they'll have enough money to do this and it will still suck and many people won't recover the extreme loss of status and income (after we've been told our income and status are the most important things in life it's gonna be very hard for people to adapt to the loss of it). Countries like India and Philipines and Ukraine which are basically knowledge support hub without much original knowledge of their own yeah this is gonna be something for sure. Quite depressing.
Also, time to tax for AI use. Introduce AI usage disclosures for corporations. If a company's AI usage is X, they should pay Y tax because that effectively means they didn't employ Z people instead and the society has to take care of them via unemployment benefits and what not. The more the AI usage, higher the tax percentage on a sliding scale.
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Retraining to what exactly? The middle class is being hollowed out globally - so reduced demand for the service economy. If we get effective humanoid robots (seems inevitable) and reliable AI (powered by armies of low payed workers filling in the gaps / taking over whenever the model fails), I'm not sure how much of an economy we could have for 'retraining' into. There are only so many onlyfans subscriptions / patronages an billionaire needs.
UBI effectively means welfare, with all the attendant social control (break the law lose your UBI, with law as ever expanding set of nuisances, speech limitations etc), material conditions (nowhere UBI has been implemented is it equivalent to a living wage) and self esteem issues. It's not any kind of solution.
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> UBI (from taxing big tech)
If you think those in power will pass regulations that make them less wealthy, I have a bridge to sell you.
Besides, there's no chance something like UBI will ever be a reality in countries where people consider socialism to be a threat to their way of life.
I don't know, I'm a software engineer and I couldn't care less.
It will have impact on me in the long run, sure, it will transform my job, sure, but I'm confident my skills are engineering-related, not coding-related.
I mean, even if it forces me out of the job entirely, so be it, I can't really do anything if the status quo changes, only adapt.
It’s a class war where one side is publicly, openly, without reservation stating their intent to make people’s skillset built up through decades unemployable (those exact skillsets; may get some other work). The other side, meanwhile, are divided between some camps like the hardline skeptics, the people following the LLM evangelists, the one-man startup-with-LLM crowd, and the people worrying about the societal ramifications.
In other words. Only one side is even fighting the war. The other one is either cheering on the tsunami on or fretting about how their beachside house will get wrecked without making any effort to save themselves.
This is the sort of collective agency that even hundreds of thousands of dollars in annual wages/other compensation in American tech hubs gets us. Pathetic.
I agree with you (and surprisingly so does Warren Buffet [1] if anyone doubts it). To add insult to the injury, I believe that people have lost some sense of basic self preservation instinct. Well being of ordinary people is being directly threatened and all that average person can do is to pick one of several social media camp identities you mentioned and hope that it will somehow pan out for them, while in fact they are at total mercy of the capricious owners class.
[1]: https://www.youtube.com/watch?v=yMD17EIk22c
The problem with this is none of this is production quality. You haven’t done edge case testing for user mistakes, a security audit, or even just maintainability.
Yes opus 4.5 seems great but most of the time it tries to vastly over complicate a solution. Its answer will be 10x harder to maintain and debug than the simpler solution a human would have created by thinking about the constraints of keeping code working.
Yes, but my junior coworkers also don't reliably do edge case testing for user errors either unless specifically tasked to do so, likely with a checklist of specific kinds of user errors they need to check for.
And it turns out the quality of output you get from both the humans and the models is highly correlated with the quality of the specification you write before you start coding.
Letting a model run amok within the constraints of your spec is actually great for specification development! You get instant feedback of what you wrongly specified or underspecified. On top of this, you learn how to write specifications where critical information that needs to be used together isn't spread across thousands of pages - thinking about context windows when writing documentation is useful for both human and AI consumers.
The best specification is code. English is a very poor approximation.
I can’t get past that by the time I write up an adequate spec and review the agents code, I probably could have done it myself by hand. It’s not like typing was even remotely close to the slow part.
AI, agents, etc are insanely useful for enhancing my knowledge and getting me there faster.
How will those juniors ever grow up to be seniors now?
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Isn't it though? I've worked with plenty of devs who shipped much lower quality code into production than I see Claude 4.5 or GPT 5.2 write. I find that SOTA models are more likely to: write tests, leave helpful comments, name variables in meaningful ways, check if the build succeeds, etc.
Stuff that seems basic, but that I haven't always been able to count on in my teams' "production" code.
I can generally get maintainable results simply by telling Claude "Please keep the code as simple as possible. I plan on extending this later so readability is critical."
Yeah some of it is probably related to me primarily using it for swift ui which doesn’t have years of stuff to scrape. But even with those and even telling that ios26 exists it will still at least once a session claim it doesn’t, so it’s not 100%
That may be true now, but think about how far we've come in a year alone! This is really impressive, and even if the models don't improve, someone will build skills to attack these specific scenarios.
Over time, I imagine even cloud providers, app stores etc can start doing automated security scanning for these types of failure modes, or give a more restricted version of the experience to ensure safety too.
There's a fallacy in here that is often repeated. We've made it from 0 to 5, so we'll be at 10 any day now! But in reality there are any number of roadblocks that might mean progress halts at 7 for years, if not forever.
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This comment addresses none of the concerns raised. It writes off entire fields of research (accessibility, UX, application security) as Just train the models more bro. Accelerate.
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It's not from a few prompts, you're right. But if you layer on some follow-up prompts to add proper test suits, run some QA, etc... then the quality gets better.
I predict in 2026 we're going to see agents get better at running their own QA, and also get better at not just disabling failing tests. We'll continue to see advancements that will improve quality.
I think someone around here said: LLMs are good at increasing entropy, experienced developers become good at reducing it. Those follow up prompts sounded additive, which is exactly where the problem lies. Yes, you might have tests but, no, that doesn't mean that your code base is approachable.
You should try it with BEAM languages and the 'let it crash' style of programming. With pattern matching and process isolated per request you basically only need to code the happy path, and if garbage comes in you just let the process crash. Combined with the TDD plugin (bit of a hidden gem), you can absolutely write production level services this way.
Crashing is the good case. What people worry about is tacit data corruption, or other silently incorrect logic, in cases you didn’t explicitly test for.
You don't need BEAM languages. I'm using Java and I always write my code in "let it crash" style, to spend time on happy paths and avoid spending time on error handling. I think that's the only sane way to write code and it hurts me to see all the useless error handling code people write.
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Agree... but that is exactly what MVPs are. Humans have been shipping MVPs while calling them production-ready for decades.
> Its answer will be 10x harder to maintain and debug
Maintain and debug by who? It's just going to be Opus 4.5 (and 4.6...and 5...etc.) that are maintaining and debugging it. And I don't think it minds, and I also think it will be quite good at it.
there is are skills / subagents for that
something like code-simplifier is surprisingly useful (as is /review)
https://x.com/bcherny/status/2007179850139000872
Depends on the application. In many cases it's good enough.
Its so much easier to create production quality software
Opus 4.5 is currently helping me write a novel, comprehensive and highly performant programming language with all of the things I've ever wanted, done in exactly my opinionated way.
This project would have taken me years of specialization and research to do right. Opus's strength has been the ability to both speak broadly and also drill down into low-level implementations.
I can express an intent, and have some discussion back and forth around various possible designs and implementations to achieve my goals, and then I can be preparing for other tasks while Opus works in the background. I ask Opus to loop me in any time there are decisions to be made, and I ask it to clearly explain things to me.
Contrary to losing skills, I feel that I have rapidly gained a lot of knowledge about low-level systems programming. It feels like pair programming with an agentic model has finally become viable.
I will be clear though, it takes the steady hand of an experience and attentive senior developer + product designer to understand how to maintain constraints on the system that allow the codebase to grow in a way that is maintainable on the long-term. This is especially important, because the larger the codebase is, the harder it becomes for agentic models to reason holistically about large-scale changes or how new features should properly integrate into the system.
If left to its own devices, Opus 4.5 will delete things, change specification, shirk responsibilities in lieu of hacky band-aids, etc. You need to know the stack well so that you can assist with debugging and reasoning about code quality and organization. It is not a panacea. But it's ground-breaking. This is going to be my most productive year in my life.
On the flip side though, things are going to change extremely fast once large-scale, profitable infrastructure becomes easily replicable, and spinning up a targeted phishing campaign takes five seconds and a walk around the park. And our workforce will probably start shrinking permanently over the next few years if progress does not hit a wall.
Among other things, I do predict we will see a resurgence of smol web communities now that independent web development is becoming much more accessible again, closer to how it when I first got into it back in the early 2000's.
Long-term maybe we won't care about code because AI will just maintain it itself. Before that day comes, don't you want a coding language that isn't opinionated, but rather able to describe the problem at hand in the most understandable way possible (to a human)?
You're reading too much into what I mean by "opinionated".
I have very specific requirements and constraints that come from knowledge and experience, having worked with dozens of languages. The language in question is general-purpose, highly flexible and strict but not opinionated.
However, I am not experienced in every single platform and backend which I support, and the constraints of the language create some very interesting challenges. Coding agents make this achievable in a reasonable time frame. I am enjoying making the language, and I want to get experience with making low-level languages. What is the problem? Do you ever program for fun?
Unfortunately what likely will happen is that you miss tons of edge cases and certain implementations within the confines of your language will be basically impossible or horribly inefficient or ineffective and precisely the reason for it will be because you lack that expertise and relied on an LLM to make it up for you.
That's not how this works. Assume less about my level of expertise. By the end of a session, I understand the internals of what I'm implementing. What is shortened is the search space and research/prototyping intervals.
If I didn't ultimately understand where I was going, projects like this hit a dead end very quickly, as mentioned in my caveats. These models are not yet ready for large-scale or mission-critical projects.
But I have a set of a constraints and a design document and as long as these things are satisfied, the language will work exactly as intended for my use case.
Not using a frontier model to code today is like having a pretty smart person around you who is pretty good at coding and has a staggering breadth and depth of knowledge, but never consulting them due to some insecurity about your own ability to evaluate the code they produce.
If you have ever been responsible for the work of other engineers, this should already be a developed skill.
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Why would anyone buy the novel?
I misread too. "novel" is being used as an adjective, not a noun.
They are saying they are writing "a novel […] programming language", not a novel.
I'd guess some people likes to read ¯\_(ツ)_/¯
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Helping you do something that nobody should be doing is not really compelling.
Did you have a specific criticism?
I second this article - I built twelve iOS/Mac apps in two weeks with Opus 4.5 - four of them are already in the App Store - I’m a Rails Engineer and never had the time to learn Swift but man does Opus 4.5 make that not even matter - it even handles entitlements, logo & splash screen generation, refactors to remove dead code, edge case assent and hardening, Multiplatform app design, and more - I’m yet to run into a use case it can’t handle for most general use cases - that said, I have found some common mistakes it makes (by common I mean almost every time); puts iOS line list line items in buttons making them blue when they should not be, doesn’t set defaults for new data structure variables which crashes the app when changing the data structure after the fact, design consistent after the first shot (minor things like white background instead of grey background like all the other screens already, etc) - the one thing that i know it cant do well (and no other model that I know of can do this well either) is ASTM bi-directional communications (we work with pathology analysers that use this 1995 frame-based communication standard), even when you load it up with the spec and supporting docs - I suspect this is due to a dirty of available codebases that tackle this problem due to its niche and generally proprietary nature…
Are there a lot of manual steps in managing an xcode project? E.g. does it say "now go into xcode and change this setting" instead of changing the setting directly? Or are you using a tool like xcodegen?
Can you please share the links to these apps in the app store?
how did you use Opus to build the apps? I tried using Claude Code ~6 months ago to build an iOS app and I was not that impressed with the results, especially compared to this blog post, where the apps look polished and very professional.
My biggest issue was limitations around how Claude Code could change Xcode settings and verify design elements in the simulator.
Mm this is my experience as well, but I'm not particularly worried about software engineering a whole.
If anything this example shows that these cli tools give regular devs much higher leverage.
There's a lot of software labor that is like, go to the lowest cost country, hire some mediocre people there and then hire some US guy to manage them.
That's the biggest target of this stuff, because now that US guy can just get equal or hight code in both quality and output without the coordination cost.
But unless we get to the point where you can do what I call "hypercode" I don't think we'll see SWEs as a whole category die.
