I worry about the "brain atrophy" part, as I've felt this too. And not just atrophy, but even moreso I think it's evolving into "complacency".
Like there have been multiple times now where I wanted the code to look a certain way, but it kept pulling back to the way it wanted to do things. Like if I had stated certain design goals recently it would adhere to them, but after a few iterations it would forget again and go back to its original approach, or mix the two, or whatever. Eventually it was easier just to quit fighting it and let it do things the way it wanted.
What I've seen is that after the initial dopamine rush of being able to do things that would have taken much longer manually, a few iterations of this kind of interaction has slowly led to a disillusionment of the whole project, as AI keeps pushing it in a direction I didn't want.
I think this is especially true if you're trying to experiment with new approaches to things. LLMs are, by definition, biased by what was in their training data. You can shock them out of it momentarily, whish is awesome for a few rounds, but over time the gravitational pull of what's already in their latent space becomes inescapable. (I picture it as working like a giant Sierpinski triangle).
I want to say the end result is very akin to doom scrolling. Doom tabbing? It's like, yeah I could be more creative with just a tad more effort, but the AI is already running and the bar to seeing what the AI will do next is so low, so....
It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.
This would be fine if not for one thing: the meta-skill of learning to use the LLM depreciates too. Today's LLM is gonna go away someday, the way you have to use it will change. You will be on a forever treadmill, always learning the vagaries of using the new shiny model (and paying for the privilege!)
I'm not going to make myself dependent, let myself atrophy, run on a treadmill forever, for something I happen to rent and can't keep. If I wanted a cheap high that I didn't mind being dependent on, there's more fun ones out there.
> let myself atrophy, run on a treadmill forever, for something
You're lucky to afford the luxury not to atrophy.
It's been almost 4 years since my last software job interview and I know the drills about preparing for one.
Long before LLMs my skills naturally atrophy in my day job.
I remember the good old days of J2ME of writing everything from scratch. Or writing some graph editor for universiry, or some speculative, huffman coding algorithm.
That kept me sharp.
But today I feel like I'm living in that netflix series about people being in Hell and the Devil tricking them they're in Heaven and tormenting them: how on planet Earth do I keep sharp with java, streams, virtual threads, rxjava, tuning the jvm, react, kafka, kafka streams, aws, k8s, helm, jenkins pipelines, CI-CD, ECR, istio issues, in-house service discovery, hierarchical multi-regions, metrics and monitoring, autoscaling, spot instances and multi-arch images, multi-az, reliable and scalable yet as cheap as possible, yet as cloud native as possible, hazelcast and distributed systems, low level postgresql performance tuning, apache iceberg, trino, various in-house frameworks and idioms over all of this?
Oh, and let's not forget the business domain, coding standards, code reviews, mentorships and organazing technical events.
Also, it's 2026 so nobody hires QA or scrum masters anymore so take on those hats as well.
So LLMs it is, the new reality.
This is a very good point. Years ago working in a LAMP stack, the term LAMP could fully describe your software engineering, database setup and infrastructure. I shudder to think of the acronyms for today's tech stacks.
Ya I agree it's totally crazy.... but, do most app deployments need even half that stuff? I feel like most apps at most companies can just build an app and deploy it using some modern paas-like thing.
> I feel like most apps at most companies can just build an app and deploy it using some modern paas-like thing.
Most companies (in the global, not SV sense) would be well served by an app that runs in a Docker container in a VPS somewhere and has PostgreSQL and maybe Garage, RabbitMQ and Redis if you wanna get fancy, behind Apache2/Nginx/Caddy.
But obviously that’s not Serious Business™ and won’t give you zero downtime and high availability.
Though tbh most mid-size companies would also be okay with Docker Swarm or Nomad and the same software clustered and running behind HAProxy.
But that wouldn’t pad your CV so yeah.
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> I happen to rent and can't keep
This is my fear - what happens if the AI companies can't find a path to profitability and shut down?
Businesses too. For two years it's been "throw everything into AI." But now that shit is getting real, are they really feeling so coy about letting AI run ahead of their engineering team's ability to manage it? How long will it be until we start seeing outages that just don't get resolved because the engineers have lost the plot?
From what I am seeing, no one is feeling coy simply because of the cost savings that management is able to show the higher-ups and shareholders. At that level, there's very little understanding of anything technical and outages or bugs will simply get a "we've asked our technical resources to work on it". But every one understands that spending $50 when you were spending $100 is a great achievement. That's if you stop and not think about any downsides. Said management will then take the bonuses and disappear before the explosions start with their resume glowing about all the cost savings and team leadership achievements. I've experienced this first hand very recently.
Of all the looming tipping points whereby humans could destroy the fabric of their existence, this one has to be the stupidest. And therefore the most likely.
How long until “the LLM did it it” is just as effective as “AWS is down, not my fault”?
Never because the only reason that works with Amazon is that everyone is down at the exact same time.
If you’re just a gladhander for an algorithm, what are you really needed for?
This to me is the point.. LLMs can't be responsible for things. It sits with a human.
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I assume you're living in a city. You're already renting out a lot of things to others (security, electricity, water, food, shelter, transportation), what is different with white collar work?
>the city gets destroyed
vs.
>a company goes bankrupt or pivots
I can see a few differences.
I have deliberately moderated my use of AI in large part for this reason. For a solid two years now I've been constantly seeing claims of "this model/IDE/Agent/approach/etc is the future of writing code! It makes me 50x more productive, and will do the same for you!" And inevitabely those have all fallen by the wayside and been replaced by some new shiny thing. As someone who doesn't get intrinsic joy out of chasing the latest tech fad I usually move along and wait to see if whatever is being hyped really starts to take over the world.
This isn't to say LLMs won't change software development forever, I think they will. But I doubt anyone has any idea what kind of tools and approaches everyone will be using 5 or 10 years from now, except that I really doubt it will be whatever is being hyped up at this exact moment.
HN is where I keep hearing the “50× more productive” claims the most.
I’ve been reading 2024 annual reports and 2025 quarterlies to see whether any of this shows up on the other side of the hype.
So far, the only company making loud, concrete claims backed by audited financials is Klarna and once you dig in, their improved profitability lines up far more cleanly with layoffs, hiring freezes, business simplification, and a cyclical rebound than with Gen-AI magically multiplying output. AI helped support a smaller org that eliminated more complicated financial products that have edge cases, but it didn’t create a step-change in productivity.
If Gen-AI were making tech workers even 10× more productive at scale, you’d expect to see it reflected in revenue per employee, margins, or operating leverage across the sector.
We’re just not seeing that yet.
> If Gen-AI were making tech workers even 10× more productive at scale, you’d expect to see it reflected in revenue per employee, margins, or operating leverage across the sector.
If we’re even just talking a 2x multiplier, it should show up in some externally verifiable numbers.
I have friends who make such 50x productivity claims. They are correct if we define productivity as creating untested apps and games and their features that will never ship --- or be purchased, even if they were to ship. Thus, “productivity” has become just another point of contention.
> It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.
I agree with the sentiment but I would have framed it differently. The LLM is a tool, just like code completion or a code generator. Right now we focus mainly on how to use a tool, the coding agent, to achieve a goal. This takes place at a strategic level. Prior to the inception of LLMs, we focused mainly on how to write code to achieve a goal. This took place at a tactical level, and required making decisions and paying attention to a multitude of details. With LLMs our focus shifts to a higher-level abstraction. Also, operational concerns change. When writing and maintaining code yourself, you focus on architectures that help you simplify some classes of changes. When using LLMs, your focus shifts to building context and aiding the model effectively implement their changes. The two goals seem related, but are radically different.
I think a fairer description is that with LLMs we stop exercising some skills that are only required or relevant if you are writing your code yourself. It's like driving with an automatic transmission vs manual transmission.
Previous tools have been deterministic and understandable. I write code with emacs and can at any point look at the source and tell you why it did what it did. But I could produce the same program with vi or vscode or whatever, at the cost of some frustration. But they all ultimately transform keystrokes to a text file in largely the same way, and the compiler I'm targeting changes that to asm and thence to binary in a predictable and visible way.
An LLM is always going to be a black box that is neither predictable nor visible (the unpredictability is necessary for how the tool functions; the invisibility is not but seems too late to fix now). So teams start cargo culting ways to deal with specific LLMs' idiosyncrasies and your domain knowledge becomes about a specific product that someone else has control over. It's like learning a specific office suite or whatever.
> An LLM is always going to be a black box that is neither predictable nor visible (the unpredictability is necessary for how the tool functions; the invisibility is not but seems too late to fix now)
So basically, like a co-worker.
That's why I keep insisting that anthropomorphising LLMs is to be embraced, not avoided, because it gives much better high-level, first-order intuition as to where they belong in a larger computing system, and where they shouldn't be put.
> So basically, like a co-worker.
Arguably, though I don't particularly need another co-worker. Also co-workers are not tools (except sometimes in the derogatory sense).
Sort of except it seems the more the co-worker does the job it atrophies my ability to understand.. So soon we'll all be that annoyingly ignorant manager saying, "I don't know, I want the button to be bigger". Yay?
> and can at any point look at the source and tell you why it did what it did
Even years later? Most people can’t unless there’s good comments and design. Which AI can replicate, so if we need to do that anyway, how is AI specially worse than a human looking back at code written poorly years ago?
I mean, Emacs's oldest source files are like 40 years old at this point, and yes they are in fact legible? I'm not sure what you're asking -- you absolutely can (and if you use it long enough, will) read the source code of your text editor.
Well especially the lisp parts!
The little experience I have with LLM confidently shows that LLMs are much better at navigating and modifying a well structured code base. And they struggle, sometimes to a point where they can't progress at all, if tasked to work on bad code. I mean, the kind of bad you always get after multiple rounds of unsupervised vibe coding.
I think I should write more about but I have been feeling very similar. I've been recently exploring using claude code/codex recently as the "default", so I've decided to implement a side project.
My gripe with AI tools in the past is that the kind of work I do is large and complex and with previous models it just wasn't efficient to either provide enough context or deal with context rot when working on a large application - especially when that application doesn't have a million examples online.
I've been trying to implement a multiplayer game with server authoritative networking in Rust with Bevy. I specifically chose Bevy as the latest version was after Claude's cut off, it had a number of breaking changes, and there aren't a lot of deep examples online.
Overall it's going well, but one downside is that I don't really understand the code "in my bones". If you told me tomorrow that I had optimize latency or if there was a 1 in 100 edge case, not only would I not know where to look, I don't think I could tell you how the game engine works.
In the past, I could not have ever gotten this far without really understanding my tools. Today, I have a semi functional game and, truth be told, I don't even know what an ECS is and what advantages it provides. I really consider this a huge problem: if I had to maintain this in production, if there was a SEV0 bug, am I confident enough I could fix it? Or am I confident the model could figure it out? Or is the model good enough that it could scan the entire code base and intuit a solution? One of these three questions have to be answered or else brain atrophy is a real risk.
I'm worried about that too. If the error is reproducible, the model can eventually figure it out from experience. But a ghost bug that I can't pattern? The model ends up in a "you're absolutely right" loop as it incorrectly guesses different solutions.
Are ghost bugs even real?
My first job had the Devs working front-line support years ago. Due to that, I learnt an important lessons in bug fixing.
Always be able to re-create the bug first.
There are no such thing as ghost bugs, you just need to ask the reporter the right questions.
Unless your code is multi-threaded, to which I say, good luck!
Historically I would have agreed with you. But since the rise of LLM-assisted coding, I've encountered an increasing number of things I'd call clear "ghost bugs" in single threaded code. I found a fun one today where invoking a process four times with a very specific access pattern would cause a key result of the second invocation to be overwritten. (It is not a coincidence, I don't think, that these are exactly the kind of bugs a genAI-as-a-service provider might never notice in production.)
I ran into similar issues with context rot on a larger backend project recently. I ended up writing a tool that parses the AST to strip out function bodies and only feeds the relevant signatures and type definitions into the prompt.
It cuts down the input tokens significantly which is nice for the monthly bill, but I found the main benefit is that it actually stops the model from getting distracted by existing implementation details. It feels a bit like overengineering but it makes reasoning about the system architecture much more reliable when you don't have to dump the whole codebase into the context window.
> I've been trying to implement a multiplayer game with server authoritative networking in Rust with Bevy. I specifically chose Bevy as the latest version was after Claude's cut off, it had a number of breaking changes, and there aren't a lot of deep examples online.
I am interested in doing something similar (Bevy. not multiplayer).
I had the thought that you ought be able to provide a cargo doc or rust-analyzer equivalent over MCP? This... must exist?
I'm also curious how you test if the game is, um... fun? Maybe it doesn't apply so much for a multiplayer game, I'm thinking of stuff like the enemy patterns and timings in a soulslike, Zelda, etc.
I did use ChatGPT to get some rendering code for a retro RCT/SimCity-style terrain mesh in Bevy and it basically worked, though several times I had to tell it "yeah uh nothing shows up", at which point is said "of course! the problem is..." and then I learned about mesh winding, fine, okay... felt like I was in over my head and decided to go to a 2D game instead so didn't pursue that further.
>I had the thought that you ought be able to provide a cargo doc or rust-analyzer equivalent over MCP? This... must exist?
I've found that there are two issues that arise that I'm not sure how to solve. You can give it docs and point to it and it can generally figure out syntax, but the next issue I see is that without examples, it kind of just brute forces problems like a 14 year old.
For example, the input system originally just let you move left and right, and it popped it into an observer function. As I added more and more controls, it began to litter with more and more code, until it was ~600 line function responsible for a large chunk of game logic.
While trying to parse it I then had it refactor the code - but I don't know if the current code is idiomatic. What would be the cargo doc or rust-analyzer equivalent for good architecture?
Im running into this same problem when trying to claude code for internal projects. Some parts of the codebase just have really intuitive internal frameworks and claude code can rip through them and provide great idiomatic code. Others are bogged down by years of tech debt and performance hacks and claude code can't be trusted with anything other than multi-paragraph prompts.
>I'm also curious how you test if the game is, um... fun?
Lucky enough for me this is a learning exercise, so I'm not optimizing for fun. I guess you could ask claude code to inject more fun.
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> Eventually it was easier just to quit fighting it and let it do things the way it wanted.
I wouldn't have believed it a few tears ago if you told me the industry would one day, in lockstep, decide that shipping more tech-debt is awesome. If the unstated bet doesn't pay off, that is, AI development will outpace the rate it generates cruft, then there will be hell to pay.
As someone who's been commissioned many times before to work on or salvage "rescue projects" with huge amounts of tech debt, I welcome that day. Still not there yet though I am starting to feel the vibes shifting.
This isn't anything new of course. Previously it was with projects built by looking for the cheapest bidder and letting them loose on an ill-defined problem. And you can just imagine what kind of code that produced. Except the scale is much larger.
My favorite example of this was a project that simply stopped working due to the amount of bugs generated from layers upon layers of bad code that was never addressed. That took around 2 years of work to undo. Roughly 6 months to un-break all the functionality and 6 more months to clean up the core and then start building on top.
Are you not worried that the sibling comment is right and the solution to this will be "more AI" in the future? So instead of hiring a team of human experts to cleanup, management might just dump more money into some specialized AI refactoring platform or hire a single AI coordinator... Or maybe they skip to rebuild using AI faster, because AI is good at greenfield. Then they only need a specialized migration AI to automate the regular switchovers.
