Comment by hollowturtle

Take it up with Anthropic. It's actually their billion-dollar TUI product you're commenting on.

The problem with being such a naysayer is that you're entirely disconnected from what's going on. You haven't tried an agent like Claude Code and experienced it for yourself, so you don't recognise what it looks like when it's in front of you.

That is the same fight the 2D animators were having with 3D aninmation 30 years ago. The resolution is likely to be the same: the tool wins but the fundamentals stay, and the line between competent and incompetent practitioners moves but does not disappear.

  > I don't want to offend (it's AI coded anyway :)) but that does not scream "high quality" to me.

Honestly, I think this is where the big divide is. People have massively different opinions on what "quality" is. Which is okay, but it feels like everyone is working under some assumption that quality is this very clear objective measure that we all agree on. Clearly we don't. We didn't before AI and well... if you can't tell that we don't with AI... you need to take a step back.

FWIW, I agree with Philip here. I don't think this screams "high quality" to me. I'm also not trying to take a shit on your project. Nothing screams "terrible" to me, but yeah, it does look a bit sloppy. There's no polish to it. It looks like someone that grades on "it works" and that's fine. But it also isn't everyone's cup of tea. Where the sloppiness comes in is like what Philip said. First thing I saw was the gif and well... I think Claude Code is sloppy. But this is also a great example at how and where LLMs visibly fail. Creating a box in text is pretty simple. There's tons of tools to do it. And the LLM 100% knows about characters like ⌜⌝⌞⌟⎜, it just doesn't use them and doesn't care. The code itself also looks very LLM generated.

It's fine and I don't think you have any reason to be ashamed of it, but I also wouldn't go around boasting that it is an example of high quality work too. And FWIW, I can't think of a single heavily LLM assisted code where I don't have similar feelings. I've seen stuff with more polish, but yeah, they feel off.

  > TUI

This is a space I feel weird in. I love the terminal. I love that there's a lot of new TUIs. But it also feels very weird because it is extremely clear that a lot of these new TUIs were written by people (or machines) that don't really have a lot of experience in the terminal itself. There's a real shared language by people like me who live in the cli. There's a reason people like me can pick up a new tool and guess certain flags and certain ways to use them. It's because of a shared design language that we know of and we end up writing that way because we know it reduces to cognitive load on our peers. But the LLMs? They don't have that shared experience.

I think this is true for a lot of stuff, not just TUIs or bash tools. Things just smell... off...

You do realize that you're complaining about the Claude Code TUI, right?

That's not what this product is; merely a tool it uses.

I think at this point there is no convincing people. Clearly there is value in these tools and it generates code when steered properly. Perhaps your struggles are down to a skill issue.

I haven't done any CSS/HTML/JS level work with Claude yet. I've mainly been using it for systems level stuff.

LLMs have traditionally had problems with visual rendering (the good ol' pelican on the bicycle test). I wonder if this is more of the same?

> I think the people doing the heaviest breathing around the elimination of programmers either aren't very good at programming, or they're not paying close attention.

Yeah, absolutely. People think you're picking on, like, code formatting and no, dawg, your code doesn't do what you think it does, or it only handles the happiest of happy paths.

I do find it funny when people get mad about you critiquing their AI project. You didn't even write it, dude.

Or they don’t know CSS.

Amazing how the LLM is godly with things I don’t understand, and falls over completely when it works in my domain… I wonder why that is /s

As a random example of a "hard" problem solved by AI that I couldn't have realistically done myself, despite having decades of wide industry experience:

Reverse engineering a proprietary protocol from a binary executable.

I heard about people finding security vulnerabilities in compiled code with the combination of Claude Mythos wired up to a disassembler like NSA's Ghidra. Someone here mentioned that GPT 5.5 "extra high" is just as capable, I had a problem to solve, spare token quota for the week, so... I gave it a go.

My problem was that I'm working with a product that uses a legacy 1990s style network appliance output log format that is proprietary, undocumented, and has no publicly available decoders other than an app by the same vendor, and that app has fundamental limitations. (I.e.: it's nothing like Splunk or Elastic.)

Codex with a Ghidra MCP bridge figured it all out: the framing, bit and byte packing, endian order, field names, data types, etc. It made me a neat little protocol parser in a modern language that I can use to spit out something sane like NDJSON or OTLP protobufs.

There is no way I could have reverse engineered this myself from compiled C++ code and/or packet captures! The format isn't self-describing and is incredibly dense (similar to NetFlow). In a hex viewer it looks like line noise!