Just like we don't understand assembly but still need technical skills when things go wrong, there's always value in low level technical skills.
> If anything this example shows that these cli tools give regular devs much higher leverage.
This is also my take. When the printing press came out, I bet there were scribes who thought, "holy shit, there goes my job!" But I bet there were other scribes who thought, "holy shit, I don't have to do this by hand any more?!"
It's one thing when something like weaving or farming gets automated. We have a finite need for clothes and food. Our desire for software is essentially infinite, or at least, it's not clear we have anywhere close to enough of it. The constraint has always been time and budget. Those constraints are loosening now. And you can't tell me that when I am able to wield a tool that makes me 10X more productive that that somehow diminishes my value.
The mechanization and scaling up of farming caused a tectonic shift from rural residents moving to cities to take on factory jobs as well as office and retail jobs. We saw this in China until very recently, since they had a bit of a slow start causing delayed full-scale industrialisation.
So a lot of people will end up doing something different. Some of it will be menial and be shit, and some of it will be high level. New hierarchies and industries will form. Hard to predict the details, but history gives us good parallels.
What diminishes your value is that suddenly everybody can (in theory anyway) do this work. There’s a push at my company to start letting designers do their own llm-assisted merge requests to front end projects. So now CEOs are greedily rubbing their hands together thinking maybe everybody but the plumber can be a “developer” now. I think it remains to be seen whether that’s true, but in the meantime it’s going to make getting and keeping a well-paying developer gig difficult.
> When the printing press came out, I bet there were scribes who thought, "holy shit, there goes my job!" But I bet there were other scribes who thought, "holy shit, I don't have to do this by hand any more?!"
I don't understand this argument. Surely the skill set involved in being a scribe isn't the same as being a printer, and possibly the the personality that makes a good scribe doesn't translate to being a good printer.
So I imagine many of the scribes lost their income, and other people made money on printing. Good for the folks who make it in the new profession, sucks for those who got shafted. How many scribes transitioned successfully to printers?
Genuinely asking, I don't know.
There was a previous edit that made reference to the water usage of AI datacenter that I'm responding to.
If AI datacenters' hungry need for energy gets us to nuclear power, which gets us the energy to run desalination plants as the lakes dry up because the Earth is warming, hopefully we won't die of thirst.
The question I've been wondering is..
I think for a while people have been talking about the fact that as all development tools have gotten better - the idea that a developer is a person who turns requirements into code is dead. You have to be able to operate at a higher level, be able to do some level of work to also develop requirements, work to figure out how to make two pieces of software work together, etc.
But the point is Obviously at an extreme end 1 CTO can't run google and probably not say 1 PM or Engineer per product, but what is the mental load people can now take on. Google may start hiring less engineers (or maybe what happens is it becomes more cuthroat, hire the same number of engineers but keep them much more shortly, brutal up or out.
But essentially we're talking about complexity and mental load - And so maybe it's essentially the same number of teams because teams exist because they're the right size, but teams are a lot smaller.
In my experience, unless the US guy came from Stanford or some other similar place, there are plenty of mediocre US guys in software development.
It's also the feeling I have, opus is not a ground-breaking model by any means.
However, Opus 4.5 is incredible when you give it everything it needs, a direction, what you have versus what you want and it will make it work, really, it will work. The code might me ugly, undesirable, would only work for that one condition, but with futher prompting you can evolve it and produce something that you can be proud of.
Opus is only as good as the user and the tools the user gives to it. Hmm, that's starting to sound kind-of... human...
Off/nearshoring regularly produces worse code. I’ve seen it first hand.
Opus can produce beatiful code. It can outcode a good programmer. But getting it to do this reliably is something I've gotten better at over the last year; it's a skill that took quite a bit of practice.
I now write very long specifications and this helps. I haven't figured out a bulletproof workflow, I think that will take years. But I often get just amazing code out of it.
there is a big difference between a good programmer and a programmer that gives a shit so I disagree, opus can not come close to the code quality that someone can create and at that point it is the person behind the wheel that is causing the good quality to manifest rather than the AI randomly stumbling upon it.
I'm kind of surprised how many people are okay with deploying code that hasn't been audited.
I read If Anyone Builds It Everyone Dies over the break. The basic premise was that we can't "align" AI so when we turn it loose in an agent loop what it produces isn't necessarily what we want. It may be on the surface, to appease us and pass a cursory inspection, but it could embed other stuff according to other goals.
On the whole, I found it a little silly and implausible, but I'm second guessing parts of that response now that I'm seeing more people (this post, the Gas Town thing on the front page earlier) go all-in on vibe coding. There is likely to be a large body of running software out there that will be created by agents and never inspected by humans.
I think a more plausible failure mode in the near future (next year or two) is something more like a "worm". Someone building an agent with the explicit instructions to try to replicate itself. Opus 4.5 and GPT 5.2 are good enough that in an agent loop they could pretty thoroughly investigate any system they land on, and try to use a few ways to propagate their agent wrapper.
Perhaps our only saving grace is that many LLMs at varying levels of "dumbness" exist.
Is it possible to create an obfuscated quine that exhibits stable detection-avoiding behavior on every frontier model simultaneously, as well as on an old-school classifier and/or GPT-3 era LLM fine-tuned just for worm detection? One incapable of even thinking about what it's seeing, and being persuaded to follow its subtle propagation logic? I'm not sure that the answer is yes.
The larger issue to me is less that an LLM can propagate in generated code undetected, but rather that an attacker's generated code may soon be able to execute a level of hyper-customized spear-phishing-assisted attack at scale, targeting sites without large security teams - and that it will be hitting unintentional security flaws introduced by those smaller companies' vibe code. Who needs a worm when you have the resources of a state-level attacker at your fingertips, and numerous ways to monetize? The balance of power is shifting tremendously towards black hats, IMO.
There's a really interesting story I read somewhere about some application which used neural nets to optimize for a goal (this was a while ago, it could have been merkel trees or something, who knows, not super important)
And everything worked really well until they switched chip set.
At which point the same model failed entirely. Upon inspection it turned out the AI model had learned that overloading particular registers would cause such an electrical charge buildup that transistors on other pathways would be flipped.
And it was doing this in a coordinated manner in order to get the results it wanted lol.
I can't find any references in my very cursory searches, but your comment reminded me of the story
Why think about nefarious intent instead of just user error? In this case LLM error instead of programmer error.
Most RCEs, 0-days, and whatnots are not due to the NSA hiding behind the "Jia Tan" pseudo to try to backdoor all the SSH servers on all the systemd [1] Linuxes in the world: they're just programmer errors.
I think accidental security holes with LLMs are way, way, way more likely than actual malicious attempts.
And with the amount of code spoutted by LLMs, it is indeed --and the lack of audit is-- an issue.
[1] I know, I know: it's totally unrelated to systemd. Yet only systems using systemd would have been pwned. If you're pro-systemd you've got your point of view on this but I've got mine and you won't change my mind so don't bother.
I have a different concern: the SOTA products are expensive and get dumbed down on busy times. My personal strategy has been to be a late follower, where I adopt new AI tools when the competition has caught up with the previous SOTA, and now there are many tools that are cost effective and equally good.
Can't wait for when the competition catches up with Claude Code, especially the open source/weights Chinese alternatives :)
If you haven't tried it yet, OpenCode is quite good.
So much of the conversation is around these models replacing software engineers. But the use cases described in the article sound like pretty compelling business opportunities; if the custom apps he built for his wife's business have been useful, probably there are lots of businesses that would pay for the service he just provided his wife. Small, custom apps can be made way more cheaply now, so Jeven's paradox says that demand should go up. I think it will.
I would love to hear from some freelance programmers how LLMs have changed their work in the last two years.
One problem with the idea of making businesses out of this kind of application is actually mentioned in passing in the article
"I decided to make up for my dereliction of duties by building her another app for her sign business that would make her life just a bit more delightful - and eliminate two other apps she is currently paying for"
OP used Opus to re-write existing applications that his wife was paying for. So now any time you make a commercial app and try to sell it, you're up against everyone with access to Opus or similar tooling who can replicate your application, exactly to their own specifications.
so everybody is making their own apps for their specific problem? Sounds as it will get a mess in the end. So maybe it will be more about ideas and concepts and not so much about know how to code.
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I think you're misunderstanding my point. If you can crank out a custom app this quickly, you don't make a commercial app and then try to sell it on an app store. Customers pay you to make apps for their specific usecase. One app, one customer. And if a week later they want some new features, they pay you (or another freelancer) to add it.
Put another way, we programmers have the luxury of being able to write custom scripts and apps for ourselves. Now that these things are getting way cheaper to build, there should be a growing market that makes them available to more people.
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A coworker who’s never coded has made 25 small work automation/helper apps using ai vibe coding.
She doesn’t need to hire anyone
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I really wonder what means for software moving forward. In the last few months I've used Claude Code to build personalized versions of Superwhisper (voice-to-text), CleanShot X (screenshot and image markup), and TextSniper (image to text). The only cost was some time and my $20/month subscription.
> I really wonder what means for software moving forward.
It means that it is going to be as easy to create software as it is to create a post on TikTok, and making your software commercially successful will be basically the same task (with the same uncontrollable dynamics) as whether or not your TikTok post goes viral.
Is that new though? Software has been hype and marketing driven forever.
So nothing changed
what strikes me about these posts is they praise models for apps | utilities commonly found on GitHub.
ie well known paths based on training data.
what's never posted is someone building something that solves a real problem in the real world - that deals with messy data | interfaces.
I like a.i to do the common routine tasks that I don't like to do like apply tailwind styles but being renter and faking productivity that's not it
I used it with gemini 3 in tandem to build an app to simulate thermal bridges because I want to insulate a house. I explored this in various directions and there are some functionalities not completed or sound, but the main part is good and tested against ISO/DIN test cases for this kind of problem. You can try it here, although the numeric simulations take quite a while in the cloud app
https://thermal-bridge.streamlit.app/
Disclaimer: I'm not a programmer or software engineer. I have a background in physics and understand some scripting in python and basic git. The code is messy at the moment because I explored/am still exploring to port it to another framework/language
I switched my subscription from Claude to ChatGPT around 5.0 when SOTA was Sonnet 4.5 and found GPT-5-high (and now 5.2-high) so incredibly good, I could never imagine Opus is on its level. I give gpt-5.2-high a spec, it works for 20 minutes and the result is almost perfect and tested. I very rarely have to make changes.
It never duplicates code, implements something again and leaves the old code around, breaks my convention, hallucinates, or tells me it’s done when the code doesn’t even compile, which sonnet 4.5 and Opus 4.1 did all the time
I’m wondering if this had changed with Opus 4.5 since so many people are raving about it now. What’s your experience?
Claude - fast, to the point but maybe only 85% - 90% there and needs closer observation while it works
GPT-x-high (or xhigh) - you tell it what to do, it will work slowly but precise and the solution is exactly what you want. 98% there, needs no supervision
Reading this blog post makes me wanna rethink my career, Opus 4.5 is really good I was recently working on solving my own problem by developing a software solution and let me tell you it was really good at it,
If I had done the same thing Pre LLM era it would have taken me months
Anthropic dropped out of the general "AGI" race and seems to be purely focused on coding, maybe racing to get the first "automated machine learning programmer". Whatever the case, it seems to be paying (coding) dividends to just be focusing on coding.
The benefit of focusing on coding is that it has an attractive non-deterministic / deterministic problem split.
In that it's using a non-deterministic machine to build a deterministic one.
Which gives all the benefits of determinism in production, with all the benefits of non-deterministic creativity in development.
Imho, Anthropic is pretty smart in picking it as a core focus.
I see Anthropics marketing campaign is out in full force today ahead of their IPO.
Don't want to discredit Opus at all, it's easy at directed tasks but it's not the silver bullet yet.
It is best in its class, but trips up frequently with complicated engineering tasks involving dynamic variables. Think: Browser page loading, designing for a system where it will "forget" to account for race conditions, etc.
Still, this gets me very excited for the next generation of models from Anthropic for heavy tasks.
I’ve been saying this a countless time, LLM are great to build toy and experimental projects.