I used to be unconcerned, but I admit to be a little frightened of the future now.
Well, in general worrying about the future is not useful. Regardless of what you think, it is always uncertain. I specifically stay away from taking part in such speculative threads here on HN.
What's interesting to me though is that very similar promises were being made about AI in the 80s. Then came the "AI Winter" after the hype cycle and promises got very far from reality. Generative AI is the current cycle and who knows, maybe it can fulfill all the promises and hype. Or maybe not.
There's a lot of irrationality currently and until that settles down, it is difficult to see what is real and useful and what is smoke and mirrors.
Don't worry. This will create the demand for even more powerful models that are able to untangle the mess created by previous models.
Once we realize the kind of mess _those_ models created, well, we'll need even more capable models.
It's a variation on the theme of Kernighan insight about the more "clever" you are while coding the harder it will be to debug.
EDIT: Simplicity is a way out but it's hard under normal circumstances, now with this kind of pressure to ship fast because the colleague with the AI chimp can outperform you, aiming at simplicity will require some widespread understanding
"That's the brilliant part: when the winter comes the apes freeze to death!"
> ... few tears ago
Brilliant. Even if it was a typo.
> unstated bet
(except where it's been stated, championed, enforced, and ultimated in no unequivocal terms by every executive in the tech industry)
I'm yet to encounter an AI-bull who admits the LLM tendency towards creating tech debt- outside of footnotes stating it can be fixed by better prompting (with no examples), or solved by whatever tool they are selling
The industry decided that decades ago. We may like to talk about quality and forethought, but when you actually go to work, you quickly discover it doesn't matter. Small companies tell you "we gotta go fast", large companies demand clear OKRs and focusing on actually delivering impact - either way, no one cares about tech debt, because they see it as unavoidable fact of life. Even more so now, as ZIRP went away and no one can afford to pay devs to polish the turd ad infinitum. The mantra is, ship it and do the next thing, clean up the old thing if it ever becomes a problem.
And guess what, I'm finally convinced they're right.
Consider: it's been that way for decades. We may tell ourselves good developers write quality code given the chance, but the truth is, the median programmer is a junior with <5 years of experience, and they cannot write quality code to save their life. That's purely the consequence of rapid growth of software industry itself. ~all production code in the past few decades was written by juniors, it continues to be so today; those who advance to senior level end up mostly tutoring new juniors instead of coding.
Or, all that put another way: tech debt is not wrong. It's a tool, a trade-off. It's perfectly fine to be loaded with it, if taking it lets you move forward and earn enough to afford paying installments when they're due. Like with housing: you're better off buying it with lump payment, or off savings in treasury bonds, but few have that money on hand and life is finite, so people just get a mortgage and move on.
--
Edited to add: There's a silver lining, though. LLMs make tech debt legible and quantifiable.
LLMs are affected by tech debt even more than human devs are, because (currently) they're dumber, they have less cognitive capability around abstractions and generalizations[0]. They make up for it by working much faster - which is a curse in terms of amplifying tech debt, but also a blessing, because you can literally see them slowing down.
Developer productivity is hard to measure in large part because the process is invisible (happens in people's heads and notes), and cause-and-effect chains play out over weeks or months. LLM agents compress that to hours to days, and the process itself is laid bare in the chat transcript, easy to inspect and analyze.
The way I see it, LLMs will finally allow us to turn software development at tactical level from art into an engineering process. Though it might be too late for it to be of any use to human devs.
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[0] - At least the out-of-distribution ones - quirks unique to particular codebase and people behind it.
I ran into a new problem today: "reading atrophy".
As in if the LLM doesn't know about it, some devs are basically giving up and not even going to RTFM. I literally had to explain to someone today how something works by...reading through the docs and linking them the docs with screenshots and highlighted paragraphs of text.
Still got push back along the lines of "not sure if this will work". It's. Literally. In. The. Docs.
That's not really a new thing now, it just shows differently.
15 years ago I was working in an environment where they had lots of Indians as cheap labour - and the same thing will show up in any environment where you go for hiring a mass of cheap people while looking more at the cost than at qualifications: You pretty much need to trick them into reading stuff that are relevant.
I remember one case where one had a problem they couldn't solve, and couldn't give me enough info to help remotely. In the end I was sitting next to them, and made them read anything showing up on the screen out loud. Took a few tries where they were just closing dialog boxes without reading it, but eventually we had that under control enough that they were able to read the error messages to me, and then went "Oh, so _that's_ the problem?!"
Overall interacting with a LLM feels a lot like interacting with one of them back then, even down to the same excuses ("I didn't break anything in that commit, that test case was never passing") - and my expectation for what I can get out of it is pretty much the same as back then, and approach to interacting with it is pretty similar. It's pretty much an even cheaper unskilled developer, you just need to treat it as such. And you don't pair it up with other unskilled developers.
The mere existence of the phrase "RTFM" shows that this phenomenon was already a thing. LLMs are the worst thing to happen to people who couldn't read before. When HR type people ask what my "superpower" is I'm so tempted to say "I can read", because I honestly feel like it's the only difference between me and people who suck at working independently.
I've been thinking along these lines. LLMs seem to have arrived right when we were all getting addicted to reels/tic tocks/whatever. For some reason we love to swipe, swipe, swipe, until we get something funny/interesting/shocking, that gives us a short-lasting dopamine hit (or whatever chemicals it is) that feels good for about 1 second, and we want MORE, so we keep swiping.
Using an LLM is almost exactly the same. You get the occasional, "wow! I've never seen it do that before!" moments (whether that thing it just did was even useful or not), get a short hit of feel goods, and then we keep using it trying to get another hit. It keeps providing them at just the right intervals for people to keep them going just like they do with tick tock
My disillusionment comes from the feeling I am just cosplaying my job. There is nothing to distinguish one cosplayer from another. I am just doordashing software, at this point, and I'm not in control.
What kind of software are you writing? Are you just a "code monkey" implementing perfectly described Jira tickets (no offense meant)? I cannot imagine feeling this way with what I'm working on, writing code is just a small part of it, most of the time is spent trying to figure out how to integrate the various (undocumented and actively evolving) external services involved together in a coherent, maintainable and resilient way. LLMs absolutely cannot figure this out themselves, I have to figure it out myself and then write it all in its context, and even then it mostly comes up with sub-par, unmaintainable solutions if I wasn't being precise engouh.
They are amazing for side projects but not for serious code with real world impact where most of the context is in multiple people's head.
I don’t get this at all. I’m using LLM’s all day and I’m constantly having to make smart architectural choices that other less experienced devs won’t be making. Are you just prompting and going with whatever the initial output is, letting the LLM make decisions? Every moderately sized task should start with a plan, I can spend hours planning, going off and thinking, coming back to the plan and adding/changing things, etc. Sometimes it will be days before I tell the LLM to “go”. I’m also constantly optimising the context available to the LLM, and making more specific skills to improve results. It’s very clear to me that knowledge and effort is still crucial to good long term output… Not everyone will get the same results, in fact everyone is NOT getting the same results, you can see this by reading the wildly different feedback on HN. To some LLM’s are a force multiplier while others claim they can’t get a single piece of decent output…
I think the way you’re using these tools that makes you feel this way is a choice. You’re choosing to not be in control and do as little as possible.
Exactly.
Once you start using it intelligently, the results can be really satisfying and helpful.
People complaining about 1000 lines of codes being generated? Ask it to generate functions one at a time and make small implementations.
People complaining about having to run a linter? Ask it to automatically run it after each code execution.
People complaining about losing track? Have it log every modifications in a file.
I think you get my point. You need to treat it as a super powerful tool that can do so many things that you have to guide it if you want to have a result that conforms to what you have in mind.
One challenge is, are those decisions making tangible differences?
We won't know until the code being produced especially greenfields hits any kind of maturity 5 years+ atleast?
It's not that challenging, the answer is, it depends.
It's like a junior dev writing features for a product everyday vs a principle engineer. The junior might be adding a feature with O(n^2) performance while principle has seen this before and writes it O(log n).
If the feature never reaches significance, the "better" solution doesn't matter, but it might!
The principle may write once and it is solid and never touched, but the junior might be good enough to never need coming back to, same with a llm and the right operator.
What? Of course it makes a difference when I direct it away from a bad solution towards a good solution. I know as soon as I review the output and it has done what I asked, or it hasn't and I make a correction. Why would I need to wait 5 years? That makes no sense, I can see the output.
If you're using LLM's and you don't know what good/bad output looks like then of course you're going to have problems, but such a person would have the same problems without the LLM...
I find the atrophy and zoning out or context switching problematic, because it takes a few seconds/ minutes in "thinking" and then BAM! I have 500 lines of all sorts of buggy and problematic code to review and get a sycophantic, not-enough-mature entity to correct.
At some point, I find myself needing to disconnect out of overwhelm and frustration. Faster responses isn't necessarily better. I want more observability in the development process so that I can be a party to it. I really have felt that I need to orchestrate multiple agents working in tandem, playing sort of a bad-cop, good-cop and a maybe a third trying to moderate that discussion and get a fourth to effectively incorporate a human in the mix. But that's too much to integrate in my day job.
I’ve actually found the tool that inspires the most worry about brain atrophy to be Copilot. Vscode is full of flashing suggestions all over. A couple days ago, I wanted to write a very quick program, and it was basically impossible to write any of it without Copilot suggesting a whole series of ways to do what it thought I was doing. And it seems that MS wants this: the obvious control to turn it off is actually just “snooze.”
I found the setting and turned it off for real. Good riddance. I’ll use the hotkey on occasion.
Yes! I spent more time trying to figure out how to turn off that garbage copilot suggesting then I did editing this 5 year old python program.
I use claude daily, no problems with it. But vscode + copilot suggestions was garbage!
"I wanted the code to look a certain way, but it kept pulling back to the way it wanted to do things."
I would argue this is ok for front-end. For back-end? very, very bad- if you can't get a usable output do it by hand.
"rip it out" is a phrase I've been saying more often to the robots.
I've gone years without coding and when I come back to it, it's like riding a bike! In each iteration of my coding career, I have become a better developer, even after a large gap. Now I can "code" during my gap. Were I ever to hand-code again, I'm sure my skills would be there. They don't atrophy, like your ability to ride a bike doesn't atrophy. Yes you may need to warm back up, but all the connections in your brain are still there.
You might still have the skillset to write code, but depending on length of the break your knowledge of tools, frameworks, patterns would be fairly outdated.
I used to know a person like that - high in the company structure who would claim he was a great engineer, but all the actual engineers would make jokes about him and his ancient skills during private conversations.
I’d push back on this framing a bit. There's a subtle ageism baked into the assumption that someone who stepped away from day-to-day coding has "ancient skills" worth mocking.
Yes, specific frameworks and tooling knowledge atrophy without use, and that’s true for anyone at any career stage. A developer who spent three years exclusively in React would be rusty on backend patterns too. But you’re conflating current tool familiarity with engineering ability, and those are different things.
The fundamentals: system design, debugging methodology, reading and reasoning about unfamiliar code, understanding tradeoffs ... those transfer. Someone with deep experience often ramps up on new stacks faster than you’d expect, precisely because they’ve seen the same patterns repackaged multiple times.
If the person you’re describing was genuinely overconfident about skills they hadn’t maintained, that’s a fair critique. But "the actual engineers making jokes about his ancient skills" sounds less like a measured assessment and more like the kind of dismissiveness that writes off experienced people before seeing what they can actually do.
Worth asking: were people laughing because he was genuinely incompetent, or because he didn’t know the hot framework of the moment? Because those are very different things.
This has nothing to do about ageism. This applies to any person of any age who has ego big enough to think that their knowledge of industry is relevant after they take prolonged break and be socially inept enough to brag about how they are still "in".
I don't disagree with your point about fundamentals, but in an industry where there seems to be new JS framework any time somebody sneezes - latest tools are very much relevant too. And of course the big thing is language changes. The events I'm describing happened in the late 00s-early 10s. When language updates picked up steam: Python, JS, PHP, C++. Somebody who used C++ 98 can't claim to have up to date knowledge in C++ in 2015.
So to answer your question - people were laughing at his ego, not the fact that he didn't know some hot new framework.
I beg to differ. I started with C in the 90s, then C# in '05, then PHP in '12, then Go in '21. The things I learned in C still apply to Go, C#, and PHP. And I even started contributing to open source C projects in '24 ... all my skills and knowledge were still relevant. This sounds exactly like ageism to me, but I clearly have a different perspective than you.
Yes, we clearly have different perspectives. I observed an arrogant person who despite their multi-year break from engineering of any kind strongly believed that they still were as capable as engineers who remained in the field during that time.
Maybe you had to be there.
Have you ever learnt a foreign language (say Mongolian, or Danish) and then never spoken it, nor even read anything in it for over 10 years? It is not like riding a bike, it doesn’t just come back like that. You have to actually relearn the language, practice it, and you will suck at it for months. Comprehension comes first (within weeks) but you will be speaking with grammatical errors, mispronunciations, etc. for much longer. You won‘t have to learn the language from scratch, second time around is much easier, but you will have to put in the effort. And if you use google translate instead of your brain, you won‘t relearn the language at all. You will simply forget it.
Anecdotally, i burned out pretty hard and basically didn't open a text editor for half a year (unemployed too). Eventually i got an itch to write code again and it didn't really feel like I was really worse. Maybe it wasn't long enough atrophy but code doesn't seem to quite work like language though ime.
Six months is definitely not long enough of a break for skills to degrade. But it's not just skills, as I wrote in another comment, the biggest thing is knowledge of new tools, new versions of language and its features.
I'd say there's at most around 2 years of knowledge runtime (maybe with all this AI stuff this is even shorter). After that period if you don't keep your knowledge up to date it fairly quickly becomes obsolete.
Honestly, this seems very much like the jump from being an individual contributor to being an engineering manager.
The time it happened for me was rather abrupt, with no training in between, and the feeling was eerily similar.
You know _exactly_ why the best solution is, you talk to your reports, but they have minds of their own, as well as egos, and they do things … their own way.
At some point I stopped obsessing with details and was just giving guidance and direction only in the cases where it really mattered, or when asked, but let people make their own mistakes.
Now LLMs don’t really learn on their own or anything, but the feeling of “letting go of small trivial things” is sorta similar. You concentrate on the bigger picture, and if it chose to do an iterative for loop instead of using a functional approach the way you like it … well the tests still pass, don’t they.
The only issue is that as an engineering manager you reasonably expect that the team learns new things, improve their skills, in general grow as engineers. With AI and its context handling you're working with a team where each member has severe brain damage that affects their ability to form long term memories.
You can rewire their brain to a degree teaching them new "skills" or giving them new tools, but they still don't actually learn from their mistakes or their experiences.
Being optimistic (or pessimistic heh), if things keep the trend then the models will evolve as well and will probably be quite better in one year than they are now.
As a manager I would encourage them to use the LLM tools. I would also encourage unit tests, e2e testing, testing coverages, CI pipelines automating the testing, automatic pr reviewing etc...
It's also peeking at the big/impactful changes and ignoring the small ones.
Your job isn't to make sure they don't have "brain damage" its to keep them productive and not shipping mistakes.
> I worry about the "brain atrophy" part, as I've felt this too. And not just atrophy, but even moreso I think it's evolving into "complacency".
Not trusting the ML's output is step one here, that keeps you intellectually involved - but it's still a far cry from solving the majority of problems yourself (instead you only solve problems ML did a poor job at).