This is a pretty wild take. What percentage of human engineers are creating novel solutions for hard problems, you think? I work in R&D and even my work is 90% doing things that other people already solved. If you are really doing cutting edge SOTA work that has never been done by another human in some form or another, then kudos to you and I want your job.

The comment was directed at:

> For generating production code even with a lot of steering and baby sitting? Absolutely not, not quite there not even close in my experience.

As I said, this is an example of using AI successfully to produce a high quality product (one that I use every day).

But to your point: I am solving hard problems that people really have. You just don't see those because I haven't mentioned them publicly yet. And they won't be released or talked about until they're ready.

Claude wrote me a little python script to help me sort and rank all the AI videos I've generated. It also extracted the metadata and organized it into a CSV. I sent it some hex dumps of the header and it got it first try. The header structure of webms generated by comfy are pretty novel.

> The frame of reference of what is considered 'production code' differs immensely between organizations, teams and people.

I think it’s really down to this. Nobody can agree on what counts as production-quality code. I remember joining a company with what I think (hope) most of us would call horrible quality code. It was an absolute mess, barely compiled with hundreds of warnings, and had uncountable number of bugs. They didn’t even have a bug tracker so nobody even knew how many they had.

But the people working there already were so proud of it! None of them had ever worked for another company so they had no idea how bad their code was in comparison with the rest of the software industry (which itself is a very low bar). I told the founder we had a huge code quality problem and he looked at me like I had horns growing out of my head.

When someone says their LLM is producing “production-quality” code, actually look at it and see. Arguing about it on HN is pointless because everyone’s quality bar is different.

Absolutely! I find its test generation, properly steered, to be top notch. In many ways it's like having a second head, because it'll spontaneously come up with test paths that I'd normally only get to after a month or so in one of my "aha! What about XYZ?" shower thoughts.

You'll also notice that Linus doesn't poo-poo AI at all. His only gripe is with people using it wrong, such as flooding security lists with drive-by security reports after pointing their agent to the code and saying "find me some VULNS!!1!1!!"

> The code I get from LLM's is usually much better than what I get from my peers

Then you should seriously question for who you're working for imo.

> It also isn't lazy.

It is indeed lazy in my experience, as in being overly zealous when creating useless test cases and ignoring the important ones. I don't want it to test a sum I want to know a test that can "guarantee" me that a further change doesn't break existing code. And producing this high quality in tests is HARD, and requires a lot of steering with agents. This culture of tests code coverage is just wrong, the best code base I worked with had code coverage only on the net percent of code that matters, the rest is covered by for static type checking and integration tests

I’d like people to notice that those who claim this amazing AI productivity boost are always: pushing out software they don’t know how to judge the quality of and pushing projects that are 70% done. Every. Single. Time.

I use Claude all the time, it is immensely helpful. It is also very nuanced and requires a high level of expertise in a specific domain to produce quality work. Even then, that take time and effort. Anyone saying otherwise, quite frankly, doesn’t know what they’re doing.

[dead]

> quality code

Probably where the mismatch is in this discussion. The measure of what is quality code is all over the place. For some, some form of "good enough" is quality. And for others, metrics like terseness, readability, vacuous amounts of comments, cleverness, various fuzzy measures of "idiomatic", etc, make "quality code" much more of a moving target.

I think this depends a lot on the task, the existing codebase, and the taste of the operator.

In general I tend to agree with you if you're talking a codebase you are deeply familiar with, the value-add from have agents write the code probably ranges from very small to negative in most cases.

On the other hand if you're trying to make changes in systems you are not familiar with, LLMs are a huge speed boost to folks with enough experience to sniff out what would be a bad path essentially via socratic method to the agent.

Obviously there are no silver bullets and no substitute for judgment. I will say though, I'll tradeoff ugly local code for good data models and interfaces any day of the week, and there is definitely an archetype of engineer that is very precious about code without good judgment on where it matters and where it doesn't.

I've quoted you two tools (Ghostty and Redis) whose development now regularly uses AI assistance to deliver production code. I quoted those because their authors shared their experiences, the strengths and the limits of the tooling.

There's many more, from Flask to Docker, from Ruby to FastAPI or Tanstack. LLVM has integrated AI-generated PRs, so did Swift and Mojo. Sasha Levin has pushed into Linux Nvidia-related kernel changes that were authored by LLMs in 6.15. You can be certain there's a magnitude more where people don't admit or tag their PRs as AI generated or co-generated.