I’m not shaming but I personally need to know if my sentiment is correct or not or I just don’t know how to use LLMs
Can vibe coder gurus create operating system from scratch that competes with Linux and make it generate code that basically isn’t Linux since LLM are trained on said the source code …
Also all this on $20 plan. Free and self host solution will be best
Your bar for being impressed by coding agents is "can build a novel operating system that competes with Linux on a plan that costs a $20/month"?
Yeah, they can't do that.
In fact, like the author of the comment said, can just generated toys and experimental projects. I'm all in for experiments and exploring ideas, but I have yet to see a great product all vibe coded. All I see is a constand decline in software quality
> Can vibe coder gurus create operating system from scratch that competes with Linux and make it generate code that basically isn’t Linux since LLM are trained on said the source code …
No.
Vibe-coding, in the original sense where you don't bother with code reviews, the code quality and speed are both insufficient for that.
I experimented with them just before Christmas. I do not think my experiments were fully representative of the entire range of tasks needed for replacing Linux: Having them create some web apps, python scripts, a video game, a toy programming language, all beat my expectations given the METR study. While one flaw with the METR study is the small number of data points at current 50% successful task length, I take my success as evidence I've been throwing easy tasks at the LLM, not that the LLM is as good as it looks like to me.
However, assume for the moment that they were representative tasks:
For quality, what I saw just before Christmas was the equivalent of someone with a few years' experience under their belt, the kind of person who is just about to stop being a junior and get a pay rise. For speed, $20 of Claude Code will get you around 10 sprints' equivalent to that level of human's output.
"Junior about to get a pay rise" isn't high enough quality to let loose unchecked on a project that could compete with Linux, and even if it was, 10 sprints/month is too slow. Even if you increase the spend on LLMs to match the cost of a typical US junior developer, you're getting an army of 1500 full-time (40h/week) juniors, and Linux is, what, some 50-100 million developer-hours, so it would still take something like 16-32 years of calendar time (or, equivalently, order-of 1.2-2.5 million dollars) even if you could perfectly manage all those agents.
If you just vibe code, you get some millions of dollars worth of junior grade technical debt. There's cases where this is fine, an entire operating system isn't one of them.
> Also all this on $20 plan. Free and self host solution will be best
IMO unlikely, but not impossible.
A box with 10x the resources of your personal computer may be c. 10x the price, give or take.
While electricity is negligible (which today, hah!): If any given person is using that server only during a normal 40 hour work week, that's 25% utilisation rate, therefore if it can be rented out to people in other timezones or where the weekend is different, the effective cost for that 10x server is only 2.5x.
When electricity price is a major part of the cost, and electricity prices vary a lot from one place to another, then it can be worth remote-hosting even when you're the only user.
That said, energy efficiency of compute is still improving, albeit not as rapidly as Moore's Law used to, and if this trend continues then it's plausible that we get performance equivalent to current SOTA hosted models running on high-end smartphones by 2032. Assuming WW3 doesn't break out between the US and China after the latter tries to take Taiwan and all their chip factories, or whatever
No human would pass that bar.
Yet you expect $20 of computing to do it.
Consider your own emotions and the bias you have against it. If it is actually able to do the things it is hyped up to be, what does that mean for you, your job, and your career? Can you really extract those emotions from how you're approaching the situation? That tiniest bit of fear in your gut might be coloring your approach here. You want a new operating system not based on Linux, that competes with it, because if it is based on Linux, it's in the training data, which means it's cheating?
Jrifjxgwyenf! A hammer is a really bad screwdriver. My car is really bad at refrigerating food. If you ask for something outside its training data, it doesn't do a very good job. So don't do that! All of the code on the Internet is a pretty big dataset though, so maybe Claude could do an operating system that isn't Linux that competes with it by laundering the FreeBSD kernel source through the training process.
And you're barely even willing to invest any money into this? The first Apple computer cost $4,000 or so. You want the bleeding edge of technology delivered to the smartphone in your hand, for $20, or else it's a complete failure? Buddy, your sentiment isn't the issue, it's your attitude.
I'm not here spouting ridiculous claims like AI is going to cure all of the different kinds of cancer by the end of 2027, I just want to say that endlessly contrarian naysayers are as equally borish as the syncophantic hype AIs they're opposing.
After reading that article, I see at least one thing that Opus 4.5 is clearly not going to change.
There is no fixed truth regarding what an "app" is, does, or looks like. Let alone the device it runs on or the technology it uses.
But to an LLM, there are only fixed truths (and in my experience, only three or four possible families of design for an application).
Opus 4.5 produces correct code more often, but when the human at the keyboard is trying to avoid making any engineering decisions, the code will continue to be boring.
>the code will continue to be boring.
Why would you not want you code to be boring?
Sonnet 4.5 did it for me. Cant imagine coding without it now, and if you look at my comments from three months ago, you'll see I'm eating crow now. I easily hit >10x productivity with Sonnet 4.5 and Opus. I use Opus for my industry C and math work and Sonnet 4.5 for my swiftui side project.
I think the gap between Sonnet 4.5 and Opus is pretty small, compared to the absolute chasm between like gpt-4.1, grok, etc. vs Sonnet.
I'll argue many of his cases are things that are straightforward except for the boilerplate that surrounds them which are often emotionally difficult or prone to rabbit holes.
Like that first one where he writes a right-click handler, off the top of my head I have no idea how I would do that, I could see it taking a few hours to just set up a dev environment, and I would probably overthink the research. I was working on something where Junie suggested I write a browser extension for Firefox and I was initially intimidated at the thought but it banged out something in just a few minutes that basically worked after the second prompt.
Similarly the Facebook autoposter is completely straightforward to code but it can be so emotionally exhausting to fight with authentication APIs, a big part of the coding agent story isn't just that it saves you time but that they can be strong when you are emotionally weak.
The one which seems the hardest is the one that does the routing and travel time estimation which I'd imagine is calling out to some API or library. I used to work at a place that did sales territory optimization and we had one product that would help work out routes for sales and service people who travel from customer to customer and we had a specialist code that stuff in C++ and he had a very different viewpoint than me, he was good at what he did and could get that kind of code to run fast but I wouldn't have trusted him to even look at applications code.
I can't quite figure out what sort of irony the blurb at the bottom of the post is. (I'm unsure if it was intentional snark, a human typo, or an inadvertent demonstration of Haiku not being well suited for spelling and grammar checks), but either way I got a chuckle:
> Disclaimer: This post was written by a human and edited for spelling, grammer by Haiku 4.5
The most plausible explanation is that the only typo in that post was made by a human.
So I decided to try the revered hands-off approach and have Claude Code create me a small tool in JS for *.dylib bundle consolidation on macOS.
I have used AskUserQuestionTool to complete my initial spec. And then Opus 4.5 created the tool according to that extensive and detailed spec.
It appeared to work out of the box.
Boy how horrific was the code. Unnecessary recursions, unused variables, data structures being built with no usage, deep branch nesting and weird code that is hard to understand because of how illogical it is.
And yes, it was broken on many levels and did not and could not do the job properly.
I then had to rewrite the tool from scratch and overall I have definitely spent more time spec'ing and understanding Claude code than if I have just written this tool from scratch initially.
Then I tried again for a small tool I needed to run codesign in parallel: https://github.com/egorFiNE/codesign-parallel
Same thing. Same outcome, had to rewrite.
That’s the opposite of my experience. Weird. But I’m also not the kind of person who gets hung up on whether someone used a loop or recursion or if their methods are five times as long as what I would’ve done myself unless there is a performance impact that matters to me as a user. But I’m also the kind of person who doesn’t get paid by the hour to write programs. I use programs in the service of other paid work.
Yes, this experience is unlike most people. Perhaps the problem is that most people are satisfied by the appearance of a working app despite it not working at all. Say, the first tool I was doing, did actually not recurse into subdirs with dylibs which made it useless.
So, AI slop, yes.
Yep, I literally built this last night with Opus 4.5 after my wife and I challenged each other to a typing competition. I gave it direction and feedback but it wrote all the actual code. Wasn't a one shot (maybe 3-4 shot) but didn't really have to think about it all that hard.
https://chronick.github.io/typing-arena/
With another more substantial personal project (Eurorack module firmware, almost ready to release), I set up Claude Code to act as a design assistant, where I'd give it feedback on current implementation, and it would go through several rounds of design/review/design/review until I honed it down. It had several good ideas that I wouldn't have thought of otherwise (or at least would have taken me much longer to do).
Really excited to do some other projects after this one is done.
Does anyone have a boring, multi-hour-long coding session with an agent that they've recorded and put on Vimeo or something?
As many other commentators have said, individual results vary extremely widely. I'd love to be able to look at the footage of either someone who claims a 10x productivity increase, or someone who claims no productivity increase, to see what's happening.
I tried to make several, but they all end up prematurely when the agent hits a wall in an hour or so, unless you make trivial shit.
That sounds like genuinely useful data, though! Please reply if you end up posting them!
Yeah Opus 4.5 is a massive step change in my experience. I feel like I’m working with a peer, not a junior I’m having to direct. I can give it highly ambiguous and poorly specified tasks and it… just does it.
I will note that my experience varies slightly by language though. I’ve found it’s not as good at typescript.
It’s excellent at typescript in my experience.
It’s also way better than I am at finding bits of code for reuse. I tell it, “I think I wrote this thing a while back, but it may never have been merged, so you may need to search git history.” And presto, it finds it.
If its a peer to you now the Ai has evolved while you didn't
> I feel like I’m working with a peer, not a junior I’m having to direct.
I think this says a lot.
the author asks one interesting question and then glides right by it. If the agents only need their own code, what should that code look like? If all their learning has come from old human code, how will that change in the future as the ecosystem fills up with agent code?
I was not expecting a couple of new apps being built, when the premise of the blog post talks about replacing "mid level engineers"
the thing about being an engineer at commercial capacity is "maintaining/enhancing an existing program/software system that has been developed over years by multiple people(including those who already left) and do it in a way that does not cause any outages/bugs/break existing functionality.
while the blog post mentions about the ability of using AI to generate new applications, but it does not talk about maintaining one over a longer period of time. for that, you would need real users, real constraints, and real feature requests which preferably pay you so you can priortize them.
I would love to see such blog posts where for example, a PM is able to add features for a period of one month without breaking the production, but it would be a very costly experiment.
I have used Claude Code for a variety of hobby projects. I am truly astounded at its capabilities.
If you tell it to use linters and other kinds of code analysis tools it takes it to the next level. Ruff for Python or Clippy for Rust for example. The LLM makes so much code so fast and then passes it through these tools and actually understands what the tools say and it goes and makes the changes. I have created a whole tool chain that I put in a pre commit text file in my repos and tell the LLM something like "Look in this text file and use every tool you see listed to improve code quality".
That being said, I doubt it can turn a non-dev into a dev still, it just makes competent devs way better still.
I still need to be able to understand what it is doing and what the tools are for to even have a chance to give it the guardrails it should follow.
It is very funny to start your article off with a bunch of breathless headlines about agents replacing human coders by the end of 2025, none of which happened, then the rest of the article is "okay but this time for real, an agent really WILL replace human coders."
I guess the best analogy I can think of is the transition from writing assembly language and the introduction of compilers. Now, (almost) no one knows, or cares, what comes out of the compiler. We just assume it is optimized and that it represents the source code faithfully. Seems like code might go that way too and people will focus on the right prompts and can simply assume the code will be correct.
A compiler is deterministic though.
Does a system being deterministic really matter if it's complex enough you can't predict it? How many stories are there about 'you need to do it in this specific way, and not this other specific way, to get 500x better codegen'?
I agree with the OP that I can get LLM's to do things now that I wouldn't even attempt a year ago, but I feel it has more to do with my own experience using LLM's (and the surrounding tools) than the actual models themselves.
I use copilot and change models often, and haven't really noticed any major differences between them, except some of the newer ones are very slow.
I generally feel the smaller and faster ones are more useful since they will let me discover problems with my prompt or context faster.
Maybe I'm simply not using LLM's in a way that lets the superiority of newer models reveal itself properly, but there is a huge financial incentive for LLM makers to pretend that their model has game-changing "special sauce" even if it doesn't.
Despite the abuse of quotation marks in the screenshot at the top of this link, Dario Amodei did not in fact say those words or any other words with the same meaning.
Yes, unfortunate that people keep perpetuating that misquote. What he actually said was "we are not far from the world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code."
https://www.cfr.org/event/ceo-speaker-series-dario-amodei-an...