Step two: I delineate interesting and uninteresting work, and Claude becomes a pair programmer without keyboard access for the latter - I bounce ideas off of it etc. making it an intelligent rubber duck. [Edit to clarify, a caveat is that] I do not bore myself with trivialities such as retrieving a customer from the DB in a REST call (but again, I do verify the output).
> I do not bore myself with trivialities such as retrieving a customer from the DB in a REST call
Genuine question, why isn't your ORM doing that? I see a lot of use cases for LLMs that seem to be more expensive ways to do snippets and frameworks...
My advice: keep it on a tight leash.
In the happy case where I have a good idea of the changes necessary, I will ask it to do small things, step by step, and examine what it does and commit.
In the unhappy case where one is faced with a massive codebase and no idea where to start, I find asking it to just “do the thing” generates slop, but enough for me to use as inspiration for the above.
My experience is the opposite - I haven't used my brain more in a while.. Typing characters was never what developers were valued for anyway. The joy of building is back too.
100% same, I had brain fog before the llms, I got tired of reading new docs over and over again for new languages. I became a manager and lost it all.
Now back to IC with 25+ years of experience + LLM = god mode, and its fun again.
Same. I feel I need to be way more into the domain and what the user is trying to do than ever before.
> Like if I had stated certain design goals recently it would adhere to them, but after a few iterations it would forget again and go back to its original approach, or mix the two, or whatever.
Context management, proper prompting and clear instructions, proper documentation are still relevant.
I feel like I'm still a couple steps behind in skill level as my lead and is trying to gain more experience I do wonder if I am shooting myself in the foot if I rely too much on AI at this stage. The senior engineer I'm trying to learn from can very effectively use ai because he has very good judgement of code quality, I feel like if I use AI too much I might lose out on chance to improve my judgement. It's a hard dilemma.
it's not about brain atrophy, it's skill atrophy
is that not the sam thing?
He didn't say "brain atrophy", he was talking about coding abilities.
> I want to say it's very akin to doom scrolling. Doom tabbing? It's like, yeah I could be more creative with just a tad more effort, but the AI is already running and the bar to seeing what the AI will do next is so low, so....
Yea exactly, Like we are just waiting so that it gets completed and after it gets completed then what? We ask it to do new things again.
Just as how if we are doom scrolling, we watch something for a minute then scroll down and watch something new again.
The whole notion of progress feels completely fake with this. Somehow I guess I was in a bubble of time where I had always end up using AI in web browsers (just as when chatgpt 3 came) and my workflow didn't change because it was free but recently changed it when some new free services dropped.
"Doom-tabbing" or complete out of the loop AI agentic programming just feels really weird to me sucking the joy & I wouldn't even consider myself a guy particular interested in writing code as I had been using AI to write code for a long time.
I think the problem for me was that I always considered myself a computer tinker before coder. So when AI came for coding, my tinkering skills were given a boost (I could make projects of curiosity I couldn't earlier) but now with AI agents in this autonomous esque way, it has come for my tinkering & I do feel replaced or just feel like my ability of tinkering and my interests and my knowledge and my experience is just not taken up into account if AI agent will write the whole code in multi file structure, run commands and then deploy it straight to a website.
I mean my point is tinkering was an active hobby, now its becoming a passive hobby, doom-tinkering? I feel like I have caught up on the feeling a bit earlier with just vibe from my heart but is it just me who feels this or?
What could be a name for what I feel?
[dead]
LLMs have some terrible patterns, don't know what do ? Just chuck a class named Service in.
Have to really look out for the crap.
The tenacity part is definitely true. I told it to keep trying when it kept getting stuck trying to spin up an Amazon Fargate service. I could feel its pain, and wanted to help, but I wanted to see whether the LLM could free itself from the thorny and treacherous AWS documentation forest. After a few dozen attempts and probably 50 KWh of energy it finally got it working, I was impressed. I could have done it faster myself, but the tradeoff would have been much higher blood pressure. Instead I relaxed and watched youtube while the LLM did its work.
Why am I not surprised that a blog was written about LLM coding going from 20% to 80% useful, yet all of the HN comments are still nit picking about some negative details rather than building positive ideas toward some progress...
Is the programmer ego really this fragile? At least luddites had an ideological reasoning, whereas here we just seem to have emotional reflexes.
It's because we see a bunch of people completely ignoring the missing 20% and flooding the world with complete slop. The push back is required to keep us sane, we need people reminding others that it's not at 100% yet even if it sometimes feels like it.
The fact that people keep pushing figures like 80% is total bs to me
I retired from paid sw dev work in 2020 when COVID arrived.
I’ve worked on my small projects since with all development by hand. I’d followed the rise of AI, but not used it.
Late last year I started a project that included reverse engineering some firmware that runs on an Intel 8096 based embedded processor. I’d never worked on that processor before. There are tools available, but they cost many $. So, I started to think about a simple disassembler.
2 weeks ago we decided to try Claude to see what it could do. We now have a disassembler, assembler and a partially working emulator. No doubt there are bugs and missing features and the code is a bit messy, but boy has it sped up the work.
One thing did occur to me. Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
> Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
Definitely. Making small, single-purpose utilities with LLMs is almost as easy these days as googling for them on-line - much easier, in fact, if you account for time spent filtering out all the malware, adware, "to finish the process, register an account" and plain broken "tools" that dominate SERP.
Case in point, last time my wife needed to generate a few QR codes for some printouts for an NGO event, I just had LLM make one as a static, single-page client-side tool and hosted it myself -- because that was the fastest way to guarantee it's fast, reliable, free of surveillance economy bullshit, and doesn't employ URL shorteners (surprisingly common pattern that sometimes becomes a nasty problem down the line; see e.g. a high-profile case of some QR codes on food products leading to porn sites after shortlink got recycled).
Whatever happened to just typing "apt install qrencode"? It's definitely "fast, reliable, free of surveillance economy bullshit, and doesn't employ URL shorteners".
> LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
I’ve always said I’m a builder even though I’ve also enjoyed programming (but for an outcome, never for the sake of the code)
This perfectly sums up what I’ve been observing between people like me (builders) who are ecstatic about this new world and programmers who talk about the craft of programming, sometimes butting heads.
One viewpoint isn’t necessarily more valid, just a difference of wiring.
I noticed the same thing, but wasn't able to put it into words before reading that. Been experimenting with LLM-based coding just so I can understand it and talk intelligently about it (instead of just being that grouchy curmudgeon), and the thought in the back of my mind while using Claude Code is always:
"I got into programming because I like programming, not whatever this is..."
Yes, I'm building stupid things faster, but I didn't get into programming because I wanted to build tons of things. I got into it for the thrill of defining a problem in terms of data structures and instructions a computer could understand, entering those instructions into the computer, and then watching victoriously while those instructions were executed.
If I was intellectually excited about telling something to do this for me, I'd have gotten into management.
Same. This kind of coding feels like it got rid of the building aspect of programming that always felt nice, and it replaced it entirely with business logic concerns, product requirements, code reviews, etc. All the stuff I can generally take or leave. It's like I'm always in a meeting.
>If I was intellectually excited about telling something to do this for me, I'd have gotten into management.
Exactly this. This is the simplest and tersest way of explaining it yet.
Maybe I don't entirely get it, but what is stopping you to just continue coding?
Because you are not coding, you are building. I've been coding since I was 7 years old, now I'm building.
I'd go one step higher, we're not builders, we're problem solvers.
Sometimes the problem needs building, sometimes not.
I'm an Engineer, I see a problem and want to solve it. I don't care if I have to write code, have a llm build something new, or maybe even destroy something. I want to solve the problem for the business and move to the next one, most of the time it is having a llm write code though.
Same same. Writing the actual code is always a huge motivator behind my side projects. Yes, producing the outcome is important, but the journey taken to get there is a lot of fun for me.
I used Claude Code to implement a OpenAI 4o-vision powered receipt scanning feature in an expense tracking tool I wrote by hand four years ago. It did it in two or three shots while taking my codebase into account.
It was very neat, and it works great [^0], but I can't latch onto the idea of writing code this way. Powering through bugs while implementing a new library or learning how to optimize my test suite in a new language is thrilling.
Unfortunately (for me), it's not hard at all to see how the "builders" that see code as a means to an end would LOVE this, and businesses want builders, not crafters.
In effect, knowing the fundamentals is getting devalued at a rate I've never seen before.
[^0] Before I used Claude to implement this feature, my workflow for processing receipts looked like this: Tap iOS Shortcut, enter the amount, snap a pic of the receipt, type up the merchant, amount and description for the expense, then have the shortcut POST that to my expenses tracking toolkit which, then, POSTs that into a Google Sheet. This feature amounted the need for me to enter the merchant and amount. Unfortunately, it often took more time to confirm that the merchant, amount and date details OpenAI provided were correct (and correct it when details were wrong, which was most of the the time) than it did to type out those details manually, so I just went back to my manual workflow. However, the temptation to just glance at the details and tap "This looks correct" was extremely high, even if the info it generated was completely wrong! It's the perfect analogue to what I've been witnessing throughout the rise of the LLMs.
What I have enjoyed about programming is being able to get the computer to do exactly what I want. The possibilities are bounded by only what I can conceive in my mind. I feel like with AI that can happen faster.
> get the computer to do exactly what I want.
> with AI that can happen faster.
well, not exactly that.
For simple things it can. But then for more complex things that's where I step it
Have you an example of getting a coding chatbot to do exactly what you want?
The problem with replying to the proof-demanders is that they'll always pick it apart and find some reason it doesn't fit their definition. You must be familiar with that at this point.
See this is a perfect example of OPs statement! I don't care about the lines, I care about the output! It was never about the lines of code.
Your comment makes it very clear there are different viewpoints here. We care about problem->solution. You care about the actual code more than the solution.
> I don't care about the lines, I care about the output! It was never about the lines of code.
> Your comment makes it very clear there are different viewpoints here.
Agreed.
I care that code output not include leaked secrets, malware installation, stealth cryptomining etc.
Some others don't.
Is this a joke? Are you genuinely implying that no one has ever got an LLM to write code that does exactly what they want?
No. Mashing up other peoples' code scraped from the web is not what I'd call writing code.
Can you not see how you truly, deep down, are afraid you might be wrong?
It's clouding your vision.
This gets at the heart of the quality of results issues a lot of people are talking about elsewhere here. Right now, if you treat them as a system where you can tell it what you want and it will do it for you, you're building a sandcastle. Instead of that, also describe the correct data structures and appropriate algorithms to use against them, as well as the particulars of how you want the problem solved, it's a different situation altogether. Like most systems, the quality of output is in some way determined by the quality of input.
There is a strange insistence on not helping the LLM arrive at the best outcome in the subtext to this question a lot of times. I feel like we are living through the John Henry legend in real time
> I got into it for the thrill of defining a problem in terms of data structures and instructions a computer could understand, entering those instructions into the computer, and then watching victoriously while those instructions were executed.
You can still do that with Claude Code. In fact, Claude Code works best the more granular your instructions get.
> Claude Code works best the more granular your instructions get.
So best feed it machine code?
Funny you say that. Because I have never enjoyed management as much as being hands on and directly solving problems.
So maybe our common ground is that we are direct problem solvers. :-)
For some reason this makes me think of a jigsaw puzzle. People usually complete these puzzles because they enjoy the process where on the end you get a picture that you can frame if you want to. Some people seem to want to get the resulting picture. No interest in process at all.
I guess that's the same people who went to all those coding camps during their hay day because they heard about software engineering salaries. They just want the money.
When I last bought a Lego Technic set because I wanted to play with making mechanisms with gears and stuff, I assembled it according to the instructions, which was fun, and then the final result was also cool and I couldn't bear to dismantle it.
IMO, this isn't entirely a "new world" either, it is just a new domain where the conversation amplifies the opinions even more (weird how that is happening in a lot of places)
What I mean by that: you had compiled vs interpreted languages, you had types vs untyped, testing strategies, all that, at least in some part, was a conversation about the tradeoffs between moving fast/shipping and maintainability.
But it isn't just tech, it is also in methodologies and the words use, from "build fast and break things" and "yagni" to "design patterns" and "abstractions"
As you say, it is a different viewpoint... but my biggest concern with where are as industry is that these are not just "equally valid" viewpoints of how to build software... it is quite literally different stages of software, that, AFAICT, pretty much all successful software has to go through.
Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
This perspective is crucial. Scale is the great equalizer / demoralizer, scale of the org and scale of the systems. Systems become complex quickly, and verifiability of correctness and function becomes harder. Companies that built from day with AI and have AI influencing them as they scale, where does complexity begin to run up against the limitations of AI and cause regression? Or if all goes well, amplification?
makes sense if you are a data scientist where people need to be boxed into tidy little categories. but some people probably fall into both categories.
But how can you be a responsible builder if you don't have trust in the LLMs doing the "right thing"? Suppose you're the head of a software team where you've picked up the best candidates for a given project, in that scenario I can see how one is able to trust the team members to orchestrate the implementation of your ideas and intentions, with you not being intimately familiar with the details.
Can we place the same trust in LLM agents? I'm not sure. Even if one could somehow prove that LLM are very reliable, the fact an AI agents aren't accountable beings renders the whole situation vastly different than the human equivalent.
Trust but verify:
I test all of the code I produce via LLMs, usually doing fairly tight cycles. I also review the unit test coverage manually, so that I have a decent sense that it really is testing things - the goal is less perfect unit tests and more just quickly catching regressions. If I have a lot of complex workflows that need testing, I'll have it write unit tests and spell out the specific edge cases I'm worried about, or setup cheat codes I can invoke to test those workflows out in the UI/CLI.
Trust comes from using them often - you get a feeling for what a model is good and bad at, and what LLMs in general are good and bad at. Most of them are a bit of a mess when it comes to UI design, for instance, but they can throw together a perfectly serviceable "About This" HTML page. Any long-form text they write (such as that About page) is probably trash, but that's super-easy to edit manually. You can often just edit down what they write: they're actually decent writers, just very verbose and unfocused.
I find it similar to management: you have to learn how each employee works. Unless you're in the Top 1%, you can't rely on every employee giving 110% and always producing perfect PRs. Bugs happen, and even NASA-strictness doesn't bring that down to zero.
And just like management, some models are going to be the wrong employee for you because they think your style guide is stupid and keep writing code how they think it should be written.
You don't simply put a body in a seat and get software. There are entire systems enabling this trust: college, resume, samples, referral, interviews, tests and CI, monitoring, mentoring, and performance feedback.
And accountability can still exist? Is the engineer that created or reviewed a Pull Request using Claude Code less accountable then one that used PICO?
> And accountability can still exist? Is the engineer that created or reviewed a Pull Request using Claude Code less accountable then one that used PICO?
The point is that in the human scenario, you can hold the human agents accountable. You cannot do that with AI. Of course, you as the orchestrator of agents will be accountable to someone, but you won't have the benefit of holding your "subordinates" accountable, which is what you do in a human team. IMO, this renders the whole situation vastly different (whether good or bad I'm not sure).
You can switch to another LLM provider or stop using them altogether. It's even easier than firing a developer.
It is as easy as getting rid of Microsoft Teams at your org.
Of course he is - because he invested so much less.
I remember leaving university going into my first engineering job, thinking "Where is all the engineering? All the problem solving and building complex system? All the math and science? Have I been demoted to a lowly programmer?"
