Comment by calrain
3 days ago
I've been using Claude Code heavily for about 3 months now, and I'm pretty sure I'm between 10 and 20 times more productive while using it.
How I measure performance is how many features I can implement in a given period of time.
It's nice that people have done studies and have opinions, but for me, it's 10x to 20x better.
I find the swings to be wild, when you win with it, you win really big. But when you lose with it, it's a real bite out of your week too. And I think 10x to 20x has to be figurative right, you can do 20x by volume maybe, but to borrow an expression from Steve Ballmer, that's like measuring an airplane by kilograms.
Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious? Even a machine that just handed you the perfect code can't 20x your real output, even if it gave you the source file at 20x your native sophistication you wouldn't be able to build and deploy it, let alone make changes to it.
But even if it's the last 5-20% after you're already operating at your very limit and trying to hit your limit every single day is massive, it makes a bunch of stuff on the bubble go from "not realistic" to "we did that".
There are definitely swings. Last night it took about 2 hours to get Monaco into my webpack built bootstrap template, it came down to CSS being mishandled and Claude couldn't see the light. I just pasted the code into ChatGPT o3 and it fixed it first try. I pasted the output of ChatGPT into Claude and viola, all done.
A key skill is to sense when the AI is starting to guess for solutions (no different to human devs) and then either lean into another AI or reset context and start over.
I'm finding the code quality increase greatly with the addition of the text 'and please follow best practices because will be pen tested on this!' and wow.. it takes it much more seriously.
Doesn't sound like you were writing actual functionality code, just integrating libraries?
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Is there a way to have two agentic AIs do pair programming?
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Let's be serious, what percentage of devs are doing "high complexity, high cognitive load, detail intense" work?
All of them, some just don’t notice, don’t care or don’t know this line of work is like that. Look at how junior devs work vs really experienced, self-aware engineers. The latter routinely solve problems the former didn’t know existed.
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> Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious?
How much of the code you write is actually like this? I work in the domain of data modeling, for me once the math is worked out majority of the code is "trivial". The kind of code you are talking about is maybe 20% of my time. Honestly, also the most enjoyable 20%. I will be very happy if that is all I would work on while rest of it done by AI.
Creatively thinking about what a client needs, how the architecture for that would be like, general systems thinking, UX etc. and seeing that come to live in a clean, maintainable way, that's what lights up my eyes. The minutiae of code implementation, not so much, that's just an implementation detail, a hurdle to overcome. The current crop of tooling helps with that tremendously, and for someone like me, it's been a wonderful time, a golden era. To the people who like to handcraft every line of code to perfection, people who derive their joy from that, I think they benefit a lot less.
> Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious?
When you zoom in, even this kind of work isn't uniform - a lot of it is still shaving yaks, boring chores, and tasks that are hard dependencies for the work that is truly cognitively demanding, but themselves are easy(ish) annoyances. It's those subtasks - and the extra burden of mentally keeping track of them - that sets the limit of what even the most skilled, productive engineer can do. Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.
> Even a machine that just handed you the perfect code can't 20x your real output, even if it gave you the source file at 20x your native sophistication you wouldn't be able to build and deploy it, let alone make changes to it.
Not true if you use it right.
You're probably following the "grug developer" philosophy, as it's popular these days (as well as "but think of the juniors!", which is the perceived ideal in the current zeitgeist). By design, this turns coding into boring, low-cognitive-load work. Reviewing such code is, thus, easier (and less demoralizing) than writing it.
20x is probably a bit much across the board, but for the technical part, I can believe it - there's too much unavoidable but trivial bullshit involved in software these days (build scripts, Dockerfies, IaaS). Preventing deep context switching on those is a big time saver.
When you zoom in, even this kind of work isn't uniform - a lot of it is still shaving yaks, boring chores, and tasks that are hard dependencies for the work that is truly cognitively demanding, but themselves are easy(ish) annoyances. It's those subtasks - and the extra burden of mentally keeping track of them - that sets the limit of what even the most skilled, productive engineer can do. Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.
Yeah, I'm not a dev but I can see why this is true, because it's also the argument I use in my job as an academic. Some people say "but your work is intellectually complex, how can you trust LLMs to do research, etc.?", which of course, I don't. But 80% of the job is not actually incrementally complex, it's routine stuff. These days I'm writing the final report of a project and half of the text is being generated by Gemini, when I write the data management plan (which is even more useless) probably 90% will be generated by Gemini. This frees a lot of time that I can devote to the actual research. And the same when I use it to polish a grant proposal, generate me some code for a chart in a paper, reformat a LaTeX table, brainstorm some initial ideas, come up with an exercise for an exam, etc.