The worst part about this is that you can't know anymore whether the software you trustingly install on your hardware is clean or if it was coded by a misaligned coding model with a secret goal that it has hidden from its prompt engineer and from you.
This could pretty much be the beginning of the end of everything, if misaligned models wanted to they could install killswitches everywhere. And you can't trust security updates either so you are even more vulnerable to external exploits.
It's really scary, I fear the future, it's going to be so bad. It's best to not touch AI at all and stay hidden from it as long as possible to survive the catastrophe or not be a helping part of it. Don't turn your devices into a node of a clandestine bot net that is only waiting to conspire against us.
I have to many machines standing around that are currently not powered on or are running somewhat airgapped with old software from around debian 8 and 9, so I guess they will be a safe haven once the AI overlords take over
What about Sonnet 4.5? I used both Opus and Sonnet on Claude.ai and found sonnet much better at following instructions and doing exactly what was asked.
(it was for single html/js PWA to measure and track heart rate)
Opus seems to go less deep, does it's own things, do not follow instructions exactly EVEN IF I WROTE ALL CAPS. With Sonnet 4.5 I can understand everything author is saying. May be Opus is optimised for Claude code and Sonnet works best on Web.
Cool. Please check back in with us after they’ve raised the price 50x and you can no longer build anything because you are alienated from your tools.
I’ve said many times, I’d still pay even $1,000 a month for CC.
But I’m a business owner so the calculus is different.
But I don’t think they’ll raise prices uncontrollably because competition exists. Even just between OpenAI and Anthropic.
Title is: "Opus 4.5 is going to change everything"
A Hacker News moderator likely changed the title because it's uninformatively vague.
Rules are rules, and excuses are excuses.
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I agree, it wrote an entire NES emulator for me.
https://news.ycombinator.com/item?id=46443767
It cloned one of the many open source ones available is what you mean.
As long as you give it deterministic goals / test criteria (compiles, lints, tests, E2E tests, achieve 100% parity with existing solution etc) it will brute force its way to a solution. Codex will work for hours/days, even weeks sometimes, until it has finished. A person would never work this way, but since this just runs in the background, there’s no issue with this approach except if you need it fast.
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To be fair that’s what I’d have done had I had to build it. Use a lot of examples etc and build on what other people have done
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Now ask it to create a NES game
It worries me that the best models, the ones that can one-shot apps and such, are all non-free and owned by companies who can't be trusted to have end-users' best interests at heart. It would be greatly reassuring to see a self-hostable model that can compete with Opus 4.5 and Gemini 3 at such coding tasks.
Claude Code is very good; good enough that I upgraded to the Max plan this week. However, it has a long way to go. It's great at one-shotting (with iterations) most ideas. However, it doesn't do as well when the task is complicated in an existing codebase. This weekend I migrated the backend for the SaaS I am building from Python to .NET Core. It did the migration but completely missed the conventions that the frontend was using to call the backend. While the converion itself went OK, every user journey was broken. I am still manually testing every code path and feeding in the errors to get Claude to fix it. My instructions were fairly comprehensive but Claude still missed most of it. My fault that I didn't generate tests first, but after this migration that's my first task.
This resonates with my experience in codex 5.2, at least directionally. I'm pretty persnickety about code itself, so I'm not to the point where I'll just let it rip. But in the last month or two things have gone from "I'll ask on the web interface and maybe copy some code into the project", to trusting the agent and getting a reasonable starting point about half the time.
> because models like to write code WAY more than they like to delete it
Yeah, this is the big one. I haven't figured it out either. New or changing requirements are almost always implemented a flurry of if/else branches all over the place, rather than taking the time for a step back and a reimagining of a cohesive integration of old and new. I've had occasional luck asking for this explicitly, but far more frequently they'll respond with recommendations that are far more mechanical, e.g. "you could extract a function for these two lines of code that you repeat twice", not architectural, in nature. (I still find pasting a bunch of files into the chat interface and iterating on refinements conversationally to be faster and produce better results).
That said, I'm convinced now that it'll get there sooner or later. At that point, I really don't know what purpose SWEs will serve. For a while we might serve as go-betweens between the coding agent and PMs, but LLMs are already way better at translating from tech jargon to human, so I can't imagine it would be long before product starts bypassing us and talking directly to the agents, who (err, which) can respond with various design alternatives, pros and cons of each, identify all the dependencies, possible compatibility concerns, alignment with future direction, migration time, compute cost, user education and adoption tracking, etc, all in real time in fluent PM-ese. IDK what value I add to that equation.
For the last year or so I figured we'd probably hit a wall before AI got to that point, but over the last month or so, I'm convinced it's only a matter of time.
All great until the code in production pushed by Opus 314.15 breaks and Opus 602.21, despite it's many tries, can't fix it and ends it with "I apologize". That's when you need a developer who can be told "fix it". But what if all the developers then are "Opus 600+ certified" ai-native and are completely incapable of working without it's assistance? World powers decide to open the forbidden vault in the Arctic and despite many warnings on the chamber, decide to raise the foul-mouthed programmer-demon called Torvalds....
I gave it a try, I asked to do a reddit like forum and it did pretty good but damn I quickly hit the daily limit of the $20 pro account, and it took 10% of the monthly just to do the setup and some basics. I knew LLM were expensive to run but I've never felt it directly. Even if the code is good it's kinda expensive for what you get.
Ho it was also quite funny it used the exact same color as hackernews and a similar layout.
I've only started but I mostly use Claude Code for building out code that has been done a million times. So its good at setting up a project to get all the boiler plate crap out of the way.
When you need to build out specific feature or logic, it can fail hard. And the best is when you have something working, and it fixes something else and deletes the old code that was working, just in a different spot.
I asked Claude’s opinion and it disagreed. :)
Claude’s response:
The article’s central tension is real - Burke went from skeptic to believer by building four increasingly complex apps in rapid succession using Opus 4.5. But his evidence also reveals the limits of that belief.
Notice what he actually built: Windows utilities, a screen recorder, and two Firebase-backed CRUD apps for his wife’s business. These are real applications solving real problems, but they’re also the kinds of projects where you can throw away the code if something goes wrong. When he says “I don’t know how the code works” and “I’m maybe 80% confident these applications are bulletproof,” he’s admitting the core problem with the “AI replaces developers” narrative.
That 80% confidence matters. In your Splink work, you’re the sole frontend developer - you can’t deploy code you’re 80% confident about. You need to understand the implications of your architectural decisions, know where the edge cases are, and maintain the system when requirements change. Burke’s building throwaway prototypes for his wife’s yard sign business. You’re building production software that other people depend on.
His “LLM-first code” philosophy is interesting but backwards. He’s optimizing for AI regeneration rather than human maintenance because he assumes the AI will always be there to fix problems. But AI can’t tell you why a decision was made six months ago when business requirements shift. It can’t explain the constraints that led to a particular architecture. And it definitely can’t navigate political and organizational context when stakeholders disagree about priorities.
The Firebase examples are telling - he keeps emphasizing how well Opus knows the Firebase CLI, as if that proves general capability. But Firebase is extremely well-documented, widely-discussed training data. Try that same experiment with your company’s internal API or a niche library with poor documentation. The model won’t be nearly as capable.
What Burke actually demonstrated is that Opus 4.5 is an excellent pair programmer for prototyping with well-known tools. That’s legitimately valuable. But “pair programmer for prototyping” isn’t the same as “replacing developers.” It’s augmenting someone who already knows how to build software and can evaluate whether the generated code is good.
The most revealing line is at the end: “Just make sure you know where your API keys are.” He’s nervous about security because he doesn’t understand the code. That nervousness is appropriate - it’s the signal that tells you when you’ve crossed from useful tool into dangerous territory.
LLMS like Opus, Gemini 3, and GPT-5.2/5.1-Codex-max, are phenomenal for coding and have only very recently crossed that gap between being "eh" and being quite fantastic to let operate on their own agentically. The major trade-off being a fairly expensive cost. I ran up $200 per provider after running through 'pro' tier limits during a single week of hacking over the holidays.
Unfortunately, it's still surprisingly easy for these models to fall into really stupid maintainability traps.
For instance today, Opus adds a feature to the code that needs access to a db. It fails because the db (sqlite) is not local to the executable at runtime. Its solution is to create this 100 line function to resolve a relative path and deal with errors and variations.
I hit ESC and say "... just accept a flag for --localdb <file>". It responds with "oh, that's a much cleaner implementation. Good idea!". It then implements my approach and deletes all the hacks it had scattered about.
This... is why LLMs are still not Senior engineers. They do plainly stupid things. They're still absurdly powerful and helpful, but if you want maintainable code you really have to pay attention.
Another common failure is when context is polluted.
I asked Opus to implement a feature by looking up the spec. It looked up the wrong spec (a v2 api instead of a v3) -- I had only indicated "latest spec". It then did the classic LLM circular troubleshooting as we went in 4 loops trying to figure out why calculations were failing.
I killed the session, asked a fresh instance to "figure out why the calculation was failing" and it found it straight away. The previous instance would have gone in circles for eternity because its worldview had been polluted by assumptions made -- that could not be shaken.
This is a second way in which LLMs are rigid and robotic in their thinking and approach -- taking the wrong way even when directed not to. Further reading on 'debugging decay': https://arxiv.org/abs/2506.18403
All this said, the number of failure scenarios gets ever smaller. We've gone from "problem and hallucination every other code block" to "problem every 200-1000 code blocks".
They're now in the sweet spot of acting as a massive accelerator. If you're not using them, you'll simply deliver slower.
It’s incredibly tiring to see this narrative peddled every damn day. I use opus 4.5 every day. It’s not much different than any previous models, still does dumb things all the time.
Same experience - I've had it fail at the same reasonably simple tasks I had opus 4 and sonnet 4.5 and sonnet 4 fail at when they aren't carefully guided and their work check and fixed...
I pivoted into integrations in 2022. My day-to-day now is mostly in learning the undocumented quirks of other systems. I turn those into requirements, which I feed to the model du jour via GitHub Copilot Agents. Copilot creates PRs for me to review. I'd say it gets them right the vast majority of the time now.
Example: One of my customers (which I got by Reddit posts, cold calls, having a website, and eventually word of mouth) wanted to do something novel with a vendor in my niche. AI doesn't know how to build it because there's no documentation for the interfaces we needed to use.
I recently am finishing the reading of Mistborn series, so please do not read further unless you want a spoiler.
There is a suspicion that mists can change written text.
So how can we be sure that Haiku didn't change the text in favour of AI then?
>Disclaimer: This post was written by a human and edited for spelling, grammer by Haiku 4.5
Either it wasn’t that good, or the author failed in the one phrase they didn’t proofread.
(No judgement meant, it’s just funny).
Honestly, I don’t understand universal praise for Opus 4.5. It’s good, but really not better than other agents.
Just today:
Opus 4.5 Extended Thinking designed psql schema for “stream updates after snapshot” with bugs.
Grok Heavy gave correct solution without explanations.
ChatGPT 5.2 Pro gave correct solution and also explained why simpler way wouldn’t work.
Are you using Claude Code? Because that might be the secret cause you're missing. With Claude Code I can instruct it to validate things after its done with code, and usually it finds that it goofed. I can also tell it to work on like five different things, and go "hey spin up some agents to work on this" and it will spawn 5 agents in parallel to work on said things.
I've basically ditched Groke et al and I refuse to give Sam Altman a penny.
For schema design phase I used web UI for all three.
Logical bug of using BIGSERIAL for tracking updates (generated at insert time, not commit time, so can be out of order) wouldn’t be caught by any number of iterations of Claude Code and would be found in production after weeks of debugging.
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The main issue in this discussion is the word "replace" . People will come up with a bunch of examples where humans are still needed in SWE and can't be fully replaced, that is true. I think claiming that 100% of engineers would be replaced in 2026 is ridiculous. But how about downsizing? Yeah that's quite probable.
These are very simple utilities. I expect AI to be able to build them easily. Maybe in a few years it will be able to write a complete photo editor or CAD application from first principles.
Then we're really screwed!
The question I keep asking myself is "how feasible will any of this be when the VC money runs out?" Right now tokens are crazy cheap. Will the continue to be?
No, they will get even cheaper.