Took me a few years to realize that this wasn't a universal feeling, and that many others found the programming tasks more fulfilling than any challenging engineering. I suppose this is merely another manifestation of the same phenomena.
There's more to it than just coding Vs building though.
For a long time in my career now I've been in a situation where I'd be able to build more if I was willing to abstract myself and become a slide-merchant/coalition-builder. I don't want to do this though.
Yet, I'm still quite an enthusiastic vibe-coder.
I think it's less about coding Vs building and more about tolerance for abstraction and politics. And I don't think there are that many people who are so intolerant of abstraction that they won't let agents write a bunch of code for them.
> > LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
This is much less significant than the fact LLMs split engineers on those who primarily like quality v. those who primarily like speed.
Maybe there's an intermediate category: people who like designing software? I personally find system design more engaging than coding (even though I enjoy coding as well). That's different from just producing an opaque artifact that seems to solve my problem.
I think he's really getting at something there. I've been thinking about this a lot (in the context of trying to understand the persistent-on-HN skepticism about LLMs), and the framing I came up with[1] is top-down vs. bottom-up dev styles, aka architecting code and then filling in implementations, vs. writing code and having architecture evolve.
I like this framing. Nice typography btw, a pleasure to read.
I think there's a place for both.
We have services deployed globally serving millions of customers where rigor is really important.
And we have internal users who're building browser extensions with AI that provide valuable information about the interface they're looking at including links to the internal record management, and key metadata that's affecting content placement.
These tools could be handed out on Zip drives in the street and it would just show our users some of the metadata already being served up to them, but it's amazing to strip out 75% of the process of certain things and just have our user (in this case though, it's one user who is driving all of this, so it does take some technical inclination) build out these tools that save our editors so much time when doing this before would have been months and months and months of discovery and coordination and designs that probably wouldn't actually be as useful in the end after the wants of the user are diluted through 18 layers of process.
To me this is similar to car enthusiasms. Some people absolutely love to build their project car, it's a major part of the hobby for them. Others just love the experience of driving, so they buy ready cars or just pay someone to work on the car.
Alternatively, others just want to get to their destination.
I think the division is more likely tied to writing. You have to fundamentally change how you do your job, from one of writing a formal language for a compiler to one of writing natural language for a junior-goldfish-memory-allstar-developer, closer to management then to contributor.
This distinction to me separates the two primary camps
The new LLM centered workflow is really just a management job now.
Managers and project managers are valuable roles and have important skill sets. But there's really very little connection with the role of software development that used to exist.
It's a bit odd to me to include both of these roles under a single label of "builders", as they have so little in common.
i don't disagree. at some point LLM's might become good enough that we wouldn't need exact technical expertise.
I enjoy both and have ended up using AI a lot differently than vibe coders. I rarely use it for generating implementations, but I use it extensively for helping me understand docs/apis and more importantly, for debugging. AI saves me so much time trying to figure out why things aren’t working and in code review.
I deliberately avoid full vibe coding since I think doing so will rust my skills as a programmer. It also really doesn’t save much time in my experience. Once I have a design in mind, implementation is not the hard part.
So far I haven't seen it actually be effective at "building" in a work context with any complexity, and this despite some on our team desperately trying to make that the case.
I have! You have to be realistic about the projects. The more irreducible local context it needs, the less useful it will be. Great for greenfield code, oneshots, write once read once run for months.
Agreed. I don’t care for engineering or coding, and would gladly give it up the moment I can. I’m also running a one man business where every hour counts (and where I’m responsible for maintaining every feature).
The fact of the matter is LLMs produce lower quality at higher volumes in more time than it would take to write it myself, and I’m a very mediocre engineer.
I find this seperation of “coding” vs “building” so offensive. It’s basically just saying some people are only concerned with “inputs”, while others with “outputs”. This kind of rhetoric is so toxic.
It’s like saying LLM art is separating people into people who like to scribble, and people who like to make art.
I like building, but I don't fool myself into thinking it can be done by taking shortcuts. You could build something that looks like a house for half the cost but it won't be structurally sound. That's why I care about the details. Someone has to.
> I enjoy both and have ended up using AI a lot differently than vibe coders. I rarely use it for generating implementations, but I use it extensively for helping me understand docs/apis and more importantly, for debugging. AI saves me so much time trying to figure out why things aren’t working and in code review.
I had felt like this and still do but man, at some point, I feel like the management churn feels real & I just feel suffering from a new problem.
Suppose, I actually end up having services literally deployed from a single prompt nothing else. Earlier I used to have AI write code but I was interested in the deployment and everything around it, now there are services which do that really neatly for you (I also really didn't give into the agent hype and mostly used browsers LLM)
Like on one hand you feel more free to build projects but the whole joy of project completely got reduced.
I mean, I guess I am one of the junior dev's so to me AI writing code on topics I didn't know/prototyping felt awesome.
I mean I was still involved in say copy pasting or looking at the code it generates. Seeing the errors and sometimes trying things out myself. If AI is doing all that too, idk
For some reason, recently I have been disinterested in AI. I have used it quite a lot for prototyping but I feel like this complete out of the loop programming just very off to me with recent services.
I also feel like there is this sense of if I buy for some AI thing, to maximally extract "value" out of it.
I guess the issue could be that I can have vague terms or have a very small text file as input (like just do X alternative in Y lang) and I am now unable to understand the architectural decisions and the overwhelmed-ness out of it.
Probably gonna take either spec-driven development where I clearly define the architecture or development where I saw something primagen do recently which is that the AI will only manipulate code of that particular function, (I am imagining it for a file as well) and somehow I feel like its something that I could enjoy more because right now it feels like I don't know what I have built at times.
When I prototype with single file projects using say browser for funsies/any idea. I get some idea of what the code kind of uses with its dependencies and functions names from start/end even if I didn't look at the middle
A bit of ramble I guess but the thing which kind of is making me feel this is that I was talking to somebody and shwocasing them some service where AI + server is there and they asked for something in a prompt and I wrote it. Then I let it do its job but I was also thinking how I would architect it (it was some detect food and then find BMR, and I was thinking first to use any api but then I thought that meh it might be hard, why not use AI vision models, okay what's the best, gemini seems good/cheap)
and I went to the coding thing to see what it did and it actually went even beyond by using the free tier of gemini (which I guess didn't end up working could be some rate limit of my own key but honestly it would've been the thing I would've tried too)
So like, I used to pride myself on the architectural decisions I make even if AI could write code faster but now that is taken away as well.
I really don't want to read AI code so much so honestly at this point, I might as well write code myself and learn hands on but I have a problem with build fast in public like attitude that I have & just not finding it fun.
I feel like I should do a more active job in my projects & I am really just figuring out what's the perfect way to use AI in such contexts & when to use how much.
Thoughts?
I would agree that OAIs GPT-5 family of models is a phase change over GPT-4.
In the ChatGPT product this is not immediately obvious and many people would strongly argue their preference for 4. However, once you introduce several complex tools and make tool calling mandatory, the difference becomes stark.
I've got an agent loop that will fail nearly every time on GPT-4. It works sometimes, but definitely not enough to go to production. GPT-5 with reasoning set to minimal works 100% of the time. $200 worth of tokens and it still hasn't failed to select the proper sequence of tools. It sometimes gets the arguments to the tools incorrect, but it's always holding the right ones now.
I was very skeptical based upon prior experience but flipping between the models makes it clear there has been recent stepwise progress.
I'll probably be $500 deep in tokens before the end of the month. I could barely go $20 before I called bullshit on this stuff last time.
Pretty sure there wasn't extensive training on tooling beforehand. I mean, god, during GPT-3 even getting a reliable json output was a battle and there were dedicated packages for json inference.
> You realize that stamina is a core bottleneck to work
There has been a lot of research that shows that grit is far more correlated to success than intelligence. This is an interesting way to show something similar.
AIs have endless grit (or at least as endless as your budget). They may outperform us simply because they don't ever get tired and give up.
Full quote for context:
Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.
>They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day.
"Listen, and understand! That Terminator is out there! It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse, or fear. And it absolutely will not stop... ever, until you are dead!"
I realized a long time ago that I’m better at computer stuff not because I’m smarter but because I will sit there all day and night to figure something out while others will give up. I always thought that was my superpower in the job industry but now I’m not so sure if it will transfer to getting AI to do what I need done…
Same, I barely made it through Engineering school, but would stay up all night figuring out everything a computer could do (before the internet).
I did it because I enjoyed it, and still do. I just do it with LLMs now. There is more to figure out than ever before and things get created faster than I have time to understand them.
LLM should be enabling this, not making it more depressing.
If you ever work with LLMs you know that they quite frequently give up.
Sometimes it's a
// TODO: implement logic
or a
"this feature would require extensive logic and changes to the existing codebase".
Sometimes they just declare their work done. Ignoring failing tests and builds.
You can nudge them to keep going but I often feel like, when they behave like this, they are at their limit of what they can achieve.
Nope, not for me, unless I tell it to.
Context matters, for an LLM just like a person. When I wrote code I'd add TODOs because we cannot context switch to another problem we see every time.
But you can keep the agent fixated on the task AND have it create these TODOs, but ultimately it is your responsibility to find them and fix them (with another agent).
If I tell it to implement something it will sometimes declare their work done before it's done. But if I give Claude Code a verifiable goal like making the unit tests pass it will work tirelessly until that goal is achieved. I don't always like the solution, but the tenacity everyone is talking about is there
> but the tenacity everyone is talking about is there
I always double-check if it doesn't simply exclude the failing test.
The last time I had this, I discovered it later in the process. When I pointed this out to the LLM, it responded, that it acknowledged thefact of ignoring the test in CLAUDE.md, and this is justified because [...]. In other words, "known issue, fuck off"
tenacity == while loop
> If you ever work with LLMs you know that they quite frequently give up.
If you try to single shot something perhaps. But with multiple shots, or an agent swarm where one agent tells another to try again, it'll keep going until it has a working solution.
Yeah exactly this is a scope problem, actual input/output size is always limited> I am 100% sure CC etc are using multiple LLM calls for each response, even though from the response streaming it looks like just one.
Using LLMs to clean those up is part of the workflow that you're responsible for (... for now). If you're hoping to get ideal results in a single inference, forget it.
The tenacity aspect makes me worried about the paper clip AI misalignment scenario more than before.
> AIs have endless grit (or at least as endless as your budget).
That is the only thing he doesn't address: the money it costs to run the AI. If you let the agents loose, they easily burn north of 100M tokens per hour. Now at $25/1M tokens that gets quickly expensive. At some point, when we are all drug^W AI dependent, the VCs will start to cash in on their investments.
LLMs do not have grit or tenacity. Tenacity doesn't desribe a machine that doesn't need sleep or experience tiredness, or stress. Grit doesn't describe a chatbot that will tirelessly spew out answers and code because it has no stake or interest in the result, never perceives that it doesn't know something, and never reflects on its shortcomings.
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
I was thinking about this the other day as relates to the DevOps movement.
The DevOps movement started as a way to accelerate and improve the results of dev<->ops team dynamics. By changing practices and methods, you get acceleration and improvement. That creates "high-performing teams", which is the team form of a 10x engineer. Whether or not you believe in '10x engineers', a high-performing team is real. You really can make your team deploy faster, with fewer bugs. You have to change how you all work to accomplish it, though.
To get good at using AI for coding, you have to do the same thing: continuous improvement, changing workflows, different designs, development of trust through automation and validation. Just like DevOps, this requires learning brand new concepts, and changing how a whole team works. This didn't get adopted widely with DevOps because nobody wanted to learn new things or change how they work. So it's possible people won't adapt to the "better" way of using AI for coding, even if it would produce a 10x result.
If we want this new way of working to stick, it's going to require education, and a change of engineering culture.
This is an interesting thing that I'm contemplating. I also do believe that (perhaps with very few exceptions) there are no "10x engineers" by themselves, but engineers that thrive 10x more in a context or another (like, I'm sure Jeff Dean is an absolutely awesome engineer - but if you took him out of Google and plugged him into IBM - would he have had the same impact?)
With that in mind - I think one very unexplored area is "how to make the mixed AI-human teams successful". Like, I'm fairly convinced AI changes things, but to get to the industrialization of our craft (which is what management seems to want - and, TBH, something that makes sense from an economic pov), I feel that some big changes need to happen, and nobody is talking about that too much. What are the changes that need to happen? How do we change things, if we are to attempt such industrialization?
I'm pretty happy with Copilot in VS Code. Type what change I want Claude to make in the Copilot panel, and then use the VS Code in context diffs to accept or reject the proposed changes. While being able to make other small changes on my own.
So I think this tracks with Karpathy's defense of IDEs still being necessary ?
Has anyone found it practical to forgo IDEs almost entirely?
I've found copilot chat is able to do everything I need. I tried the Claude plugin for vscode and it was a noticeably worse experience for me.
Mind you copilot has only supported agent mode relatively recently.
I really like the way copilot does changes in such a way you can accept or reject and even revert to point in time in the chat history without using git. Something about this just fits right with how my brain works.
Using Claude plugin just felt like I had one hand tied behind my back.
I find Claude Code in VS Code is sometimes horribly inefficient. I tell it to replace some print-statements with proper logging in the one file I have open and it first starts burning tokens to understand the codebase for the 13th time today, despite not needing to and having it laid out in the CLAUDE.md already.
I have been assigning issues to copilot in Github. It will then create a pull request and work on and report back on the issue in the PR. I will pull the code and make small changes locally using VSCode when needed.
But what I like about this setup is that I have almost all the context I need to review the work in a single PR. And I can go back and revisit the PR if I ever run into issues down the line. Plus you can run sessions in parallel if needed, although I don't do that too much.
Are you letting it run your tests and run little snippets of code to try them out (like "python -c 'import module; print(module.something())'") or are you just using it to propose diffs for you to accept or reject?
This stuff gets a whole lot more interesting when you let it start making changes and testing them by itself.
Coplilot is not on par with cc or cursor even
I use it to access Claude. So what's the difference?
This stuff is a little messy and opaque, but the performance of the same model in different harnesses depends a lot on how context is managed. The last time I tried Copilot, it performed markedly worse for similar tasks compared to Claude Code. I suspect that Copilot was being very aggressive in compressing context to save on token cost, but I'm not 100% certain about this.
Also note that with Claude models, Copilot might allocate a different number of thinking tokens compared to Claude Code.
Things may have changed now compared to when I tried it out, these tools are in constant flux. In general I've found that harnesses created by the model providers (OpenAI/Codex CLI, Anthropic/Claude Code, Google/Gemini CLI) tend to be better than generalist harnesses (cheaper too, since you're not paying a middleman).
Different harnesses and agentic environments produce different results from the same model. Claude Code and Cursor are the best IME and Copilot is by far the worst.
Why not? You can select Opus 4.5, Gemini 3 Pro, and others.
Claude Code is a CLI tool which means it can do complete projects in a single command. Also has fantastic tools for scaffolding and harnessing the code. You can define everything from your coding style to specific instructions for designing frontpages, integrating payments, etc.
It's not about the model. It's about the harness
Claude Code is a CLI tool which means it can do complete projects in a single command
This would make some sense if VS Code didn't have a terminal built into it. The LLMs have the same bash capabilities in either form.
Huh? There is nothing stopping copilot from doing an entire project in one go.
Ive done it 10s of times.
it's not a model limit anymore, it's tools , skills, background agents, etc.