In fact I am quite confident that projects and developers that are not leveraging the tools are increasingly rare. There's really no reason in 2026 to write a non-trivial PR and not ask a cheap review to an AI tool.

The industry is changing, I don't really like the trends I'm seeing, but to state that LLMs cannot and are not writing production code, very often quality ones, (especially when used, setup and overviewed properly) is plain denial.

Your anecdotal experience isn't relevant, especially when applied to the largest parts of the industry, composed of mediocre developers working on terrible codebases.

I'm not a native english speaker and when i mentioned that i might use LLM for fixing spellings, people argued about the use of LLM. So spelling error yes/no?

I do not understand the quote you rference at all tbh?

Firstly i wrote examples but also etc. so its more than just that. It is also refactoring, cicd pipelines and co.

2 years ago when I prompted something, it had compile time errors left and right. Took me 3-10 iterations to even get it running.

Now its one shoting a lot. Including websides, refactorings, etc.

The question is what is missing? How far are we that it can handle huge code bases vs. smaller ones? How far are we that it can comprehend the whole architecture and doesn't try to put a service in a wrong place just becaus the context is too small?

Mythos is 10 Trillion, that might be already pushing it.

95% might be not enough for someone in sense of "yeah i can't do the 95% and i can't do the 5% either the AI can do 100% or i still need Kevin with his knowledge even if its just for the last 5%"

I wrote coding job. And its true for coding jobs.

Your Product Manager is not a coding job. Your Product Owner is not a coding job.

vibe-kanban exists you could already do a proper experiment letting your PO maintain a vibe-kanban board with proper requirements and see how an agent progresses.

But 5% is often enough wwhat breaks it. Doesn't help much when your PM, PO or CEO or CTO have no clue about coding harnesses, coding agents, coding platforms, LLMs etc.

CEO makes fresh account to tell someone that writing code is not the entire job? I don’t buy it.

No, it would be contradicted by the sentiment among devs at top tech firms, but I don't know what the sentiment is. I do know they are being forced to use AI at peril of termination such that their use of AI is a non signal.

+1 to all of this. The challenge can be staying focused and thinking when the AI assistant is (1) moving very fast and (2) often times doing multiple things at the same time.

I know I have struggled to keep up, and fall into the trap of approving things (either commands or recommendations) without taking the time to really process and think about them.

It's a bit like the age old problem of "it's super easy to ask questions, and can be super hard to answer many of them". So the economy of the conversation gets out of whack fast.

From what I've heard this is a good assessment of steve's book.

Like I said most of my prompts cover 1-3 files at most, rarely more

In my experience most people with the type of critique I'm seeing from you have only tried it one time or have not taken the time to invest in an environment/process that will work for agentic coding.

My question is not so much about sharing a cherry picked example, but the question was more like "have you tried in earnest to make it work". That's the part that wasn't clear from your original post. But you say you have, and you weren't impressed. Fair enough. I'm not trying to convince you otherwise, but I encourage people to give the tools a fair chance before throwing up their hands and deciding it's meh.

Having said all that, you're right there isn't a shared experience.

We'll there's your problem.

F1 mechanic pops the hood of a mass-market Toyota Corolla and doesn't understand why everyone says it's really good.

A lot of us are out here building websites or phone apps.

Not to say that these things can't also be taken very seriously from first-principles, but I think that's rare.

I don't really see your point. Most problems that people have aren't really super-novel, but just extremely bespoke.

To give a specific example, 12 months ago I had a client pay me me to make a Chrome plugin that changed the rows in his Shopify Products page to display Quantity and SKU.

These days you'd just one-shot it in Claude.

I'm beginning to get the sense that Sturgeon's Law is at play here and the non-crap 10% of us are arguing with the 90% for whom LLM's shitty output is actually better than what they could do on their own.

I've been lucky enough to work at places with majority intelligent engineers with similar tastes on quality to my own... but it seems to be that's not the norm or the case everywhere.

and it's the 90% that's most vocal. Sturgeon and D-K seen to go hand-in-hand.

The obvious pushback to all of the slop is: coding was never hard. Learning resources were abundant and free.

If these people had a burning desire to build things prior to LLMs and couldn’t put in the effort to learn to build them (which is also fun!) then why would they ever put the effort into anything to understand it and make it good??

What would you consider a "hard" problem?

Give me a "hard problem" and I'll give you a Codex or Claude Code transcript showing how I'd use them to tackle it.

[flagged]