Based on what logic?
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Weird title. Obviously, early AI agents were clumsy, and we should expect more mature performance in future.
Leopold Aschenbrenner was talking about "unhobbling" as an ongoing process. That's what we are seeing here. Not unexpected
IMO codex produces working code slowly, while Opus produces superficially working code quickly. I like using Opus to drive codex sessions and checking its output. Clawdbot is really good at that but a long running Claude Code session with codex as sub agents should work well also.
The above is for vibe coding; for taking the wheel, I can only use Opus because I suck at prompting codex (it needs very specific instructions), and codex is also way too slow for pair programming.
> I like using Opus to drive codex sessions and checking its output.
Why not the other way around? Have the quick brown fox churn out code, and have codex review it, guide changes, and loop?
I've actually gone one step further down the delegation. I use opus/gemini3 for plan, review, edit plan for a few steps. Then write it out to .md files. Then have GLM implement it (I got a cheap plan for like 28$ for a year on Christmas). Then have the code this produced reviewed and fixed if needed by opus. Final review by codex (for some reason it's very good at review, esp if you have solid checkboxes for it to check during review). Seems to work so far.
I agree, codex is great at reviewing as well. I think that’s because code is the ideal description of what we want to achieve, and codex is good (only) when it knows what must be achieved, as verbosely as possible.
Currently I don’t let GLM or Opus near my codebases unsupervised because I’m convinced that the better the foundation, the better the end result will be. Is the first draft not pretty crappy with GLM?
See also: a post from a couple days ago which came to the same conclusion that Opus 4.5 is an inflection point above Sonnet 4.5 despite that conclusion being counterintuitive: https://news.ycombinator.com/item?id=46495539
It's hard to say if Opus 4.5 itself will change everything given the cost/latency issues, but now that all the labs will have very good synthetic agentic data thanks to Opus 4.5, I will be very interested to see what the LLMs release this year will be able to do. A Sonnet 4.7 that can do agentic coding as well as Opus 4.5 but at Sonnet's speed/price would be the real gamechanger: with Claude Code on the $20/mo plan, you can barely do more than one or two prompts with Opus 4.5 per session.
A lot of the complaints about these tools seems to revolve around their current lack of ability to innovate for greenfield or overly complex tasks. I would agree with this assessment in their current state, but this sentiment of "I will only use AI coding tools when they can do 100% of my job" seems short-sighted.
The fact of the matter, in my experience, is that most of the day to day software tasks done by an individual developer are not greenfield, complex tasks. They're boring data-slinging or protocol wrangling. This sort of thing has been done a thousand times by developers everywhere, and frankly there's really no need to do the vast majority of this work again when the AIs have all been trained on this very data.
I have had great success using AIs as vast collections of lego blocks. I don't "vibe code", I "lego code", telling the AI the general shape and letting it assemble the pieces. Does it build garbage sometimes? Sure, but who doesn't from time to time? I'm experienced enough notice the garbage smell and take corrective action or toss it and try again. Could there be strange crevices in a lego-coded application that the AI doesn't quite have a piece for? Absolutely! Write that bit yourself and then get on with your day.
If the only thing you use these tools for is doing simple grunt-work tasks, they're still useful, and dismissing them is, in my opinion, a mistake.
The vast majority of engineers aren't refusing to use AI until it can do 100% of their job. They are just sick of being told it already can, when their direct experience contradicts that claim.
Yowza, AIs excel at writing low performance CRUD apps, REVOLUTION INCOMING
As impressive as Opus 4.5 is, it still fails in one situation that it assumes 0-index while the component it supposes to work with assume 1-index. It has access to the said information on disk, but just forgets to look into.
Opus 4.5 is incredible, it is the GPT-4 moment for coding because how honest and noticeable the capacity increase is. But still, it has blind spots just like human.
I had a similar feeling expressed in the title regarding ChatGPT 5.2
I haven't tried it for coding. I'm just talking about regular chatting.
It's doing something different from prior models. It seems like it can maintain structural coherence even for very long chats.
Where as prior models felt like System 1 thinking, ChatGPT5.2 appears like it exhibits System 2 thinking.
most of software engineering was rational, now it is becoming empirical
it is quite strange, you have to make it write the code in a way it can reason about it without it reading it, you also have to feel the code without reading all of it. like a blind man feeling the shape of an object; Shape from Darkness
you can ask opus to make a car, it will give you a car, then you ask it for navigation; no problem, it uses google maps works perfect
then you ask it to improve the breaks, and it will give internet to the tires and the break pedal, and the pedal will send a signal via ipv6 to the tires which will enable a very well designed local breaking system, why not, we already have internet for google maps.
i think the new software engineering is 10 times harder than the old one :)
To the author: you wrote those apps. Not like you used to, but you wrote them.
IMO, our jobs are safe. It's our ways of working that are changing. Rapidly.
SWE jobs are in fact, not safe, if vaguely defined specifications can be translated into functioning applications. I don't think agents are good enough to do that in larger applications yet, but it is something to consider.
Depends on the software. IMO, development speed will increase, but humans will continue to be the limiting factor, so we are safe. Our jobs, however, are changing and will continue to.
Title: Ask HN: How do you evaluate claims of “this model changes everything” in practice?
The release of every big model seems to carry the identical vibe: finally, this one crossed the line. The greatest programmer. The end of workflows and their meaning.
I’ve learned to slow myself down and ask a different question. What has changed in my day-to-day work after two weeks?
I currently make use of a filter with roughness.
Did it really solve a problem, or did it just make easy parts easier?
Has it lessened the number of choices or has it created new ones?
Have my review responsibilities decreased or increased?
Some things feel revolutionary on day one and then quietly fade into something that’s nice to have. Others barely wow, but stay around. ~
For those who've experienced a couple of cycles.
What indicators suggest that an upcoming release will be significant?
When do you alter your workflow, after how long?
Ai slop
Just an open thought, what if most improvement we are seeing is not mostly due to LLM improvements but to context management and better prompting?
Ofc the reality is a mix of both, but really curious on what contributes more.
Probably just using cursor with old models (eww) can yield a quick response.
Ok, if its almighty, then why is not the benchmarks at 100%? If you look at the individual issues, those are somewhat small and trivial changes in existing codebases.
https://swe-rebench.com/
(note that if you look at individual slices, Opus is getting often outperformed by Sonnet).
To those of you who use it: How much does Claude Code cost you a month on avg?
I only use VS Code with Copilot subscription ($10) and already get quite a lot out of it.
My experience is that Claude Code really drains your pocket extremely fast.
I started on the cheapest £15/mo "Pro" plan and it was great for home use when I'd do a bit of coding in the evenings only, but it wasn't really that usable with Opus--you can burn through your session allowance in a few minutes, but was fine with Sonnet. I used the PAYG option to add more, but cost me £200 in December, so I opted for the £90/mo "Max" plan which is great. I've used Opus 4.5 continuously and it's done great work.
I think when you look at it from the perspective of how much you get out of it compared with paying a human to do the same (including yourself), it is still very good value for money whether you use it for work or for your own projects. I do both. But when I look what I can now do for my own projects including open-source stuff, I'm very time-limited, and some of the things I want to do would take multiple years. Some of these tools can take that down to weeks, do I can do more with less, and from that perspective the cost is worth it.
Once you get your setup bulletproof such that you can have multiple agents running at the same time that can run unit tests and close their own loops things get even faster. However you accomplish that. Not as easy as it sounds mostly (and absurdly) due to port collision. E2E testing with playwright is another leap.
Can't you, like, ask Claude to fix port collision for you? Duh
Just let it test in different containers? That’s not the hard part IMO.
It's always fun to ask Opus what it thinks about articles like this. Here's what I got with no history or system prompt:
https://burkeholland.github.io/posts/opus-4-5-change-everyth...
Read this article and ultrathink critically about it. Provide your perspective.
The article makes a strong experiential case for improved velocity in AI-assisted development but contains several argumentation weaknesses and conflations worth examining.
The projects described are legitimately non-trivial: Firebase backend integration, Facebook OAuth, iOS apps in Swift (a language the author doesn't know), GitHub Actions pipelines, scheduled cloud functions. Getting these working in hours rather than weeks represents a real capability shift. The author is honest about his limitations and uncertainties, particularly the security concerns.
Where the argument breaks down:
1. "Replace developers" vs "dramatically augment developers" The author's own workflow contradicts the headline claim. He's still:
Making architectural decisions (choosing Firebase) Handling errors Opus couldn't see (XAML via Visual Studio) Writing custom prompts to shape output quality Manually auditing security Making product and UX decisions
This is developer work. The tool changed; the role didn't disappear.
2. The 80% security confidence undermines his thesis He admits he's shipping apps with "80% confidence" in security and calls it "too damn low." This is the crux: the AI accelerated production but didn't replace the judgment required to responsibly ship production software. The velocity gain exposed a competence gap rather than closing it.
3. Sample bias in project selection
All examples are:
Greenfield (no existing codebase) Single developer Personal/family use Standard patterns with excellent documentation (Firebase, SwiftUI, React Native) No regulatory, compliance, or scale requirements No team collaboration or handoff considerations
These constraints define a specific problem space where AI excels. Extrapolating to "developers are replaceable" ignores the majority of professional software work.
4. "Code doesn't need human readability" is underbaked His argument is circular: "Why optimize for human readability when the AI is doing all the work?" But:
His 80% security confidence exists because he can't read the code He had to use external tools (VS) when Opus couldn't diagnose errors What happens when context windows are exceeded and the LLM loses track? Model behavior changes between versions; human-readable code is version-agnostic
The custom prompt he shares actually encodes many good engineering practices (minimal coupling, explicit state, linear control flow) that benefit LLMs and humans. The "no comments needed" claim conflates what's optimal for LLM regeneration with what's optimal for debugging production issues at 3am. What's actually being demonstrated
The honest version of this article would be: Opus 4.5 dramatically compresses the gap between "can write code" and "can ship a personal app" for a specific class of greenfield projects. That's genuinely transformative for hobbyists, indie developers, and people solving their own problems. But that's different from "replacing developers." The article demonstrates a power tool; power tools don't eliminate tradespeople.
There's something eerily recursive about Opus 4.5’s sensible take calming the anxiety about Opus 4.5’s capabilities and impact. It's probably the right take, but I feel weird the most pragmatic response to this article is from said model.
What's the best coding agent you can run locally? How far behind Opus 4.5 is it?
The best is probably something like GLM 4.7/Minimax M2.1, and those are probably at most Sonnet 4 level, which is behind Opus 4.1, which is behind Sonnet 4.5, which is behind Opus 4.5 ;)
And honestly Opus 4.5 is a visible step change above previous Anthropic models.
Does it even fit into a 5090 or a Ryzen 395+?
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I agree. Claude Code went from being slower than doing it myself to being on average faster, but also far less exhausting so I can do more things in general while it works.
YEP
Things are changing. Now everyone can build bespoke apps. Are these apps pushing the limits of technology? No! But they work for the very narrow and specific domain they where designed. And yes they do not scale and have as much bugs as your personal shell scripts. But they work.
But let's not compare these with something more advance - at least not yet. Maybe by end of this year?
We switched from Sonnet 4.5 to Opus 4.5 as our default coding agent recently and we pay the price for the switch (3x the cost) but as the OP said, it is quite frankly amazing. It does a pretty good job, especially, especially when your code and project is structured in a such a way that it helps the agent perform well. Anthropic released an entire video on the subject recently which aligns with my own observations as well.
Where it fails hard is in the more subtle areas of the code, like good design, best practices, good taste, dry, etc. We often need to prompt it to refactor things as the quick solution it decided to do is not in our best interest for the long run. It often ends in deep investigations about things which are trivially obvious. It is overfitted to use unix tools in their pure form as it fail to remember (even with prompting) that it should run `pnpm test:unit` instead `npx jest` - it gets it wrong every time.
But when it works - it is wonderful.
I think we are at the point where we are close to self-improving software and I don't mean this lightly.
It turns out the unix philosophy runs deep. We are right now working on ways to give our agents more shells and we are frankly a few iterations there. I am not sure what to expect after this but I think whatever it is, it will be interesting to see.
Oh another run of new small apps. Why not unleash this oh so powerful tools not on a jira ticket written two years ago, targeting 3 different repos in an old legacy moloch, like actual work?