It's an entire agentic environment.
Github copilot has support for this stuff as well. Agent skills, background/subagents, etc.
I used CC in year age and it was not good. But one month ago I paid for max and started to rebuild my company web shop using it.
It is like plowing land with hand one year age and now is like I'm in brend new John Deere. It's amazing.
Of course its not perfect but if you understand code and problem it needs to solve then it works really good.
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
This is true... Equally I've seen it dive into a rabbit hole, make some changes that probably aren't the right direction... and then keep digging.
This is way more likely with Sonnet, Opus seems to be better at avoiding it. Sonnet would happily modify every file in the codebase trying to get a type error to go away. If I prompt "wait, are you off track?" it can usually course correct. Again, Opus seems way better at that part too.
Admittedly this has improved a lot lately overall.
I don't understand why anyone finds it interesting that a machine, or chatbot, never tires or gets demoralized. You have to anthromorphize the LLM before you can even think of those possibilities. A tractor never tires or gets demoralized either, because it can't. Chatbots don't "dive into a rabbit hole ... and then keep digging" because they have superhuman tenacity, they do it because that's what software does. If I ask my laptop to compute the millionth Fibonacci number it doesn't sigh and complain, and I don't think it shows any special qualities unless I compare it to a person given the same job.
You're a machine. You're literally a wet, analog device converting some forms of energy into other forms just like any other machine as you work, rest, type out HN comments, etc. There is nothing special about the carbon atoms in your body -- there's no metadata attached to them marking them out as belonging to a Living Person. Other living-person-machines treat "you" differently than other clusters of atoms only because evolution has taught us that doing so is a mutually beneficial social convention.
So, since you're just a machine, any text you generate should be uninteresting to me -- correct?
Alternatively, could it be that a sufficiently complex and intricate machine can be interesting to observe in its own right?
If humans are machines, they are still a subset of machines and they (among other animals) are the only ones who can be demotivated and so it is still a mistake to assume an entirely different kind of machine would have those properties.
>Other living-person-machines treat "you" differently than other clusters of atoms only because evolution has taught us that doing so is a mutually beneficial social convention
Evolution doesn't "teach" anything. It's just an emergent property of the fact that life reproduces (and sometimes doesn't). If you're going to have this radically reductionist view of humanity, you can't also treat evolution as having any kind of agency.
maybe its just me doing stuff that's out the usual loop
even dealing with api's that have MCP servers the so called agents make a mess of everything.
my stuff is just regular data stuff - ingest data from x - transform it | make it real time - then pipe it to y
LLM coding splits up engineers based on those who primarily like building and those who primarily like code reviews and quality assessment. I definitely don’t love the latter (especially when reviewing decisions not made by a human with whom I can build long-term personal rapport).
After certain experience threshold of making things from scratch, “coding” (never particularly liked that term) has always been 99% building, or architecture, and I struggle to see how often a well-architected solution today, with modern high-level abstractions, requires so much code that you’d save significant time and effort by not having to just type, possibly with basic deterministic autocomplete, exactly what you mean (especially considering you would have to also spend time and effort reviewing whatever was typed for you if you used a non-deterministic autocomplete).
See, I don't take it that extreme: LLMs make fantastic, never-before seen quality autocompletes. I hacked together a Neovim plugin that prompts an LLM to "finish this function" on command, and it's a big time save for the menial plumbing type operations. Think things like "this api I use expects JSON that encodes some subset of SQL, I want all the dogs with Ls in their name that were born on a Tuesday". Given an example of such API (or if the documentation ended up in its training), LLMs will consistently one-shot stuff like that.
Asking it to do entire projects? Dumb. You end up with spaghetti, unless you hand-hold it to a point that you might as well be using my autocomplete method.
Depends on the scope of the project. If it's small, and you direct it correctly, it can one-shot yes. Or 2-3-shot.
Agree with Karpathy's take. Finally a down to Earth analysis from a respected source in the AI space. I guess I'll be using slopocalypse a lot more now :)
> I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media
It has arrived. Github will be most affected thanks to git-terrorists at Apna College refusing to take down that stupid tutorial. IYKYK.
The respect is unwarranted.
He ran Teslas ML division, but still doesnt know what a simple kalman filter is (in the sense where he claimed that lidar would be hard to integrate with cameras).
The Kalman filter examples I've seen always involve estimating a very simple quantity, like the location of a single 3D point, from noisy sensors. It's clear how multiple estimates can be combined into a new estimate.
I'd guess that cameras on a self-driving car are trying to estimate something much more complex, something like 3D surfaces labeled with categories ("person", "traffic light", etc.). It's not obvious to me how estimates of such things from multiple sensors and predictions can be sensibly and efficiently combined to produce a better estimate. For example, what if there is a near red object in front of a distant red background, so that the camera estimates just a single object, but the lidar sees two?
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually...
> Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.
Until you struggle to review it as well. Simple exercise to prove it - ask LLM to write a function in familiar programming language, but in the area you didn't invest learning and coding yourself. Try reviewing some code involving embedding/SIMD/FPGA without learning it first.
People would struggle to review code in a completely unfamiliar domain or part of the stack even before LLMs.
That’s why you need to write code to learn it.
No-one has ever learned skill just by reading/observing
How would you find yourself in that situation before AI?
No, because they wouldn't be so foolish as to try it.
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
Somewhere, there are GPUs/NPUs running hot. You send all the necessary data, including information that you would never otherwise share. And you most likely do not pay the actual costs. It might become cheaper or it might not, because reasoning is a sticking plaster on the accuracy problem. You and your business become dependent on this major gatekeeper. It may seem like a good trade-off today. However, the personal, professional, political and societal issues will become increasingly difficult to overlook.
This quote stuck out to me as well, for a slightly different reason.
The “tenacity” referenced here has been, in my opinion, the key ingredient in the secret sauce of a successful career in tech, at least in these past 20 years. Every industry job has its intricacies, but for every engineer who earned their pay with novel work on a new protocol, framework, or paradigm, there were 10 or more providing value by putting the myriad pieces together, muddling through the ever-waxing complexity, and crucially never saying die.
We all saw others weeded out along the way for lacking the tenacity. Think the boot camp dropouts or undergrads who changed majors when first grappling with recursion (or emacs). The sole trait of stubbornness to “keep going” outweighs analytical ability, leetcode prowess, soft skills like corporate political tact, and everything else.
I can’t tell what this means for the job market. Tenacity may not be enough on its own. But it’s the most valuable quality in an employee in my mind, and Claude has it.
There is an old saying back home: an idiot never tires, only sweats.
Claude isn't tenacious. It is an idiot that never stops digging because it lacks the meta cognition to ask 'hey, is there a better way to do this?'. Chain of thought's whole raison d'etre was so the model could get out of the local minima it pushed itself in. The issue is that after a year it still falls into slightly deeper local minima.
This is fine when a human is in the loop. It isn't what you want when you have a thousand idiots each doing a depth first search on what the limit of your credit card is.
> it lacks the meta cognition to ask 'hey, is there a better way to do this?'.
Recently had an AI tell me this code (that it wrote) is a mess and suggested wiping it and starting from scratch with a more structure plan. That seems to hint at some meta cognition outlines
Haha, it has the human developer traits of thinking all old code is garbage, failing to identify oneself as the dummy who wrote this particular code, and wanting to start from scratch.
It's like NIH syndrome but instead "not invented here today". Also a very human thing.
More like NIITS: Not Invented in this Session.
Perhaps. I've had LLMs tell me some code is deeply flawed garbage that should be rewritten about code that exact same LLM wrote minutes before. It could be a sign of deep meta cognition, or it might be due to some cognitive gaps where it has no idea why it did something a minute ago and suddenly has a different idea.
Someone will say "you just need to instruct Claude.md to be more meta and do a wiggum loop on it"
I asked Claude to analyze something and report back. It thought for a while said “Wow this analysis is great!” and then went back to thinking before delivering the report. They’re auto-sycophantic now!
Metacognition As A Service, you say?
Running on the Meta Cognition Protocol server near you.
You’ll get sued by Meta for this!
I think that’s called “consulting”.
lol no it doesn’t. It hints at convincing language models
I mean, not always. I've seen Claude step back and reconsider things after hitting a dead end, and go down a different path. There are also workflows, loops that can increase the likelihood of this occurring.
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This is a major concern for junior programmers. For many senior ones, after 20 (or even 10) years of tenacious work, they realize that such work will always be there, and they long ago stopped growing on that front (i.e. they had already peaked). For those folks, LLMs are a life saver.
At a company I worked for, lots of senior engineers become managers because they no longer want to obsess over whether their algorithm has an off by one error. I think fewer will go the management route.
(There was always the senior tech lead path, but there are far more roles for management than tech lead).
I feel like if you're really spending a ton of time on off by one errors after twenty years in the field you haven't actually grown much and have probably just spent a ton of time in a single space.
Otherwise you'd be senior staff to principle range and doing architecture, mentorship, coordinating cross team work, interviewing, evaluating technical decisions, etc.
I got to code this week a bit and it's been a tremendous joy! I see many peers at similar and lower levels (and higher) who have more years and less technical experience and still write lots of code and I suspect that is more what you're talking about. In that case, it's not so much that you've peaked, it's that there's not much to learn and you're doing a bunch of the same shit over and over and that's of course tiring.
I think it also means that everything you interact with outside your space does feel much harder because of the infrequency with which you have interacted with it.
If you've spent your whole career working the whole stack from interfaces to infrastructure then there's really not going to be much that hits you as unfamiliar after a point. Most frameworks recycle the same concepts and abstractions, same thing with programming languages, algorithms, data management etc.
But if you've spent most of your career in one space cranking tickets, those unknown corners are going to be as numerous as the day you started and be much more taxing.
That's just sad. Right when I found love in what I do, my work has no value anymore.
Aren't you still better off than the rest of us who found what they love + invested decades in it before it lost its value. Isn't it better to lose your love when you still have time to find a new one?
I don't think so. Those of us who found what we love and invested decades into it got to spend decades getting paid well to do what we love.
Depends on if their new love provides as much money as their old one, which is probably not likely. I'd rather have had those decades to stash and invest.
A lot of pre-faang engineers dont have the stash you're thinking about. What you meant was "right when I found a lucrative job that I love". What was going on in tech these last 15 years, unfortunately, probably was once in a lifetime.
It's crazy to think back in the 80's programmers had "mild" salaries despite programming back then being worlds more punishing. No libraries, no stack exchange, no forums, no endless memory and infinite compute. If you had a challenging bug you better also be proficient in reading schematics and probing circuits.
on the bright side software evolved much more slowly in the 80s. You could go very far by being an expert in 1 thing.
People had real offices with actual quiet focus time.
User expectations were also much lower.
pros and cons i guess?
This is genuinely such a good take
Especially on the topic of value! We are all intuitively aware that value is highly contextual, but get in a knot trying to rationalize value long past genuine engagement!
Imagine a senior dev who just approves PRs, approves production releases, and prioritizes bug reports and feature requests. LLM watches for errors ceaslessly, reports an issue. Senior dev reviews the issue and assigns a severity to it. Another LLM has a backlog of features and errors to go solve, it makes a fix and submits a PR after running tests and verifying things work on its end.
Why are we pretending like the need for tenacity will go away? Certain problems are easier now. We can tackle larger problems now that also require tenacity.
Even right at this very moment where we have a high-tenacity AI, I'd argue that working with the AI -- that is to say, doing AI coding itself and dealing with the novel challenges that brings requires a lot of stubborn persistence.
Fittingly, George Hinton toiled away for years in relative obscurity before finally being recognized for his work. I was always quite impressed by his "tenacity".
So although I don't think he should have won the Nobel Prize because not really physics, I felt his perseverance and hard work should merit something.
I still find in these instances there's at least a 50% chance it has taken a shortcut somewhere: created a new, bigger bug in something that just happened not to have a unit test covering it, or broke an "implicit" requirement that was so obvious to any reasonable human that nobody thought to document it. These can be subtle because you're not looking for them, because no human would ever think to do such a thing.
Then even if you do catch it, AI: "ah, now I see exactly the problem. just insert a few more coins and I'll fix it for real this time, I promise!"
The value extortion plan writes itself. How long before someone pitches the idea that the models explicitly almost keep solving your problem to get you to keep spending? Would you even know?
First time I've seen this idea, I have a tingling feeling it might become reality sooner rather than later.
That’s far-fetched. It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise. High value to user + lower compute costs = better pricing power and better margins overall.
> far-fetched
Remember Google?
Once it was far-fetched that they would make the search worse just to show you more ads. Now, it is a reality.
With tokens, it is even more direct. The more tokens users spend, the more money for providers.
> Now, it is a reality.
What are the details of this? I'm not playing dumb, and of course I've noticed the decline, but I thought it was a combination of losing the battle with SEO shite and leaning further and further into a 'give the user what you think they want, rather than what they actually asked for' philosophy.
Only if you are paying per token on the API. If you are paying a fixed monthly fee then they lose money when you need to burn more tokens and they lose customers when you can’t solve your problems within that month and max out your session limits and end up with idle time which you use to check if the other providers have caught up or surpassed your current favourite.
Indeed, unlimited plan seems like the only way that makes sense to not have it be guaranteed to be abused by the provider
> It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise.
Unless you’re paying by the token.
I was thinking more of deliberate backdoor in code. RCE is an obvious example, but another one could be bias. "I'm sorry ma'am, computer says you are ineligable for a bank account." These ideas aren't new. They were there in 90s already when we still thought about privacy and accountability regarding technology, and dystopian novels already described them long, long ago.
The free market proposition is that competition (especially with Chinese labs and grok) means that Anthropic is welcome to do that. They're even welcome to illegally collude with OpenAi such that ChatGPT is similarly gimped. But switching costs are pretty low. If it turns out I can one shot an issue with Qwen or Deepseek or Kimi thinking, Anthropic loses not just my monthly subscription, but everyone else's I show that too. So no, I think that's some grade A conspiracy theory nonsense you've got there.
It’s not that crazy. It could even happen by accident in pursuit of another unrelated goal. And if it did, a decent chunk of the tech industry would call it “revealed preference” because usage went up.
LLMs became sycophantic and effusive because those responses were rated higher during RLHF, until it became newsworthy how obviously eager-to-please they got, so yes, being highly factually correct and "intelligent" was already not the only priority.
> But switching costs are pretty low
Switching costs are currently low. Once you're committed to the workflow the providers will switch to prepaying for a year's worth of tokens.
To be clear I don't think that's what they're doing intentionally. Especially on a subscription basis, they'd rather me maximize my value per token, or just not use them. Lulling users into using tokens unproductively is the worst possible option.
The way agents work right now though just sometimes feels that way; they don't have a good way of saying "You're probably going to have to figure this one out yourself".
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This is a good point. For example if you have access to a bunch of slot machines, one of them is guaranteed to hit the jackpot. Since switching from one slot machine to another is easy, it is trivial to go from machine to machine until you hit the big bucks. That is why casinos have such large selections of them (for our benefit).
"for our benefit" lol! This is the best description of how we are all interacting with LLMs now. It's not working? Fire up more "agents" ala gas town or whatever
last time I was at a casino I checked to see what company built the machines, imagine my surprise that it was (by my observation) a single vendor.
As a rational consumer, how would you distinguish between some intentional "keep pulling the slot machine" failure rate and the intrinsic failure rate?