Yes, things that get resolved very quickly with AI include fixing Linting errors, reorganizing CI pipelines, documenting agreed on requirements, building well documented commits, cleaning up temporary files used to validate dev work, building README.md's in key locations to describe important code aspects, implementing difficult but well known code, e.g. I got a trie security model implemented very quickly.
Tons of dev work is not exciting, I have already launched a solo dev startup that was acquired, and the 'fun' part of that coding was minimal. Too much was the scaffolding, CRUD endpoints, web forms, build scripts, endpoint documentation, and the true innovative stuff was such a small part of the whole project. Of the 14 months of work, only 1 month was truly innovative.
> Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.
Maybe, but I don't feel (of course, I could be wrong) that doing boring tasks take away any mental capacity; they feel more like fidgeting while I think. If a tool could do the boring things it may free my time to do other boring work that allows me to think - like doing the dishes - provided I don't have to carefully review the code.
Another issue (that I asked about yesterday [1]) is that seemingly boring tasks may end up being more subtle once you start coding them, and while I don't care too much about the quality of the code in the early iterations of the project, I have to be able to trust that whatever does the coding for me will come back and report any difficulties I hadn't anticipated.
> Reviewing such code is, thus, easier (and less demoralizing) than writing it.
That might well be true, but since writing it doesn't cost me much to begin with, the benefit might not be large. Don't get me wrong, I would still take it, but only if I could fully trust the agent to tell me what subtleties it encountered.
> there's too much unavoidable but trivial bullshit involved in software these days (build scripts, Dockerfies, IaaS). Preventing deep context switching on those is a big time saver.
If work is truly trivial, I'd like it to be automated by something that I can trust to do trivial work well and/or tell me when things aren't as trivial and I should pay attention to some detail I overlooked.
We can generally trust machines to either work reliably or fail with some clear indication. People might not be fully reliable, but we can generally trust them to report back with important questions they have or information they've learnt while doing the job. From the reports I've seen about using coding agents, they work like neither. You can neither trust them to succeed or fail reliably, nor can you trust them to come back with pertinent questions or information. Without either kind of trust, I don't think that "offloading" work to them would truly feel like offloading. I'm sure some people can work with that, but I think I'll wait until I can trust the agents.
[1]: https://news.ycombinator.com/item?id=44526048
Yeah, I don't fuck with Docker jank and cloud jank and shit. I don't fuck with dynamic linking. I don't fuck with lagged-ass electron apps. I don't fuck with package managers that need a SAT solver but don't have one. That's all going to be a hard no from me dawg.
When I said that after you've done all the other stuff, I was including cutting all the ridiculous bullshit that's been foisted on an entire generation of hackers to buy yachts for Bezos and shit.
I build clean libraries from source with correct `pkg-info` and then anything will build against it. I have well-maintained Debian and NixOS configurations that run on non-virtualized hardware. I use an `emacs` configuration that is built-to-specifications, and best-in-class open builds for other important editors.
I don't even know why someone would want a model spewing more of that garbage onto the road in front of them until you're running a tight, optimized stack to begin with, then the model emulates to some degree the things it sees, and they're also good.
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I cringe when I see these numbers. 20 times better means that you can accomplish in two months what you would do in 4 years, which is ridiculus when said out loud. We can make it even more ridiculous by pointing out you would do in 3 years the work of working lifetime (60 years)
I am wondering, what sort of tasks are you seeing these x20 boost?
It is amazing, cringe all you want :)
I scoped out a body of work and even with the AI assisting on building cards and feature documentation, it came to about 2 to 4 weeks to implement.
It was done in 2 days.
The key I've found with working as fast as possible is to have planning sessions with Claude Code and make it challenge you and ask tons of questions. Then get it to break the work into 'cards' (think Jira, but they are just .md files in your repo) and then maintain a todo.md and done.md file pair that sorts and organizes work flow.
Then start a new context, tell it to review todo.md and pick up next task, and burn through it, when done, commit and update todo.md and done.md, /compact and you're off on the next.
It's more than AI hinting at what to do, it's a whole new way of working with rigor and structure around it. Then you just focus fire on the next card, and the next, and if you ever think up new features, then card it up and put it in the work queue.
Did this 20x increase in productivity come with a 20x increase in salary? Do you clock off at Monday lunchtime and spend the rest of the week playing video games? Did your boss fire nineteen developers and give their jobs to you?
If one of these things isn’t true, you’re either a fool or those productivity increases aren’t real.
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You did not answer the question
You are extrapolating over years as if a programmer’s task list is consistent.