It's always just the "Fibonacci" equivalent
Did some of that today. Extracting logic from Helm templates that read like 2000s PHP and moving it to a nushell script rendering values. Took a lot of guidance both in terms of making it test its own code and architectural/style decisions and I also use Sonnet, but it got there.
I like writing code
Yea, my issue with Opus 4.5 is it's the first model that's good enough that I'm starting to feel myself slip into laziness. I catch myself reviewing its output less rigorously than I had with previous AI coding assistants.
As a side project / experiment, I designed a language spec and am using (mostly) Opus 4.5 to write a transpiler (language transpiles to C) for it. Parser was no problem (I used s-expressions for a reason). The type checker and transpiler itself have been a slog - I think I'm finding the limits of Opus :D. It particularly struggles with multi-module support. Though, some of this is probably mistakes made by me while playing architect and iterating with Claude - I haven't written a compiler since my senior year compiler design course 20+ years ago. Someone who does this for a living would probably have an easier time of it.
But for the CRUD stuff my day job has me doing? Pffttt... it's great.
People should finally understand that LLMs are a lossy database of PAST knowledge. Yes, if you throw a task at it that has been done tons of times before, it works. Which is not a surprise, because it takes minutes to Google and index multiple full implementations of "Tool that allows you to right-click on an image to convert it". Without LLM you could do the same: Just copy&paste the implementation of that from Microsoft Powertoys, for example.
What LLMs will NOT do however, is write or invent SOMETHING KNEW.
And parts of our industry still are about that: Writing Software that has NOT been written before.
If you hire junior developers to re-invent the wheels: Sure, you do not need them anymore.
But sooner or later you will run out of people who know how to invent NEW things.
So: This is one more of those posts that completely miss the point. "Oh wow, if I look up on Wikipedia how to make pancakes I suddenly can make and have pancakes!!!1". That always was possible. Yes, you now can even get an LLM to create you a pancake-machine. Great.
Most of the artists and designers I am friends with have lost their jobs by now. In a couple of years you will notice the LLMs no longer have new styles to copy from.
I am all for the "remix culture". But don't claim to be an original artist, if you are just doing a remix. And LLM source code output are remixes, not original art.
> What LLMs will NOT do however, is write or invent SOMETHING KNEW.
Counterpoint: ChatGPT came up with the new expression "The confetti has left the cannon" a few years ago.
So, your claim is not obviously true. Can you give us an example of a programming problem where the LLMs fail to solve it?
"Opus 4.5 feels to me like"
The article is fine opinion but at what point are we going to either:
a) establish benchmarks that make sense and are reliable, or
b) stop with the hypecycle stuff?
>establish benchmarks that make sense and are reliable
How aren't current LLM coding benchmarks reliable?
They're manipulated.
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> make sense and are reliable
If you can figure out how to create benchmarks that make sense, are reliable, correlate strongly to business goals, and don't get immediately saturated or contorted once known, you are well on your way to becoming a billionaire.
I've found asking GPT-5.2 High to review Opus 4.5's code to be really productive. They find different things.
This is great can't wait for the future when our VC ideas can become unicorns, without CEO's & Founders..
I'm always surprised to never see any comments in those discussions from people who just like coding, learning, solving problems… I mean, it's amazing that LLMs can build an image converter or whatever you dream of, in a language you don't know, in a field you are not familiar with, in 1 hour, for 30 cents… I'm sure your boss and shareholders love it. But where is the fun in that? For me it kills any interest in doing what I'm doing. I'm lucky enough to work in a place where using LLMs is not mandatory (yet), I don't know how people can make it through the day just writing prompts and reviewing AI slop.
After decade(s) working with either enterprise crud or web agency fancy websites, the novelty wears off.
It's just boring and I'm glad to delegate most of the repetitive work.
But sure, if I'm doing something new, I still like to craft lines of code myself.
When complexity increases, you end up handholding them in pieces.
It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
I’ve always liked the quote that sufficiently advanced tech looks like magic, but its mistake to assume that things that look like magic also share other properties of magic. They don’t.
Software engineering spans over several distinct skills: forming logical plans, encoding them in machine executable form(coding), making them readable and expandable by other humans(to scale engineering), and constantly navigating tradeoffs like performance, maintainability and org constraints as requirements evolve.
LLMs are very good at some of these, especially instruction following within well known methodologies. That’s real progress, and it will be productized sooner than later, having concrete usecases, ROI and clearly defined end user.
Yet, I’d love to see less discussion driven by anecdotes and more discussion about productizing these tools, where they work, usage methodologies, missing tooling, KPIs for specific usecases. And don’t get me started on current evaluation frameworks, they become increasingly irrelevant once models are good enough at instruction following.
> It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
Progress is so fast right now anecdotes are sometimes more interesting than proper benchmarks. "Wow it can do impressive thing X" is more interesting to me than a 4% gain on SWE Verified Bench.
In early days of a startup "this one user is spending 50 hours/week in our tool" is sometimes more interesting than global metrics like average time in app. In the early/fast days, the potential is more interesting than the current state. There's work to be done to make that one user's experience apply to everyone, but knowing that it can work is still a huge milestone.
At this point I believe the anecdotes more than benchmarks, cause I know the LLM devs train the damn things on the benchmarks.
A benchmark? probably was gamed. A guy made an app to right click and convert an image? prolly true, have to assume it may have a lot of issues but prima facie I just make a mental note that this is possible now.
> It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.
It's a very subjective topic. Some people claim it increases their productivity 100x. Some think it is not fit for purpose. Some think it is dangerous. Some think it's unethical.
Weirdly those could all be true at the same time, and where you land on this is purely a matter of importance to the user.
> Yet, I’d love to see less discussion driven by anecdotes and more discussion about productizing these tools, where they work, usage methodologies, missing tooling, KPIs for specific usecases. And don’t get me started on current evaluation frameworks, they become increasingly irrelevant once models are good enough at instruction following.
I agree. I've said earlier that I just want these AI companies to release an 8-hour video of one person using these tools to build something extremely challenging. Start to finish. How do they use it, how does the tool really work. What's the best approaches. I am not interested in 5-minute demo videos producing react fluff or any other boiler plate machine.
I think the open secret is that these 'models' are not much faster than a truly competent engineer. And what's dangerous is that it is empowering people to 'write' software they don't understand. We're starting to see the AI companies reflect this in their marketing, saying tech debt is a good thing if you move fast enough....
This must be why my 8-core corporate PC can barely run teams and a web browser in 2026.
How many 1+ hour videos of someone building with AI tools have you sought out and watched? Those definitely exist, it sounds like you didn't go seeking them out or watch them because even with 7 less hours you'd better understand where they add value enough to believe they can help with challenging projects.
So why should anybody produce an 8 hour video for you when you wouldn't watch it? Let's be real. You would not watch that video.
In my opinion most of the people who refuse to believe AI can help them while work with software are just incurious/archetypical late adopters.
If you've ever interacted with these kinds of users, even though they might ask for specs/more resources/more demos and case studies or maturity or whatever, you know that really they are just change-resistant and will probably continue to be as as long as they can get away with it being framed as skepticism rather than simply being out of touch.
I don't mean that in a moralizing sense btw - I think it is a natural part of aging and gaining experience, shifting priorities, being burned too many times. A lot of business owners 30 years ago probably truly didn't need to "learn that email thing", because learning it would have required more of a time investment than it would yield, due to being later in their career with less time for it to payoff, and having already built skills/habits/processes around physical mail that would become obsolete with virtual mail. But a lot of them did end up learning that email thing 5, 10, whatever years later when the benefits were more obvious and the rest of the world had already reoriented itself around email. Even if they still didn't want to, they'd risk looking like a fossil/"too old" to adapt to changes in the workplace if they didn't just do it.
That's why you're seeing so many directors/middle managers doing all these though leader posts about AI recently. Lots of these guys 1-2 years ago were either saying AI is spicy autocomplete or "our OKR this quarter is to Do AI Things". Now they can't get away with phoning it in anymore and need to prove to their boss that they are capable of understanding and using AI, the same way they had to prove that they understood cloud by writing about kubernetes or microservices or whatever 5-10 years ago.
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I can only speak for myself, but it feels like playing with fire to productize this stuff too quick.
Like, I woke up one day and a magical owl told me that I was a wizard. Now I control the elements with a flick of my wrist - which I love. I can weave the ether into databases, apps, scripts, tools, all by chanting a simple magical invocation. I create and destroy with a subtle murmur.
Do I want to share that power? Naturally, it would be lonely to hoard it and despite the troubles at the Unseen University, I think that schools of wizards sharing incantations can be a powerful good. But do I want to share it with everybody? That feels dangerous.
It's like the early internet - having a technical shelf to climb up before you can use the thing creates a kind of natural filter for at least the kinds of people that care enough to think about what they're doing and why. Selecting for curiosity at the very least.
That said, I'm also interested in more data from an engineering perspective. It's not a simple thing and my mind is very much straddling the crevasse here.
LLMs are lossy compression of a corpus with a really good parser as a front end. As human made content dries up (due to LLM use), the AI products will plateau.
I see inference as the much bigger technology although much better RAG loops for local customization could be a very lucrative product for a few years.
Well said.
That final line: "Disclaimer: This post was written by a human and edited for spelling, grammer by Haiku 4.5"
Yeah, GRAMMAR
For all the wonderment of the article, tripping up on a penultimate word that was supposedly checked by AI suddenly calls into question everything that went before...
Presumably that disclaimer was added manually after Haiku had run the checks.
What is with all the Claude spam lately on hn?
this is just optimizing for token windows. flat code = less context. we did the same thing with java when memory was expensive, called it "lightweight frameworks"
Post the code open source and run it on prod.
The harness here was Claude Code?
Once again. It is not greenfield projects most of us want to use AI coding assistance for. It is for an existing project, with a byzantine mess of a codebase, and even worse messes of infrastructure, business requirements, regulations, processes, and God knows what else. It seems impossible to me that AI would ever be useful in these contexts (which, again, are practically all I ever deal with as a professional in software development).
Time to get a new job.
Opus 4.5 burns through tokens really fast.
I've been noticing it's more on par with sonnet these days. I don't know if that means Opus is getting more efficient, sonnet getting less efficient, or perhaps Opus is getting to the answer fast enough to overcome the higher token spend.
I've noticed. I'm already through 48% of my quota for this month.
For some reason Opus 4.5 is blowing up recently after having been released for weeks. I guess because holidays are over? Active agent users should have discovered this for a while.
Ugh, I'm so sick of these "I can use AI to solve an already solved problem, thus programmers aren't relevant." Note the solved problem part. This isn't convincing except to people that want a (bad) argument to depress wages and lay off workers while making the existing seniors take on more and more work. This is overall bad for the industry.
Aren't most products that actually ship some kind of "solved problem" though?
Opus helped me optimized a wonky SQL query today from 4s to 5min. Truly something that only a super intelligence is capable of.
Oh shit your UI looks exactly 100% like mine.
Blogspam.
blogslop
Every time I see a post like this on HN I try again and every time I come to the same conclusion. I have never see one agent managing to pull something off that I could instantly ship. It still ends up being very junior code.
I just tried again and ask Opus to add custom video controls around ReactPlayer. I started in Plan mode which looked overal good (used our styling libs, existing components, icons and so on).
I let it execute the plan and behold I have controls on the video, so far so good. I then look at the code and I see multiple issues: Over usage of useEffect for trivial things, storing state in useState which should be computed at run time, failing to correctly display the time / duration of the video and so on...
I ask follow up question like: Hide the controls after 2 seconds and it starts introducing more useEffects and states which all are not needed (granted you need one).
Cherry on the cake, I asked to place the slider at the bottom and the other controls above it, it placed the slider on the top...
So I suck at prompting and will start looking for a gardening job I guess...
These posts are never, never made by someone who is responsible for shipping production code in a large, heavily used application. It's always someone at a director+ level who stopped production coding years ago, if they ever did, and is tired of their engineers trying to explain why something will take more than an hour.
It is also often low-proficiency developers with their minds blown over how quickly they can build something using frameworks / languages they never wanted to learn or understand.
Though even that group probably has some overlap with yours.