I feel like saying "the market will fix the incentives" handwaves away the lack of information on internals. After all, look at the market response to Google making their search less reliable - sure, an invested nerd might try Kagi, but Google's still the market leader by a long shot.
In a market for lemons, good luck finding a lime.
FWIW, kagi is better than Google
Is this from a page of dating apps playbook?
> These can be subtle because you're not looking for them
After any agent run, I'm always looking the git comparison between the new version and the previous one. This helps catch things that you might otherwise not notice.
And after manually coding I often have an LLM review the diff. 90% of the problems it finds can be discounted, but it’s still a net positive.
And there is this paradox where it becomes harder to detect the problems as the models 'improve'.
You are using it wrong, or are using a weak model if your failure rate is over 50%. My experience is nothing like this. It very consistently works for me. Maybe there is a <5% chance it takes the wrong approach, but you can quickly steer it in the right direction.
you are using it on easy questions. some of us are not.
A lot of people are getting good results using it on hard things. Obviously not perfect, but > 50% success.
That said, more and more people seem to be arriving at the conclusion that if you want a fairly large-sized, complex task in a large existing codebase done right, you'll have better odds with Codex GPT-5.2-Codex-XHigh than with Claude Code Opus 4.5. It's far slower than Opus 4.5 but more likely to get things correct, and complete, in its first turn.
I think a lot of it comes down to how well the user understands the problem, because that determines the quality of instructions and feedback given to the LLM.
For instance, I know some people have had success with getting claude to do game development. I have never bothered to learn much of anything about game development, but have been trying to get claude to do the work for me. Unsuccessful. It works for people who understand the problem domain, but not for those who don't. That's my theory.
It works for hard problems when the person already solves it and just needs the grunt work done
It also works for problems that have been solved a thousand times before, which impresses people and makes them think it is actually solving those problems
Which matches what they are. They're first and foremost pattern recognition engines extraordinaire. If they can identify some pattern that's out of whack in your code compared to something in the training data, or a bug that is similar to others that have been fixed in their training set, they can usually thwack those patterns over to your latent space and clean up the residuals. If comparing pattern matching alone, they are superhuman, significantly.
"Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape. Their ability to pattern match makes reasoning seem more powerful than it actually is. If your bug is within some reasonable distance of a pattern it has seen in training, reasoning can get it over the final hump. But if your problem is too far removed from what it has seen in its latent space, it's not likely to figure it out by reasoning alone.
>"Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape.
What do you mean by this? Especially for tasks like coding where there is a deterministic correct or incorrect signal it should be possible to train.
> It also works for problems that have been solved a thousand times before
So you mean it works on almost all problems?
Don’t use it for hard questions like this then; you wouldn’t use a hammer to cut a plank, you’d try to make a saw instead
> It might become cheaper or it might not
If it does not, this is going to be first technology in the history of mankind that has not become cheaper.
(But anyway, it already costs half compared to last year)
> But anyway, it already costs half compared to last year
You could not have bought Claude Opus 4.5 at any price one year ago I'm quite certain. The things that were available cost half of what they did then, and there are new things available. These are both true.
I'm agreeing with you, to be clear.
There are two pieces I expect to continue: inference for existing models will continue to get cheaper. Models will continue to get better.
Three things, actually.
The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].
interesting post. i wonder if these people go back and introspect on how incorrect they have been? do they feel the need to address it?
No, people do not do that.
This is harmless when it comes to tech opinions but causes real damage in politics and activism.
People get really attached to ideals and ideas, and keep sticking to those after they fail to work again and again.
i don't think it is harmless or we are incentivising people to just say whatever they want without any care for truth. people's reputations should be attached to their predictions.
Some people definitely do but how do they go and address it? A fresh example in that it addresses pure misinformation. I just screwed up and told some neighbors garbage collection was delayed for a day because of almost 2ft of snow. Turns out it was just food waste and I was distracted checking the app and read the notification poorly.
I went back to tell them (do not know them at all just everyone is chattier digging out of a storm) and they were not there. Feel terrible and no real viable remedy. Hope they check themselves and realize I am an idiot. Even harder on the internet.
Do _you_ do that?
i try to yes
As a user of LLMs since GPT-3 there was noticeable stagnation in LLM utility after the release of GPT-4. But it seems the RLHF, tool calling, and UI have all come together in the last 12 months. I used to wonder what fools could be finding them so useful to claim a 10x multiplier - even as a user myself. These days I’m feeling more and more efficiency gains with Claude Code.
That's the thing people are missing, the models plateaued a while ago, still making minor gains to this day, but not huge ones. The difference is now we've had time to figure out the tooling. I think there's still a ton of ground to cover there and maybe the models will improve given that the extra time, but I think it's foolish to consider people who predicted that completely wrong. There are also a lot of mathematical concerns that will cause problems in the near and distant future. Infinite progress is far from a given, we're already way behind where all the boosters thought we'd be my now.
I believe Sam Altman, perhaps the greatest grifter in today’s Silicon Valley, claimed that software engineering would be obsolete by the end of last year.
> The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].
Everybody who bet against Moore's Law was wrong ... until they weren't.
And AI is the reaction to Moore's Law having broken. Nobody gave one iota of damn about trying to make programming easier until the chips couldn't double in speed anymore.
This is exactly backwards: Dennard scaling stopped. Moore’s Law has continued and it’s what made training and running inference on these models practical at interactive timescales.
You are technically correct. The best kind of correct.
However, most people don't know the difference between the proper Moore's Law scaling (the cost of a transistor halves every 2 years) which is still continuing (sort of) and the colloquial version (the speed of a transistor doubles every 2 years) which got broken when Dennard scaling ran out. To them, Moore's Law just broke.
Nevertheless, you are reinforcing my point. Nobody gave a damn about improving the "programming" side of things until the hardware side stopped speeding up.
And rather than try to apply some human brainpower to fix the "programming" side, they threw a hideous number of those free (except for the electricity--but we don't mention that--LOL) transistors at the wall to create a broken, buggy, unpredictable machine simulacrum of a "programmer".
(Side note: And to be fair, it looks like even the strong form of Moore's Law is finally slowing down, too)
If you can turn a few dollars of electricity per hour into a junior-level programmer who never gets bored, tired, or needs breaks, that fundamentally changes the economics of information technology.
And in fact, the agentic looped LLMs are executing much better than that today. They could stop advancing right now and still be revolutionary.
That's not true. Many technologies get more expensive over time, as labor gets more expensive or as certain skills fall by the wayside, not everything is mass market. Have you tried getting a grandfather clock repaired lately?
Repairing grandfather clocks isn't more expensive now because it's gotten any harder; it's because the popularity of grandfather clocks is basically nonexistent compared to anything else to tell time.
"repairing a unique clock" getting costlier doesn't mean technology hasn't gotten cheaper.
check out whether clocks have gotten cheaper in general. the answer is that it has.
there is no economy of scale here in repairing a single clock. its not relevant to bring it up here.
Clocks prices have gone up since 2020. Unless a cheaper better way to make clocks has emerged inflation causes prices to grow.
Luxury watches have gone up, 'clocks' as a technology is cheaper than ever.
You can buy one for 90 cents on temu.
The landing cost for that 90 cent watch has gone way up. Shipping and to some degree taxes has pushed the price higher.
that's not the technology
of course it's silly to talk about manufacturing methods and yield and cost efficiency without having an economy to embed all of this into, but ... technology got cheaper means that we have practical knowledge of how to make cheap clocks (given certain supply chains, given certain volume, and so and so)
we can make very cheap very accurate clocks that can be embedded into whatever devices, but it requires the availability of fabs capable of doing MEMS components, supply materials, etc.
not true, clocks have gone down after accounting for inflation. verified using ChatGPT.
You can't account for inflation because the price increase is inflation.
you can look at a basket of goods that doesn't have your specific product and compare directly
but inflation is the general price level increase, this can be used as a deflator to get the price of whatever product in past/future money amount to see how the price of the product changed in "real" terms (ie. relative to the general price level change)
this is not true
Instead of advancing tenuous examples you could suggest a realistic mechanism by which costs could rise, such as a Chinese advance on Taiwan, effecting TSMC, etc.
Time-keeping is vastly cheaper. People don't want grandfather clocks. They want to tell time. And they can, more accurately, more easily, and much cheaper than their ancestors.
No. You don't get to make "technology gets more expensive over time" statements for deprecated technologies.
Getting a bespoke flintstone axe is also pretty expensive, and has also absolutely no relevance to modern life.
These discussions must, if they are to be useful, center in a population experience, not in unique personal moments.
I purchased a 5T drive in 2019 and the price is higher now despite newer better drives going on the market since.
Not much has down in price over the last few years.
You will get a different bridge. With very different technology. Same as "I can't repair my grandfather clock cheaply".
In general, there are several things that are true for bridges that aren't true for most technology:
* Technology has massively improved, but most people are not realizing that. (E.g. the Bay Bridge cost significantly more than the previous version, but that's because we'd like to not fall down again in the next earthquake)
* We still have little idea how to reason about the cost of bridges in general. (Seriously. It's an active research topic)
* It's a tiny market, with the major vendors forming an oligopoly
* It's infrastructure, not a standard good
* The buy side is almost exclusively governments.
All of these mean expensive goods that are completely non-repeatable. You can't build the same bridge again. And on top of that, in a distorted market.
But sure, the cost of "one bridge, please" has gone up over time.
This seems largely the same as any other technology. The prices of new technologies go down initially as we scale up and optimize it's production, but as soon as demand fades, due to newer technology or whatever, the cost of that technology goes up again.
> But sure, the cost of "one bridge, please" has gone up over time.
Even if you adjust for inflation?
Bought any RAM lately? Phone? GPU in the last decade?
The latest iphone has gone down in price? It's double. I guess the marketing is working.
"Pens are not cheaper, look at this Montblanc" is not a good faith response.
'84 Motorola DynaTAC - ~$12k AfI (adjusted for inflation)
'89 MicroTAC ~$8k AfI
'96 StarTAC ~$2k AfI
`07 iPhone ~$673 AfI
The current average smartphone sells for around $280. Phones are getting cheaper.
Or riding in an uber?
Sure, running an LLM is cheaper, but the way we use LLMs now requires way more tokens than last year.
10x more tokens today cost less than than half of X tokens from ~mid 2024.
ok but the capabilities are also rising. what point are you trying to make?
That it's not getting cheaper?
But it is, capability adjusted, which is the only way it makes sense. You can definitely produce last years capability at a huge discount.
this is accounting for the fact that more tokens are used.
The chart shows that they’re right though. Newer models cost more than older models.
Sure they’re better but that’s moot if older models are not available or can’t solve the problem they’re tasked with.
this is incorrect. the cost to achieve the same task by old models is way higher than by new models.
> Newer models cost more than older models
where did you see this?
On the link you shared, 4o vs 3.5 turbo price per 1m tokens.
There’s no such thing as ”same task by old model”, you might get comparable results or you might not (and this is why the comparison fail, it’s not a comparison), the reason you pick the newer models is to increase chances of getting a good result.
> The dataset for this insight combines data on large language model (LLM) API prices and benchmark scores from Artificial Analysis and Epoch AI. We used this dataset to identify the lowest-priced LLMs that match or exceed a given score on a benchmark. We then fit a log-linear regression model to the prices of these LLMs over time, to measure the rate of decrease in price. We applied the same method to several benchmarks (e.g. MMLU, HumanEval) and performance thresholds (e.g. GPT-3.5 level, GPT-4o level) to determine the variation across performance metrics
This should answer. In your case, GPT-3.5 definitely is cheaper per token than 4o but much much less capable. So they used a model that is cheaper than GPT-3.5 that achieved better performance for the analysis.
OpenAI has always priced newer models lower than older ones.
Not according to their pricing table. Then again I’m not sure what OpenAI model versions even mean anymore, but I would assume 5.2 is in the same family as 5 and 5.2-pro as 5-pro
Check GPT 5.2 vs it's predecessor the 'o' series of reasoning models.
I don't think computation is going to become more expensive, but there are techs that have become so: Nuclear power plants. Mobile phones. Oil extraction.
(Oil rampdown is a survival imperative due to the climate catastrophe so there it's a very positive thing of course, though not sufficient...)
Not true. Bitcoin has continued to rise in cost since its introduction (as in the aggregate cost incurred to run the network).
LLMs will face their own challenges with respect to reducing costs, since self-attention grows quadratically. These are still early days, so there remains a lot of low hanging fruit in terms of optimizations, but all of that becomes negligible in the face of quadratic attention.
> bitcoin
so close! that is a commodity
For Bitcoin that’s by design!
There are plenty of technologies that have not become cheaper, or at least not cheap enough, to go big and change the world. You probably haven't heard of them because obviously they didn't succeed.
cheaper doesnt mean cheap enough to be viable after the bills come due
Concorde?
Supersonic jet engines, rockets to the moon, nuclear power plants, etc. etc. all have become more expensive. Superconductors were discovered in 1911, and we have been making them for as long as we have been making transistors in the 1950s, yet superconductors show no sign of becoming cheaper any time soon.
There have been plenty of technologies in history which do not in fact become cheaper. LLMs are very likely to become such, as I suspect their usefulness will be superseded by cheaper (much cheaper in fact) specialized models.
> you most likely do not pay the actual costs. It might become cheaper or it might not
Why would this be the first technology that doesn't become cheaper at scale over time?
> And you most likely do not pay the actual costs.
This is one of the weakest anti AI postures. "It's a bubble and when free VC money stops you'll be left with nothing". Like it's some kind of mystery how expensive these models are to run.
You have open weight models right now like Kimi K2.5 and GLM 4.7. These are very strong models, only months behind the top labs. And they are not very expensive to run at scale. You can do the math. In fact there are third parties serving these models for profit.
The money pit is training these models (and not that much if you are efficient like chinese models). Once they are trained, they are served with large profit margins compared to the inference cost.
OpenAI and Anthropic are without a doubt selling their API for a lot more than the cost of running the model.
Humans run hot too. Once you factor in the supply chain that keeps us alive, things become surprisingly equivalent.
Eating burgers and driving cars around costs a lot more than whatever # of watts the human brain consumes.
I mean, “equivalent” is an understatement! There’s a reason Claude Code costs less than hiring a full time software engineer…
> Somewhere, there are GPUs/NPUs running hot.
Running at their designed temperature.
> You send all the necessary data, including information that you would never otherwise share.
I've never sent the type of data that isn't already either stored by GitHub or a cloud provider, so no difference there.
> And you most likely do not pay the actual costs.
So? Even if costs double once investor subsidies stop, that doesn't change much of anything. And the entire history of computing is that things tend to get cheaper.
> You and your business become dependent on this major gatekeeper.
Not really. Switching between Claude and Gemini or whatever new competition shows up is pretty easy. I'm no more dependent on it than I am on any of another hundred business services or providers that similarly mostly also have competitors.
My agent struggled for 45 minutes because it tried to do `go run` on a _test.go file, which the compiler repeatedly exited after posting an error message that files named like this cannot be executed using the run command.
So yeah, that wasted a lot of GPU cycles for a very unimpressive result, but with a renewed superficial feeling of competence
I don’t understand this pov. Unfortunately, id pay 10k mo for my cc sub. I wish I could invest in anthropic, they’re going to be the most profitable company on earth
To me this tenacity is often like watching someone trying to get a screw into board using a hammer.
There’s often a better faster way to do it, and while it might get to the short term goal eventually, it’s often created some long term problems along the way.