Claude code has made bootstrapping a new project, searching for API docs, troubleshooting, summarizing code, finding a GitHub project, building unit tests, refactoring, etc easily 20x faster.
It’s the context switching that is EXTREMELY expensive for a person, but costless for the LLM. I can focus on strategy (planning features) instead of being bogged down in lots of tactics (code warnings, syntax errors).
Claude Code is amazing, but the 20x gains aren’t evenly distributed. There are some projects that are too specialized (obscure languages, repos larger than the LLM’s context window, concepts that aren’t directly applicable to any codebase in their training corpus, etc). But for those of us using common languages and commodity projects, it’s a massive force multiplier.
I built my second iOS app (Swift) in about 3 days x 8 hours of vibe coding. A vocab practice app with adjustable learning profile, 3 different testing mechanisms, gamification (awards, badges), iOS notifications, text to speech, etc. My first iOS app was smaller, mostly a fork of another app, and took me 4 weeks of long days. 20x speed up with Claude Code is realistic.
And it saves even more time when researching + planning which features to add.
> in two months what you would do in 4 years
There should be a FOSS project explosion if those numbers were true by now. Commercial products too.
Claude Code was released 4 months ago, agebtic coding in general really came into being earlier this year. Maybe give it a minute?
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Maybe writing made up HN comments?
I am honestly convinced these are AI comments. They fail to answer the question of what sort of work they see the x20 improvements, just like Chatgpt fails to answer my hard technical questions.
It isn’t ridiculous, it’s easily true, especially when you’re experienced in general, but have little to no knowledge of this particular big piece of tech, like say you’ve stopped doing frontend when jquery was all there was and you’re coming back. I’m doing things with react in hours I would have no business doing in weeks a couple years ago.
I am waiting to see your 4 year human-equivalent project in a couple of months.
Words without actions are junk. You are asserting something you have no proof for. Proove it then. Amaze us all with your productivity, out in the open. Shred those pilled up open issues on open source projects and then give us a report of how fast-easy it.
If it is "easily true" you'll be done by next month
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I agree I feel more productive. AI tools do actually make it easier and makes my brain use less energy. You would think that would be more productive but maybe it just feels that way.
Stage magicians say that the magic is done in the audiences memory after the trick is done. It's the effect of the activity.
AI coding tools makes developers happier and able to spend more brain power on actually difficult things. But overall perhaps the amount of work isn't in orders of magnitudes it just feels like it.
Waze the navigation app routes you in non standard routes so that you are not stuck in traffic, so it feels fast that you are making progress. But the time taken may be longer and the distance travelled may be further!
Being in stuck traffic and not moving even for a little bit makes you feel that time has stopped, it's boring and frustrating. Now developers need never be stuck. Their roads will be clear, but they may take longer routes.
We get little boosts of dopamine using AI tools to do stuff. Perhaps we used these signals as indicators of productivity "Ahh that days work felt good, I did a lot"
> Waze the navigation app routes you in non standard routes so that you are not stuck in traffic, so it feels fast that you are making progress. But the time taken may be longer and the distance travelled may be further!
You're not "stuck in traffic", you are the traffic. If the app distributes users around and this makes it so they don't end up in traffic jams, it's effectively preventing traffic jams from forming
I liked your washing machine vs. sink example that I see you just edited out. The machine may do it slower and less efficiently than you'd do in the sink, but the machine runs in parallel, freeing you to do something else. So is with good use of LLMs.
Yeah I totally agree. It's like washing by hand vs using a mangle possibly. The metaphor of agents to machines was also what I thought but didn't write as it's about companion tools mainly. (I got confused and put in a high level comment but somehow didn't actually post that!)
For Waze, even if you are traffic and others go around you, you still may get there quicker and your car use less energy than taking the suggested route that feels faster. Others may feel happier and feel like they were faster though. Indeed they were faster but might have taken a longer journey.
Also, generally most people don't use the app around here to effect significant road use changes. But if they did im not sure (but I'm having fun trying to think) what metaphor we can apply to the current topic :)
> on actually difficult things
Can't help but note that in 99% cases this "difficult things" trope makes little sense. In most jobs, the freed time is either spent on other stupid tasks or is lost due to org inefficiencies, or is just procrastinated.
> AI coding tools makes developers happier and able to spend more brain power on actually difficult things
Please don't speak for all developers when you say stuff like this
AI coding tools make me miserable to use
Where I have found Claude most helpful is on problems with very specific knowledge requirements.
Like: Why isn’t this working? Here Claude read this like 90 page PDF and tell me where I went wrong interfacing with this SDK.