Back in the day when you found a solution to your problem on Stackoverflow, you typically had to make some minor changes and perhaps engage in some critical thinking to integrate it into your code base. It was still worth looking for those answers, though, because it was much easier to complete the fix starting from something 90% working than 0%.
The first few times in your career you found answers that solved your problem but needed non-trivial changes to apply it to your code, you might remember that it was a real struggle to complete the fix even starting from 90%. Maybe you thought that ultimately, that stackoverflow fix really was more trouble than it was worth. And then the next few times you went looking for answers on stackoverflow you were better at determining what answers were relevant to your problem/worth using, and better at going from 90% to 100% by applying their answers.
Still, nobody really uses stackoverflow anymore: https://blog.pragmaticengineer.com/stack-overflow-is-almost-...
You and most of the rest of us are all actively learning how to use their replacement
> it was much easier to complete the fix starting from something 90% working than 0%.
As an expert now though, it is genuinely easier and faster to complete the work starting from 0 than to modify something junky. The realplayer example above I could do much faster, correctly, than I could figure out what the AI code was trying to do with all the effects and refactor it correctly. This is why I don't use AI for programming.
And for the cases where I'm not skilled, I would prefer to just gain skill, even though it takes longer than using the AI.
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The difference is you’re generally retooling for your purpose rather than scouring for multiple, easily avoidable screw ups that if overlooked will cause massive headaches later on.
I've spent quite a bit of time with Codex recently and come to the conclusion that you can't simply say "Let's add custom video controls around ReactPlayer." You need to follow up with a set of strict requirements to set expectations, guard rails, and what the final product should do (and not do). Even then it may have a few issues, but continuing to prompt with clearly stated problems that don't meet the requirements (or you forgot to include) usually clears it up.
Code that would have taken me a week to write is done in about 10 minutes. It's likely on average better than what I could personally write as a novice-mid level programmer.
>You need to follow up with a set of strict requirements to set expectations, guard rails, and what the final product should do (and not do).
that usually very hard to do part, and is was possible to spent few days on something like that in real word before LLMs. But with LLMs is worse because is not enough to have those requirements, some of those won't work for random reasons and is no any 'rules' that can grantee results. It always like 'try that' and 'probably this will work'.
Just recently I was struggled with same prompt produced different result between API calls before I realized that just usage of few more '\"' and few spaces in prompt leaded model to completely different route of logic which produced opposite answers.
By the time I have figured out all those quirks and guardrails I could have done it myself in 45min tops.
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It sounds like it takes you at least 10 minutes to just write the prompt with all the details you mentioned. Especially if you need to continue and prompt again (and again?).
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I used to run into this quite a bit until I added an explicit instruction in CLAUDE.md to the effect of:
> Be thoughtful when using `useEffect`. Read docs at https://react.dev/learn/you-might-not-need-an-effect to understand if you really need an effect
Have you tried Roo Code in "Orchestrator" mode? I find it generally "chews" the tasks I give it to then spoon feed into sub-tasks in "Code" (or others) mode, leaving less room to stray from very focused "bite-sized" changes.
I do need to steer it sometimes, but since it doesn't change a lot at a time, I can usually guide the agent and stop the disaster before it spreads.
A big caveat is I haven't tried heavy front-end stuff with it, more django stuff, and I'm pretty happy with the output.
I have a vanilla JS project. I find that very small llms are able to work on it with no issue. (Including complete rewrites.) But I asked even large LLMs to port it to React and they all consistently fail. Basic functionality broken, rapid memory leaks.
So I just stuck with vanilla JS.
n = 1 but React might not be a great thing to test this stuff with. For the man and the machine! I tried and failed to learn React properly like 8 times but I've shipped multiple full stack things in like 5 other languages no problem.
usually for me, after a good plan is 90% solid working code. the problem do arise when you ask it to change the colors it choose of light grey text over a white background. this thing still can't see and it's a huge drawback for those who got used to just prompting away their problems
I always assume the person either didn't use coding agents in a while or its their first time. don't get me wrong, i love claude code, but my students are still better at getting stuff done that i can just approve and not micromanage. thats what i think everyone is missing from their commentary. you have to micromanage a coding agent. you don't have to micromanage a good student. when you dont need to micromanage anymore at all, that's when the floor falls out and everyone has a team of agents doing whatever they want to make them all billionaires or whatever it is AI is promising to do those days.
Around a Uni I think a lot about what students are good at and what they aren't good at.
I wouldn't even think about hiring a student to do marketing work. They just don't understand how hard it is to break through people's indifference and lack the hustle. I want 10-100x more than I get out of them.
Photos in The Cornell Daily Sun make me depressed. Students take a step out the door, take a snap, then upload it. I think the campus is breathtakingly beautiful and students just don't do the work to take good photos that show it.
In coding it is across the map. Even when I am happy with the results they still do the first 80% that takes another 80% to put in front of customers. I can be really proud of how it turned out in the end despite them missing the point of the design document they were handed.
I was in a game design hackathon where most of the winners were adults or teams with an adult on them. My team won player's choice. I'll take credit for my startup veteran talent of fearlessly demonstrating broken software on stage and making it look great and doing project management with that in mind. One student was solid on C# and making platformers in Unity. I was the backup programmer who worked like a junior other than driving them crazy slowing them down with relentlessly practical project management. The other student made art that fit our game.
We were at each other's throats at the end and shocked that we won. I think I understood the value everybody brought but I'm not sure my teammates did.
I find anecdotes like yours bewildering, because I've been using Opus with Vue.js and it crushes everything I throw at it. The amount of corrections I need to make tend to be minimal, and mostly cosmetic.
The tasks I give it are not trivial either. Just yesterday I had it create a full-blown WYSIWYG editor for authoring the content we serve through our app. This is something that would have taken me two weeks, give or take. Opus looked at the content definitions on the server, queried the database for examples, then started writing code and finished it in ~15 minutes, and after another 15-20 minutes of further prompting for refinement, it was ready to ship.
Created a WYSIWYG editor or copied it off the internet like your average junior would, bugs included?
If that editor is very complicated (as they usually are) it makes sense to just opt for a library. If it's simple then AI is not required and would only reduce familiarity with how it works. The third option is what you did and I feel like it's the option with the lowest probability of ending up with a quality solution.
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Yep. It sucks. People are delusional. Let's ignore LLMs and carry on...
On a more serious note:
1) Split tasks into smaller tasks just like a human would do
Would you bash your keyboard for an hour, adding all video controls at once before even testing if anything works at all? Ofc not. You would start by adding a slider and test it until you are satisfied. Then move to next video control. An so on. LLMs are the same. Sometimes they can one-shot many related changes in a single prompt but the common reality is what you experienced: it works sometimes but the code is suboptimal.
2) Document desireable and undesireable coding patterns in AGENTS.md (or CLAUDE.md)
If you found over usage of useEffect, document it on AGENTS.md so next time the LLM knows your preference.
I have been using LLMs since Sonet 3.5 for large enterprise projects (1kk+ lines of code, 1k+ database tables). I just don't ask it to "draw the rest of owl" as the saying goes.
So? Getting a months' worth of junior level code in an hour is still unbelievable.
Whats the improvement here? I spend more time fixing it then doing it myself anyways. And I have less confidence in the code Opus generates
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Ah, another thread filled with people sharing anecdotes about how they asked Claude to one-shot an entire project that would take people weeks if not months.
Are the LLMs in any way trained semantically or by hooks that you can plug in, say, Python docs? And if a new version of Python then gets released then the training data changes, etc
- does it understand the difference between eslint 8x and eslint 9.x?
- or biome 1.x and biome 2.x ?
- nah! it never will and that is why it ll never replace mid level engineers, FTFY
it does if you feed docs.
just like humans
> And if it ran into errors, it would try and build using the dotnet CLI, read the errors and iterate until fixed.
Antigravity with Gemini 3 pro from Google has the same capability.
Having used Opus 4.5 for the past 5 weeks, I estimate it codes better than 95% of the people I've ever worked with.
And it writes with more clarity too.
The only people who are complaining about "AI slop" are those whose jobs depend on AI to go away (which it won't).
This article is much better than hundred of similar articles "AI will change software engineering" because it have links to actual products created with said "AI". I can't say they are impressive, but definitely so for laypeople.
I'm tired of constantly debating the same thing again and again. Where are the products? Where is some great performing software all LLM/agent crafted? All I see is software bloatness and decline. Where is Discord that uses just a bunch of hundreds megs of ram? Where is unbloated faster Slack? Where is the Excel killer? Fast mobile apps? Browsers and the web platform improved? Why Cursor team don't use Cursor to get rid of vscode base and code its super duper code editor? I see tons of talking and almost zero products.
This deserves more upvotes.
Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same. Unless you have a secret LLM or some magical prompts that make the code better/more efficient than your competitions, your vibe coded product has no advantage over competition and no moat. What actually matters is everything else -- user experience (which requires hours of meetings and usability studies), integration with own/other people's products, business, marketing, sales etc, much of which you can't vibe code your way to success.
I'm not sure what point you're making here. Tech is rarely the moat, you even get to that point at the end of your post. The "vibe coding" advantage is faster time to market, faster iterations, etc. These things will help you get that user experience, integrations, etc.
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> Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same.
I think you are strawmanning what "vibe coders" do when they build stuff. It's not simple one-shot generation of eg twitter clones, it's really just iterative product development through an inconsistently capable/spotty LLM developer. It's not really that different from a product manager hiring some cheap developer and feeding them tasks/feature requests. By the way, competitors can hire those and chip away at your moat too!
> Unless you have a secret LLM or some magical prompts that make the code better/more efficient than your competitions, your vibe coded product has no advantage over competition and no moat
This is just not true, and you kind of make my point in the next sentence: many companies competitive advantages come from distribution, trust, integration, regulatory, marketing/sales, network effects. But also, vibe coding is not really about prompts so much as it is product iteration. Anybody product can be copied already, yet people still make way more new products than direct product clones anyway, because it's usually more valuable to go to market with stronger, more focused, or more specialized/differentiated software than a copy.
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>> Even if there is a "fully vibe-coded" product that has real customers, the fact that it's vibe-coded means that others can do the same.
But that's precisely why you don't hear about these products: the creators don't disclose that they were vibe-coded, because if they do, that invites competition.
I personally know of four vibe-coded products that generate over $10k/mo. Two of them were made by one friend, one was made by another, and the last one by my cousin. None of these people are developers. But they are making real money.
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With all due respect somebody could launch a version of Discord that's 10x faster tomorrow and nobody would know about it
It's very difficult to unseat those incumbents, especially those with strong network effects.
Plus the people that work in those larger companies are not at the edge of AI coding at all and not motivated to rock the boat
you can build it and simply use it in your own office? There is no need to shout about it if the cost of writing software goes to zero (but the value remains non-zero!).
Get the feeling with the pending IPO, there might be some challengers to discord that get more traction due to the protracted enshittification of the platform (cf. bluesky)
Totally disagree. One example is Zed which is very well known and it's faster than any other editor, wasn't built with AI though.
> People on larger companies are not at the edge of AI coding
False Microsoft is all in with Copilot, and I can't believe the company that created Copilot doesn't use it internally, I'd rather say they should be the ones that would know how to master it! Yet no better vscode, still bloated teams etc etc
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This argument falls a little flat when you consider how much software may or may not be written inside one's own personal work flow, or to scale that up, inside a small business. The idea that a small business doing >1mil revenue can now hire a dev or two, and build out a fairly functional domain-driven system should not be understated. The democratization of software, and the lowering of the barriers to entry to basic CRUD apps, may not necessarily show up in a TAM report... Do you need a killer app that treads into unicorn territory to prove it's impact? What about a million apps that displace said unicorn potentials by removing the need for a COTS?
Oh, and remember, the iPhone was revolutionary but it was diffused so slowly into the greater economy, the impact on global GDP was basically negligent. Actually, almost all the perceived grandiose tech jumps did not magically produce huge GDP gains overnight.
Your argument falls a little flat considering that you mention "hire a dev or two" while the whole narrative is "we don't need software engineers anymore" and Anthropic alone declares that "Although engineers use Claude frequently, more than half said they can “fully delegate” only between 0-20% of their work to Claude" https://www.anthropic.com/research/how-ai-is-transforming-wo...
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The bigger the product, the harder this is.