> And you most likely do not pay the actual costs
Oh my lord you absolutely do not. The costs to oai per token inference ALONE are at least 7x. AT LEAST and from what I’ve heard, much higher.
We can observe how much generic inference providers like deepinfra or together-ai charge for large SOTA models. Since they are not subsidized and they don’t charge 7x of OpenAI, that means OAI also doesn’t have outrageously high per-token costs.
It is also amazing seeing Linux kernel work, scheduling threads, proving interrupts and API calls all without breaking a sweat or injuring its ACL.
With optimizations and new hardware, power is almost a negligible cost. You can get 5.5M tokens/s/MW[1] for kimi k2(=20M/KWH=181M tokens/$) which is 400x cheaper than current pricing. It's just Nvidia/TSMC/other manufacturers eating up the profit now because they can. My bet is that China will match current Nvidia within 5 years.
Electricity is negligible but the dominant cost is the hardware depreciation itself. Also inference is typically memory bandwidth bound so you are limited by how fast you can move weights rather than raw compute efficiency.
Yes, because the margin is like 80% for Nvidia, and 80% again for the manufacturers like Samsung and TSMC. Once the fixed cost like R and D is amortized the same node technology and hardware capacity could be just few single digit percent of current.
AI genius discover brute forcing... what a time to be alive. /s
Like... bro that's THE foundation of CS. That's the principle of The bomb in Turing's time. One can still marvel at it but it's been with us since the beginning.
Are there good guides about how to write Agents or good repos with examples? Also, are there big differences between how you would write one in Codex cli vs Claude code? Can there be run on it interchangeably?
So what is he even coding there all the time?
Does anybody have any info on what he is actually working on besides all the vibe-coding tweets?
There seems to be zero output from they guy for the past 2 years (except tweets)
> There seems to be zero output from they guy for the past 2 years (except tweets)
Well, he made Nanochat public recently and has been improving it regularly [1].
This doesn't preclude that he might be working on other projects that aren't public yet (as part of his work at Eureka Labs).
So, it's generative pre-trained transformers again?
Helper scripts for APIs for applications and tools I know well. LLMs have made my work bearable. Many software providers expose great apis, but expert use cases require data output/input that relies on 50-500 line scripts. Thanks to the models post gpt4.5 most requirements are solvable in 15 minutes when they could have taken multiple workdays to write and check by hand. The only major gap is safe ad-hoc environments to run these in. I provide these helper functions for clients that would love to keep the runtime in the same data environment as the tool, but not all popular software support FaaS style environments that provide something like a simple python env.
He's building Eureka Labs[1], an AI-first education company (can't wait to use it). He's both a strong researcher[2] and an unusually gifted technical communicator. His recent videos[3] are excellent educational material.
More broadly though: someone with his track record sharing firsthand observations about agentic coding shouldn't need to justify it by listing current projects. The observations either hold up or they don't.
I don’t know, but it’s interesting that he and many others come up with this “we should act like LLMs are junior devs”. There is a reason why most junior devs work on fairly separate parts of products, most of the time parts which can be removed or replaced easily, and not an integral part of products: because their code is usually quite bad. Like every few lines contains issues, suboptimal solutions, and full with architectural problems. You basically never trust junior devs with core product features. Yet, we should pretend that an “LLM junior dev” is somehow different. These just signal to me that these people don’t work on serious code.
This is the first question I ask, and every time I get the answer of some monolith that supposedly solves something. Imo, this is completely fine for any personal thing, I am happy when someone says they made an API to compare weekly shopping prices from the stores around them, or some recipe, this makes sense.
However more often than not, someone is just building a monolithic construction that will never be looked at again. For example, someone found that HuggingFace dataloader was slow for some type of file size in combination with some disk.
What does this warrant? A 300000+ line non-reviewed repo to fix this issue. Not a 200-line PR to HuggingFace, no you need to generate 20% of the existing repo and then slap your thing on there.
For me this is puzzling, because what is this for? Who is this for? Usually people built these things for practice, but now its generated, so its not for practice because you made very little effort on it. The only thing I can see that its some type of competence signaling, but here again, if the engineer/manager looking knows that this is generated, it does not have the type of value that would come with such signaling. Either I am naive and people still look at these repos and go "whoa this is amazing", or it's some kind of induced egotrip/delusion where the LLM has convinced you that you are the best builder.
> - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
Starcraft and Factorio are exactly what it is not. Starcraft has a loooot of micro involved at any level beyond mid level play, despite all the "pro macros and beats gold league with mass queens" meme videos. I guess it could be like Factorio if you're playing it by plugging together blueprint books from other people but I don't think that's how most people play.
At that level of abstraction, it's more like grand strategy if you're to compare it to any video game? You're controlling high level pushes and then the units "do stuff" and then you react to the results.
I think the StarCraft analogy is fine, you have to compare it not to macro and micro RTS play, but to INDIVIDUAL UNITS. For your whole career until now, you have been a single Zergling or Probe. Now you are the Commander.
Except that pro starcraft player still micro-manage every single Zergling or probe when necessary, while vibe coders just right click on the ennemy base and hope it'll go well
It's like the Victoria 3 combat system. You just send an army and a general to a given front and let them get to work with no micro. Easy! But of course some percentage of the time they do something crazy like deciding to redeploy from your existential Franco-Prussian war front to a minor colonial uprising...
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I wish the people who wrote this let us know what king of codebases they are working on. They seem mostly useless in a sufficiently large codebase especially when they are messy and interactions aren't always obvious. I don't know how much better Claude is than ChatGPT, but I can't get ChatGPT to do much useful with an existing large codebase.
I've been working in the mobile space since 2009, though primarily as a designer and then product manager. I work in kinda a hybrid engineering/PM job now, and have never been a particularly strong programmer. I definitely wouldn't have thought I could make something with that polish, let alone in 3 months.
That code base is ~98% Claude code.
I don’t know if “antidotal example” is a pun or a typo but I quite like it.
Lol typing on my phone during lunch and meant anecdotal. But let's leave it anyways. :)
That is fun.
Not sure if it's an American pronunciation thing, but I had to stare at that long and hard to see the problem and even after seeing it couldn't think of how you could possibly spell the correct word otherwise.
> Not sure if it's an American pronunciation thing
It's a bad American pronunciation thing like "Febuwary" and "nuculer".
If you pronounce the syllables correctly, "an-ec-dote", "Feb-ru-ar-y", "nu-cle-ar" the spellings follow.
English has it's fair share of spelling stupidities, but if people don't even pronounce the words correctly there is no hope.
Are you using Codex?
I'm not sure how big your repos are but I've been effective working with repos that have thousands of files and tens of thousands of lines of code.
If you're just prototyping it will hit wall when things get unwieldy but that's normally a sign that you need to refactor a bit.
Super strict compiler settings, static analysis, comprehensive tests, and documentation help a lot. As does basic technical design. After a big feature is shipped I do a refactor cycle with the LLM where we do a comprehensive code review and patch things up. This does require human oversight because the LLMs are still lacking judgement on what makes for good code design.
The places where I've seen them be useless is working across repositories or interfacing with things like infrastructure.
It's also very model-dependent. Opus is a good daily driver but Codex is much better are writing tests for some reason. I'll often also switch to it for hard problems that Claude can't solve. Gemini is nice for 'I need a prototype in the next 10 minutes', especially for making quick and dirty bespoke front-ends where you don't care about the design just the functionality.
> tens of thousands of lines of code
Perhaps this is part of it? Tens of thousands of lines of code seems like a very small repo to me.
It's important to understand that he's talking about a specific set of models that were release around november/december, and that we've hit a kind of inflection point in model capabilities. Specifically Anthropic's Opus 4.5 model.
I never paid any attention to different models, because they all felt roughly equal to me. But Opus 4.5 is really and truly different. It's not a qualitative difference, it's more like it just finally hit that quantitative edge that allows me to lean much more heavily on it for routine work.
I highly suggest trying it out, alongside a well-built coding agent like the one offered by Claude Code, Cursor, or OpenCode. I'm using it on a fairly complex monorepo and my impressions are much the same as Karpathy's.
People have said this about every single model release.
Claude and Codex are CLI tools you use to give the LLM context about the project on your local machine or dev environment. The fact that you're using the name "ChatGPT" instead of Codex leads me to believe you're talking about using the web-based ChatGPT interface to work on a large codebase, which is completely beside the point of the entire discussion. That's not the tool anyone is talking about here.
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Almost always, notes like these are going to be about greenfield projects.
Trying to incorporate it in existing codebases (esp when the end user is a support interaction or more away) is still folly, except for closely reviewed and/or non-business-logic modifications.
That said, it is quite impressive to set up a simple architecture, or just list the filenames, and tell some agents to go crazy to implement what you want the application to do. But once it crosses a certain complexity, I find you need to prompt closer and closer to the weeds to see real results. I imagine a non-technical prompter cannot proceed past a certain prototype fidelity threshold, let alone make meaningful contributions to a mature codebase via LLM without a human engineer to guide and review.
I'm using it on a large set of existing codebases full of extremely ugly legacy code, weird build systems, tons of business logic and shipping directly to prod at neckbreaking growth over the last two years, and it's delivering the same type of value that Karpathy writes about.
That was true for me, but is no longer.
It's been especially helpful in explaining and understanding arcane bits of legacy code behavior my users ask about. I trigger Claude to examine the code and figure out how the feature works, then tell it to update the documentation accordingly.
> I trigger Claude to examine the code and figure out how the feature works, then tell it to update the documentation accordingly.
And how do you verify its output isn't total fabrication?
Your docs are a contact. You can verify that contract using integration tests
Contract? These docs are information answering user queries. So if you use a chatbot to generate them, I'd like to be reasonably sure they aren't laden with the fabricated misinformation for which these chatbots are famous.
These models do well changing brownfield applications that have tests because the constraints on a successful implementation are tight. Their solutions can be automatically augmented by research and documentation.
I don't exactly disagree with this but I have seen models simply deleting the tests, or updating the tests to pass and declaring the failures were "unrelated to my changes", so it helpfully fixed them
Yes. You have to treat the model like an eager yet incompetent worker, i.e. don't go full yolo mode and review everything they do.
For me, in just the golang server instance and the core functional package, `cloc` reports over 40k lines of code, not counting other supporting packages. I spent the last week having Claude rip out the external auth system and replace it with a home-grown one (and having GPT-codex review its changes). If anything, Claude makes it easier on me as a solo founder with a large codebase. Rather than having to re-familiarize myself with code I wrote a year ago, I describe it at a high level, point Claude to a couple of key files, and then tell it to figure out what it needs to do. It can use grep, language server, and other tools to poke around and see what's going on. I then have it write an "epic" in markdown containing all the key files, so that future sessions already know the key files to read.
I really enjoyed the process. As TFA says, you have to keep a close eye on it. But the whole process was a lot less effort, and I ended up doing mor than I would otherwise have done.
At my dayjob my team uses it on our main dashboard, which is a pretty large CRUD application. The frontend (Vue) is a horrible mess, as it was originally built by people who know just enough to be dangerous. Over time people have introduced new standards without cleaning up the old code - for example, we have three or four different state management techologies.
For this the LLM struggles a bit, but so does a human. The main issues are it messes up some state that it didnt realise was used elsewhere, and out test coverage is not great. We've seen humans make exactly the same kind of mistakes. We use MCP for Figma so most of the time it can get a UI 95% done, just a few tweaks needed by the operator.
On the backend (Typescript + Node, good test coverage) it can pretty much one-shot - from a plan - whatever feature you give it.
We use opus-4.5 mostly, and sometimes gpt-5.2-codex, through Cursor. You aren't going to get ChatGPT (the web interface) to do anything useful, switch to Cursor, Codex or Claude Code. And right now it is worth paying for the subscription, you don't get the same quality from cheaper or free models (although they are starting to catch up, I've had promising results from GLM-4.7).
I had never used Swift before that and was able to use AI to whip up a fairly full-featured and complex application with a decent amount of code. I had to make some cross-cutting changes along the way as well that impacted quite a few files and things mostly worked fine with me guiding the AI. Mind you this was a year ago so I can only imagine how much better I would fare now with even better AI models. That whole month was spent not only on coding but on learning Swift enough to fix problems when AI started running into circles and then learning about Xcode profiler to optimize the application for speed and improving perf.
I don't know how big sufficiently large codebase is, but we have a 1mil loc Java application, that is ~10years old, and runs POS systems, and Claude Code has no issues with it. We have done full analyses with output details each module, and also used it to pinpoint specific issues when described. Vibe coding is not used here, just analysis.
> They seem mostly useless in a sufficiently large codebase especially when they are messy and interactions aren't always obvious.
What type of documents do you have explaining the codebase and its messy interactions, and have you provided that to the LLM?
Also, have you tried giving someone brand new to the team the exact same task and information you gave to the LLM, and how effective were they compared to the LLM?
> I don't know how much better Claude is than ChatGPT, but I can't get ChatGPT to do much useful with an existing large codebase.
As others have pointed out, from your comment, it doesn't sound like you've used a tool dedicated for AI coding.
(But even if you had, it would still fail if you expect LLMs to do stuff without sufficient context).
The code base I work on at $dayjob$ is legacy, has few files with 20k lines each and a few more with around 10k lines each. It's hard to find things and connect dots in the code base. Dont think LLMs able to navigate and understand code bases of that size yet. But have seen lots of seemingly large projects shown here lately that involve thousands of files and millions of lines of code.
I’ve found that LLMs seem to work better on LLM-generated codebases.
Commercial codebases, especially private internal ones, are often messy. It seems this is mostly due to the iterative nature of development in response to customer demands.
As a product gets larger, and addresses a wider audience, there’s an ever increasing chance of divergence from the initial assumptions and the new requirements.
We call this tech debt.
Combine this with a revolving door of developers, and you start to see Conway’s law in action, where the system resembles the organization of the developers rather than the “pure” product spec.
With this in mind, I’ve found success in using LLMs to refactor existing codebases to better match the current requirements (i.e. splitting out helpers, modularizing, renaming, etc.).
Once the legacy codebase is “LLMified”, the coding agents seem to perform more predictably.
YMMV here, as it’s hard to do large refactors without tests for correctness.
(Note: I’ve dabbled with a test first refactor approach, but haven’t gone to the lengths to suggest it works, but I believe it could)
are LLM codebases not messy?
Claude by default, unless I tell it not to, will write stuff like:
// we need something to be true
somethingPasses = something()
if (!somethingPasses) {
return false
}
// we need somethingElse to be true
somethingElsePasses = somethingElse()
if (!somethingElsePasses) {
return false
}
return true
instead of the very simple boolean logic that could express this in one line, with the "this code does what it obviously does" comments added all over the place.
generally unless you tell it not to, it does things in very verbose ways that most humans would never do, and since there's an infinite number of ways that it can invent absurd verbosity, it is hard to preemptively prompt against all of them.
to be clear, I am getting a huge amount of value out of it for executing a bunch of large refactors and "modernization" of a (really) big legacy codebase at scale and in parallel. but it's not outputting the sort of code that I see when someone prompts it "build a new feature ...", and a big part of my prompts is screaming at it not to do certain things or to refuse the task if it at any point becomes unsure.
Yeah to be clear it will have the same issues as a flyby contributor if prompted to.
Meaning if you ask it “handle this new condition” it will happily throw in a hacky conditional and get the job done.