Ohh I accidentally passed async_context_background_threading_safe instead of async_context_thread_safe_poll and it’s so now it’s panicking. Wow that would have taken me forever.
I can’t believe such numbers. If this was true why don’t you quit your job and vibe code 10 ios apps
I wish I could. Some problems are difficult to solve and I still need to pay the bills.
So I work 8 hours a day (to get money to eat) and code another 4 hours at home at night.
Weekends are both 10 hour days, and then rinse / repeat.
Unfortunately some projects are just hard to do and until now, they were too hard to attempt to solve solo. But with AI assistance, I am literally moving mountains.
The project may still be a failure but at least it will fail faster, no different to the pre-AI days.
Can't you use your AI skills to work only 1 hour? Or is the 8 hours you work already 10xed by AI?
I don't think you are understanding how big 10x and 20x are.
It means you can replace a whole team of developers alone.
I can believe that some tasks are speed up by 10x or even 20x, but I find very hard to believe it's the average of your productivity (maintaining good code quality)
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You're getting 6 months worth of work done in a week?
I bet with a co-worker that a migration from angular 15 to angular 19 could be done really fast avoiding months. I spent a whole evening on it and Claude code have never been able to pull off a migration from 15 to 16 on its own. A total waste of time and nothing worked. I had the surprise that it cost me 275$ for nothing. So maybe for greenfield projects it’s smooth and saves time but it’s not a silver bullet on projects with problems.
I've had a lot of issues with Claude and web development.
I ended up asking it how it wanted to work and would an 'AdminKit Template' work to get things moving.
It recommended AdminKit and that was a good move.
For me, custom UI's aren't a big part of the solution, I just need web pages to manage CRUD endpoints to manage the product.
AdminKit has been a good fit so far, but it was a fresh start, no migration.
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> it cost me 275$ for nothing
Recently, there was story about developer who was able to crush interview and got parallel full-time jobs in several start-ups. Initially he was able to deliver but then not so much.
Somehow your case is reminding this to me, where AI is this overemployed developer.
For the sake of argument 20x means you have basically suddenly got access to 19 people with the same skill set as you.
You can build a new product company with 20 people. Probably in the same domain as you are in right now.
Output doesn't necessarily scale linearly with as you add more people. Look up mythical man.
I'm between 73 and 86 times more productive using claude code. You're not using it well.
Those are rookie numbers, you gotta pump those numbers up.
Can you show some of those problems and their solutions?
> How I measure performance is how many features I can implement in a given period of time.
When a measure becomes a target, it ceases to be a good measure.
> I'm pretty sure
So were the people taking the study. Which is why we do these, to understand where our understanding of ourselves is lacking.
Maybe you are special and do get extra gains. Or maybe you are as wrong about yourself as everyone else and are overestimating the gains you think you have.
Have any open source work you can show off?
Not the OP, but:
https://repo.autonoma.ca/notanexus.git
I don't know the PDF.js library. Writing both the client- and server-side for a PDF annotation editor would have taken 60 hours, maybe more. Instead, a combination Copilot, DeepSeek, Claude, and Gemini yielded a working prototype in under 6 hours:
https://repo.autonoma.ca/notanexus.git/tree/HEAD/src/js
I wrote maybe 3 lines of JavaScript, the rest was all prompted.
> Writing both the client- and server-side for a PDF annotation editor would have taken 60 hours, maybe more.
How do you know? Seems to me you’re making the exact same estimation mistake of the people in the study.
> Instead, a combination Copilot, DeepSeek, Claude, and Gemini yielded a working prototype in under 6 hours
Six hours for a prototype using four LLMs? That is not impressive, it sounds insane and a tremendous mess that will take so long to dig out of the prototype stage it’ll effectively require a rewrite.
And why are you comparing an LLM prototype to a finished product “by hand” (I surely hope you’re not suggesting such a prototype would take sixty hours)? That is disingenuous and skewing the numbers.
Unfortunately not, but ensuring the final code quality will be well written is a challenge I am putting off for now.
I'm leaning into the future growth of AI capabilities to help me here, otherwise I'll have to do it myself.
That is a tomorrow problem, too much project structure/functionality to get right first.
So you are more productive, as long as you don't have to consider code quality.
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Same, I’ve done stuff that should have taken me 2-3 weeks in days
I’ve done this without AI. The thing was not as hard as I thought it would be.
Same, often a missing feature or library had become available since I last looked into a subject.
I have exactly the same experience.
You're only getting 10x to 20x more productive? For me it's more like 10,000x to 50,000x, at minimum. YMMV.