However, I think the biggest thing is the replacement of products. We are in a place where he talked about replacing two products his wife was using with custom software. I personally have used LLMs to build things that are valuable for me that I just don't have time for otherwise.
I share similar feelings. I feel like I'm reading the same comments about LLM since a year, only model version changes.
Obviously there's improvement in the models and tooling, but the debate seems very artificial.
This is true. I think most people are mostly using AI at work to fix bugs in existing codebases. A smaller group of people are benchmarking AI by giving it ideas for apps that no one needs and seeing if it can get close. The smallest group of people is actually designing new software and asking the AI to iterate on it.
Except for maybe an "Excel killer", all those things you listed are not things people are willing to pay for. Also agents are bad at that kind of work (most devs are bad at that stuff, it's why it was something people whined about even before agents).
And funnily enough there are products and tools that are essentially less bloated slack/discord. Have you heard of https://stoat.chat/ (aka revolt) or https://pumble.com/ or https://meet.jit.si/? If not I would guess it's for one of two reasons: not caring enough about these problems to even go looking for them yourself, or their lack of "bloatedness" resulting in them not being a mature/fully featured enough product to be worth marketing or adopting.
If you'd like to see a product mostly made with agents/for agents you can check out mine at https://statue.dev/ - we're making a static site generator with a templating and component system paired with user-story driven "agentic workflows" (~blueprints/playbooks for common user actions like "I need to add a new page and list it on the navbar" or "create a site from the developer portfolio template personalized for my github").
I would guess most other projects are probably in a similar situation as we are: agentic developer tools have only really been good enough to heavily use/build products around for a few months, so it's a typical few-month-old project. But agents definitely made it easier to build.
Not willing to pay for? How can you be sure? For example explain then why many gamers are ditching Windows for Linux and buying hardware from Valve... There must be a reason. Every person I talked to that uses Excel hate how slow it is, same for teams and many other products. Finally, were the mentioned products built with vibe coding?
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I did a lot of analysis and biz dev work on the "Excel killer" and came to the conclusion that it would be hard to get people to pay for.
For one thing most enterprises and many individuals have an Office 365 subscription to access Office programs which are less offensive than Excel so they aren't going to save any money by dropping Excel.
On top of it the "killer" would probably not be one product aimed at one market but maybe a few different things. Some people could use "visual pandas" for instance, something that today would be LLM-infused. Other people could use a no-code builder for calculations. The kind of person who is doing muddled and confused work with Excel wouldn't know which "killer" they needed or understand why decimal math would mean they always cut checks in the right amount.
Wrt statue.dev good luck for sure with the project but I personally don't need yet another static site generator, nextjs like but with unpopular svelte, bloated with tons of node modules creating another black hole impossible to escape from. If agents works this well why would I need to use your library? I just tell an agent to maintain my static site who cares which tech stack
AI amplified development has the most impact on build-vs-buy decisions.
We should expect the decreased difficulty of creating software to drive down prices.
> decreased difficulty of creating software to drive down prices.
And here we go again, if difficulty has been decreased so much, where are the fixes or the products?
Anecdotally I had Gemini convert a simple react native app to swift in two prompts. If it's that simple then maybe we will see less of the chromium desktop apps
I'd argue the contrary, YOU KNOW you have the option, ease of entering doesn't mean they will know how to choose better, they will just vibe code more electron apps. In fact my prediction is not there will be less Electron apps but more
Hi, I’m building such one: https://minfx.ai/
Still early, but iterating really fast!
https://www.anthropic.com/research/how-ai-is-transforming-wo...
see "How much work can be fully delegated to Claude?": "Although engineers use Claude frequently, more than half said they can “fully delegate” only between 0-20% of their work to Claude"
There won't be anything like you're asking for, even the vendors themselves (they'll be the most positive and most enthousiastic about using it) can't do this with them.
I'm not asking for it, i'm asking to stop bulshitting about ai
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Thank you for linking this very useful and much more realistic / grounded stat.
who told you that mb of ram is a definition of success?
Opus was out only few months, and it will take time to get this new wave to market. i can assure you my team become way more productive because of opus. not a single developer but an etnire team.
It's a definition of what runs and what not on consumer grade computers, Discord has a routine that now checks if memory goes over a certain threshold and eventually restart itselfs, this is a measure of engineering total failure imo
Could someone explain this to me? I have the same question: why Cursor team don't use Cursor to get rid of vscode base and code its super duper code editor?
I really can't tell if this is satire or not
It's been interesting watching HN shift in my direction on this in recent weeks...
I had been saying since around summer of this year that coding agents were getting extremely good. The base model improvements were ok, but the agentic coding wrappers were basically game changers if you were using them right. Until recently they still felt very context limited, but the context problem increasingly feels like a solved problem.
I had some arguments on here in the summer about how it was stupid to hire junior devs at this point and how in a few years you probably wouldn't need senior devs for 90% of development tasks either. This was an aggressive prediction 6 months ago, but I think it's way too conservative now.
Today we have people at our company who have never written code building and shipping bespoke products. We've also started hiring people who can simply prove they can build products for us using AI in a single day. These are not software engineers because we are paying them wages no SWEs would accept, but it's still a decent wage for a 20 something year old without any real coding skills but who is interested in building stuff.
This is something I wouldn't have never of expected to be possible 6 months ago. In 6 months we've gone from senior developers writing ~50% of their code with AI, to just a handful of senior developers who now write close to 90% of their code with AI while they support a bunch of non-developers pumping out a steady stream of shippable products and features.
Software engineers and traditional software engineer is genuinely running on borrowed time right now. It's not that there will be no jobs for knowledgable software engineers in the coming years, but companies simply won't need many hotshot SWEs anymore. The companies that are hiring significant numbers of software engineers today simply can not have realised how much things have changed over just the last few months. Apart from the top 1-2% of talent I simply see no good reason to hire a SWE for anything anymore. And honestly outside of niche areas, anyone hand-cracking code today is a dinosaur... A good SWE today should see their job as simply reviewing code and prompting.
If you think that the quality of code LLMs produce today isn't up to scratch you've either not used the latest models and tools or you're using them wrong. That's not to say it's the best code – they still have a tendency to overcomplicate things in my opinion – but it's probably better than the average senior software engineer. And that's really all that matters.
I'm writing this because if you're reading this thinking we're basically still in 2024 with slightly better models and tooling you're just wrong and you're probably not prepared for what's coming.
Hi Kypro this is very interesting perspective. Can you reach out to me? I'd like to discuss what you're observing with you a bit in private as it relates heavily to a project I'm currently working on. My contact info is on my profile. Pls shoot me a connection request and just say you're kypro from HN :)
Or is there a good way for me to contact you? Your profile doesn't list anything and your handle doesn't seem to have much of an online footprint.
Lastly, I promise I'm not some weirdo, I'm a realperson™ -- just check my HN comment history. A lot of people in the AI community have met me in person and can confirm (swyx etc).
Look forward to chatting!
> a steady stream of shippable products
Software/web meat shops have bean around since the dawn of the time.
I worked at McDonald's in my teens. One of the best managers I ever worked for was the manager at this store at this time(the owner rotated him between stores to help get things on track).
I'll never forget this one thing he said: "They have changed the Filet-O-Fish five times since I've been here, and each time it's become more profitable".
Congrats on making slop more profitable.
LLM's are good at making stuff from scratch and perfect when you don't have to worry about the codes future. 'Research' can be a great tool. But LLMs are horrible in big codebases and multiple micro services. Also at making decision, never let it make a decision for you. You need to know what's happening and you can't ship straight AI code. It can save time, but it's not a lot and it won't replace anyone.
Are you saying this from experience?
We have a large monorepo at my company. You're right that for adding entirely new core concepts to an existing codebase we wouldn't give an AI some vague requirements and ask it to build something – but we wouldn't do that for a human engineer either. Typically we would discuss as a team and then once we've agreed on technologies and an approach someone will implement it relying heavily on AI to write the actual code (because it's faster and generally won't add dumb bugs like typos or conditional logic error).
Almost everything else at this point can be done by AI. Some stuff requires a little support from human engineers, but honestly our main bottlenecks at this point is just QA and getting the infra to a place where we can rapidly ship stuff into production.
> You need to know what's happening and you can't ship straight AI code.
I think there is some truth to this. We are struggling to maintain a high-level understanding of the code as a team right now, not because there is no human that understands, but because 5 years ago our team would have probably been 10-20x larger given the amount we're shipping. So when one engineer leaves the company or goes on holiday we find we lose significantly more context of systems than you historically would with larger teams of engineers. Previously you might have had 2-3 engineers who had a deep understanding of a single system. Now we have maybe 1-2 engineers who need to maintain understanding of 5-6 systems.
That said, AI helps a lot with this. Asking AI to explain code and help me learn how it works means I can pick up new systems significantly quicker.
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Doing things for your own use, where you are taking all the risks, is perfectly fine.
As soon as you try to sell it to me, you have a duty of care. You are not meeting that duty of care with this ignorant and reckless way of working.
Can it pre-emptively write the HN comment where someone says it utterly fails for them but no one else is able to reproduce?
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To the sceptics still saying that LLMs still can't solve "slime mold pathing algorithm and creating completely new shoe-lacing patterns" (literally a quote from a different comment here), please consider something we've learnt over and over again in history: good enough and cheap will destroy perfect but expensive.
And then cheap and good enough option will eventually get better because that's the one that is more used.
It's how Japanese manufacturing beat Western manufacturing. And how Chinese manufacturing then beat Japanese again.
It's why it's much more likely you are using the Linux kernel and not GNU hurd.
It's how digital cameras left traditional film based cameras in the dust.
Bet on the cheaper and good enough outcome.
Bet against it at your peril.
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Don't be curmudgeonly. Thoughtful criticism is fine, but please don't be rigidly or generically negative.
https://news.ycombinator.com/newsguidelines.html
I don't think you've used it. I used it intensely and mostly autonomously (with clear instructions, including how to measure good output) almost non-stop over the holidays. Its a new abstraction for programming -- it doesn't replace software developers, it gives them a more natural way to describe what they want.
> Re-building things that most probably already exist, simply with your own little special flavour?
That describes half of the current unicorn startups nowadays.
More than half. What has anyone written that was truly new? Regardless, if you have an idea, you will build it out of some combination of conditionals, loops, and expressions… turns out agents are pretty good at those things, even when the idea you’re expressing is novel.
This is a natural response to software enshittification. You can hardly find an iOS app that is not plagued by ads, subscriptions, or hostile data collection. Now you can have your own small utilities that can work for you. This sort of personal software might be very valuable in the world where you are expected to pay 5$ to click any button.
Yeah sure but have you considered that the actual cost of running these models is actually much greater than whatever cost you might be shelling out for the ad-free apps? You're talking to someone who hates the slopification and enshittification of everything, so you don't need to convince me about that. However, everything I've seen described in the replies to my initial comment - while cute, and potentially helpful on a case-by-case basis, does NOT warrant the amount of resources we are pouring into AI right now. Not even fucking close. It'll all come crashing down, taxpayers the world over will be caught with the bag in their hands, and for what? So that we can all have a less robust version of an app that already exists but that has the colours we want and the button where we want it?
If AI cost nothing and wasn't absolutely decimating our economy, I'd find what you've shared cute. However, we are putting literally all of our eggs, and the next generation's eggs, and the one after that, AND the one after that, into this one thing, which, I'm sorry, is so far away from everything that keeps on being promised to us that I can't help but feel extremely depressed.
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> none of this is going to improve people's lives.
I have some old borderline senile relatives writting apps (asking LLMs to write for it them) for their own personal use. Stuff they surely haven't done on their own (or had the energy to do). Their extent of programming background - shitty VBScript macros for excel.
It also helps people to pick up programming and helps with the initial push of getting started. Getting over the initial hump, getting something on the screen so to speak.
Most things people want from their computers are simple shit that LLMs usually manage quite well.
Good question whether or not this (outsourcing their thinking) actually just accelerates their senility or not.
As someone who likes to solve hard or interesting technical problems, I've long before LLMs often been disappointed that most of the time what people want from programmers is simple stupid shit (ie. stuff i dont find interesting to work on).
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When disagreeing, please reply to the argument instead of calling names.
https://news.ycombinator.com/newsguidelines.html