I’ve found the most success in having it reason about the current architecture (explicitly), and then to propose a set of changes to accomplish the task (2-5 ways), review, and then implement the changes that best suit the scope of the larger system.
The failure mode is missing constraints, not “coding skill”. Treat the model as a generator that must operate inside an explicit workflow: define the invariant boundaries, require a plan/diff before edits, run tests and static checks, and stop when uncertainty appears. That turns “hacky conditional” behaviour into controlled change.
Yes, exactly.
The LLM is onboarding to your codebase with each context window, all it knows is what it’s seen already.
Surely because LLM generated code is part of the training data for the model, so code/patterns it can work with is closer to its training data.
I successfully use Claude Code in a large complex codebase. It's Clojure, perhaps that helps (Clojure is very concise, expressive and hence token-dense).
Perhaps it's harder to "do Closure wrong" than it is to do JavaScript or Python or whatever other extremely flexible multi-paradigm high-level language
Having spent 3 years of my career working with Clojure, I think it actually gives you even more rope to shoot yourself with than Python/JS.
E.g. macros exist in Clojure but not Python/JS, and I've definitely been plenty stumped by seeing them in the codebase. They tend to be used in very "clever" patterns.
On the other hand, I'm a bit surprised Claude can tackle a complex Clojure codebase. It's been a while since I attempted using an LLM for Clojure, but at the time it failed completely (I think because there is relatively little training data compared to other mainstream languages). I'll have to check that out myself
If you have a ChatGPT account, there's nothing stopping you from installing codex cli and using your chatgpt account with it. I haven't coded with ChatGPT for weeks. Maybe a month ago I got utility out of coding with codex and then having ChatGPT look at my open IDE page to give comments, but since 5.2 came out, it's been 100% codex.
Try Claude code. It’s different.
After you tried it, come back.
I think its not Claude code per se itself but rather the (Opus 4.5 model?) or something in an agentic workflow.
I tried a website which offered the Opus model in their agentic workflow & I felt something different too I guess.
Currently trying out Kimi code (using their recent kimi 2.5) for the first time buying any AI product because got it for like 1.49$ per month. It does feel a bit less powerful than claude code but I feel like monetarily its worth it.
Y'know you have to like bargain with an AI model to reduce its pricing which I just felt really curious about. The psychology behind it feels fascinating because I think even as a frugal person, I already felt invested enough in the model and that became my sunk cost fallacy
Shame for me personally because they use it as a hook to get people using their tool and then charge next month 19$ (I mean really Cheaper than claude code for the most part but still comparative to 1.49$)
1. Write good documentation, architecture, how things work, code styling, etc.
2. Put your important dependencies source code in the same directory. E.g. put a `_vendor` directory in the project, in it put the codebase at the same tag you're using or whatever: postgres, redis, vue, whatever.
3. Write good plans and requirements. Acceptance criteria, context, user stories, etc. Save them in markdown files. Review those multiple times with LLMs trying to find weaknesses. Then move to implementation files: make it write a detailed plan of what it's gonna change and why, and what it will produce.
4. Write very good prompts. LLMs follow instructions well if they are clear "you should proactively do X", is a weak instruction if you mean "you must do X".
5. LLMs are far from perfect, and full of limits. Karpathy sums their cons very well in his long list. If you don't know their limits you'll mismanage the expectations and not use them when they are a huge boost and waste time on things they don't cope well with. On top of that: all LLMs are different in their "personality", how they adhere to instruction, how creative they are, etc.
I've been trying Claude on my large code base today. When I give it the requirements I'd give an engineer and so "do it" it just writes garbage that doesn't make sense and doesn't seem to even meet the requirements (if it does I can't follow how - though I'll admit to giving up before I understood what it did, and I didn't try it on a real system). When I forced it to step back and do tiny steps - in TDD write one test of the full feature - it did much better - but then I spent the next 5 hours adjusting the code it wrote to meet our coding standards. At least I understand the code, but I'm not sure it is any faster (but it is a lot easier to see things wrong than come up with green field code).
Which is to say you have to learn to use the tools. I've only just started, and cannot claim to be an expert. I'll keep using them - in part because everyone is demanding I do - but to use them you clearly need to know how to do it yourself.
Have you tried showing it a copy of your coding standards?
I also find pointing it to an existing folder full of code that conforms to certain standards can work really well.
At least some of them that it violated it has seen.
Yeah let's share all your IP for the vague promise that it will somehow work ;)
You just gave me a revelation as to why some people report being unable to get decent results out of coding agents!
(Shrug) If you're not willing to make that tradeoff, you'll be outcompeted by people who are. Your call.
I've been playing around with the "Superpowers" [0] plugin in Claude Code on a new small project and really like it. Simple enough to understand quickly by reading the GitHub repo and seems to improve the output quality of my projects.
There's basically a "brainstorm" /slash command that you go back and forth with, and it places what you came up with in docs/plans/YYYY-MM-DD-<topic>-design.md.
Then you can run a "write-plan" /slash command on the docs/plans/YYYY-MM-DD-<topic>-design.md file, and it'll give you a docs/plans/YYYY-MM-DD-<topic>-implementation.md file that you can then feed to the "execute-plan" /slash command, where it breaks everything down into batches, tasks, etc, and actually implements everything (so three /slash commands total.)
There's also "GET SHIT DONE" (GSD) [1] that I want to look at, but at first glance it seems to be a bit more involved than Superpowers with more commands. Maybe it'd be better for larger projects.
it's all about the context. observe what files it opened, etc.
good luck
Also I never see anyone talking about code reviews, which is one of the primary ways that software engineering departments manage liability. We fired someone recently because they couldn’t explain any of the slop they were trying to get merged. Why tf would I accept the liability of managing code that someone else can’t even explain?
I guess this is fine when you don’t have customers or stakeholders that give a shit lol.
They build Claude Code fully with Claude Code.
Which is equal parts praise and damnation. Claude Code does do a lot of nice things that people just kind of don't bother for time cost / reward when writing TUIs that they've probably only done because they're using AI heavily, but equally it has a lot of underbaked edges (like accidentally shadowing the user's shell configuration when it tries to install terminal bindings for shift-enter even though the terminal it's configuring already sends a distinct shift-enter result), and bugs (have you ever noticed it just stop, unfinished?).
i haven't used Claude Code but come on.. it is a production level quality application used seriously by millions.
Look up the flickering issue. The program was created by dunces.
If you haven't used it, how can you judge its quality level?
Ah, now I understand why @autocomplete suddenly got broken between versions and still not fixed )
What do you even mean by "ChatGPT"? Copy pasting code into chatgpt.com?
AI assisted coding has never been like that, which would be atrocious. The typical workflow was using Cursor with some model of your choice (almost always an Anthropic model like sonnet before opus 4.5 released). Nowadays (in addition to IDEs) it's often a CLI tool like Claude Code with Opus or Codex CLI with GPT Codex 5.2 high/xhigh.
chatGPT is not made to write code. Get out of stone age :)
I'm afraid that we're entering a time when the performance difference between the really cutting edge and even the three-month-old tools is vast
If you're using plain vanilla chatgpt, you're woefully, woefully out of touch. Heck, even plain claude code is now outdated
Why is plain Claude code outdated? I thought that’s what most people are using right now that are AI forward. Is it Ralph loops now that’s the new thing?
Plain Claude Code doesn’t have enough scaffolding to handle large projects
At a base level, people are “upgrading” their Claude Code with custom skills and subagents - all text files saved in .claude/agents|skills.
You can also use their new tasks primitive to basically run a Ralph-like loop
But at the edges, people are using multiple instances, each handling different aspects in parallel - stuff like Gas Town
Tbf you can still get a lot of mileage out of vanilla Claude Code. But I’ve found that even adding a simple frontend design skill improves the output substantially
Is there anywhere where we can learn more about creating your own agents/skills? Maybe some decent public repos that you could recommend.
You can just ask Claude to create them. They’re just markdown files
People who just let the agent code for them, how big of a codebase are you working on? How complex (i.e. is it a codebase that junior programmers could write and maintain)?
I've been an EM for the last 10 of my 25 year Software Engineering career. Coding is, frankly, boring to me anymore, even though I enjoyed doing it most of my career. I had this project I wanted to exist in world but couldn't be bothered to get started.
Decided to figure out what this "vibe coding" nonsense is, and now there's a certain level of joy to all of this again. Being able to clearly define everything using markdown contexts before any code is even written has been a great way to brain dump those 25 years of experience and actually watch something sane get produced.
I then realized I could feed it everything it ever needed to know. Just create a docs/* folder and tell it to read that every session.
Through discovery I learned about CLAUDE.md, and adding skills.
Now I have an /analyst, /engineer, and /devops that I talk to all day with their own logic and limitations, as well as the more general project CLAUDE.md, and dozens of docs/* files we collaborate on.
I'm at the point I'm running happy.engineering on my phone and don't even need to sit in front of the computer anymore.
Interesting!
I wonder if this line
> It will configure an auth_backend.rs and wire up a basic user
over a big enough number of projects will lead to at least 2-3 different user names.
How much did this type of project cost you to make?
rust compiler and redox operating system with modified Qemu for Mac Vulcan metal pipeline ... probably not junior stuff
you might think I'm kidding but Search redox on github, you will find that project and the anonymous contributions
I am curious. What do you want us to see in that github repo?
Feels like a combination of writing very detailed task descriptions and reviewing junior devs. It's horrible. I very much hope this won't be my job.
Minor nitpick: The original measure of a 10x programmer was not the productivity multiplier max/mean, but rather max/min.
> the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*
I have a professor who has researched auto generated code for decades and about six months ago he told me he didn't think AI would make humans obsolete but that it was like other incremental tools over the years and it would just make good coders even better than other coders. He also said it would probably come with its share of disappointments and never be fully autonomous. Some of what he said was a critique of AI and some of it was just pointing out that it's very difficult to have perfect code/specs.
I can sense two classes of coders emerging.
Billionaire coder: a person who has "written" billion lines.
Ordinary coders : people with only couple of thousands to their git blame.
Boris Cherny (Claude Code creator) replies to Andrej Karpathy
I think in less than a year writing code manually will be akin to doing arithmetic problems by hand. Sure you can still code manually, but it's going to be a lot faster to use an LLM (calculator).
People keep using these analogies but I think these are fundamentally different things.
1. hand arithmetic -> using a calculator
2. assembly -> using a high level language
3. writing code -> making an LLM write code
Number 3 does not belong. Number 3 is a fundamentally different leap because it's not based on deterministic logic. You can't depend on an LLM like you can depend on a calculator or a compiler. LLMs are totally different.
There are definitely parallels though. eg you could swap out your compiler for a different one that produces slightly different assembly. Similarly a LLM may implement things differently…but if it works do we care? Probably no more than when you buy software you don’t care precisely what compiler optimisation were used. The precise deterministicness isn’t a key feature
With the llm, it might work or it might not. If it doesn't work, then you have to keep iterating and hand holding it to make it work. Sometimes that process is less optimal than writing the code manually. With a calculator, you can be sure that the first attempt will work. An idiot with a calculator can still produce correct results. An idiot with an llm often cannot outside trivial solutions.
> but if it works so we care?
It often doesn't work. That's the point. A calculator works 100% of the time. A LLM might work 95% of the time, or 80%, or 40%, or 99% depending on what you're doing. This is difference and a key feature.
[deleted]
This is true if your calculator sometimes gave the wrong answer and you had to check each time
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I agree, but writing code is so different to calculations that long-term benefits are less clear.
It doesn't matter how good you are at calculations the answer to 2 + 2 is always 4. There are no methods of solving 2 + 2 which could result in you accidentally giving everyone who reads the result of your calculation write access to your entire DB. But there are different ways to code a system even if the UI is the same, and some of these may neglect to consider permissions.
I think a good parallel here would be to imagine that tomorrow we had access to humanoid robots who could do construction work. Would we want them to just go build skyscrapers and bridges and view all construction businesses which didn't embrace the humanoid robots as akin to doing arithmetic by hand?
You could of course argue that there's no problem here so long as trained construction workers are supervising the robots to make sure they're getting tolerances right and doing good welds, but then what happens 10 years down the road when humans haven't built a building in years? If people are not writing code any more then how can people be expected to review AI generated code?
I think the optimistic picture here is that humans just won't be needed in the future. In theory when models are good enough we should be able to trust the AI systems more than humans. But the less optimistic side of me questions a future in which humans no longer do, or even know how to do such fundamental things.
> How much of society is bottlenecked by digital knowledge work?
I think not much. The real society bottleneck is that a growing number of peeps try to convince each other that life and society are a zero sum game.
I actually disagree with Andrej here re: "Generation (writing code) and discrimination (reading code) are different capabilities in the brain." and I would argue that the only reason he can read code fluently, find issues, etc. is because he has spent year in a non-AI assisted world writing code. As time goes on, he will become substantially worse.
This also bodes incredibly poorly for the next generation, who will mostly in their formative years now avoid writing code and thus fail to even develop a idea of what good code is, how it works/why it works, why you make certain decisions, and not others, etc. and ultimately you will see them become utterly dependent on AI, unable to make progress without it.
IMO outsourcing thinking is going to have incredibly negative consequences for the world at large.
Read your blog post and agree with some of it. Largely I agree with the premise that the 2nd and 3rd order effects of this technology will be more impactful than the 1st order “I was able to code this app I wouldn’t have otherwise even attempted to”. But they are so hard to predict!
Thanks, this rings true to me. The struggle is an investment, and it pays off in good judgement and taste. The same goes for individual codebases too. When I see some weird bug and can immediately guess what’s going wrong and why, that’s my time spent in that codebase paying off. I guess LLM-ing a feature is the inverse, incurring some kind of cognitive debt.
Is coding like piloting, where pilots need a certain number of hours of "flight time" to gain skills, and then a certain number of additional hours each year to maintain their skills? Do developers need to schedule in a certain number of "manually written lines of code" every year?
Thank you for the really excellent summation. I echo your thought 1 to 1. I have found it more difficult to learn new languages or coding skills, because I am no longer forced to go through the painful slow grind of learning.
Painful slow grind? I have always found the learning part what I enjoy most about programming. I don't intend to outsource that a chatbot.
Does one ever still need to learn new languages or coding skills if an AI will be able to do it?
Probably not. But as someone who has learned a few languages, having to outsource a conversation to a machine will never not feel incredibly lame.
I doubt most people feel the same, though.
This question makes me unbelievably sad. Why should anyone learn anything?
I'm not disagreeing.
> if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side.
This is about where I'm at. I love pure claude code for code I don't care about, but for anything I'm working on with other people I need to audit the results - which I much prefer to do in an IDE.
I don't see the AI capacity jump in the recent months at all. For me it's more the opposite, CC works worse than a few months ago. Keeps forgetting the rules from CLAUDE.md, hallucinates function calls, generates tons of over-verbose plans, generates overengineered code. Where I find it a clear net-positive is pure frontend code (HTML + Tailwind), it's spaghetti but since it's just visualization, it's OK.
> Where I find it a clear net-positive is pure frontend code (HTML + Tailwind), it's spaghetti but since it's just visualization, it's OK.
This makes it sound like we're back in the days of FrontPage/Dreamweaver WYSIWYG. Goodness.
Hmm, your comment gave me the idea that maybe we should invent "What You Describe Is What You Get|. To replace HTML+Tailwind spaghetti with prompts generating it.
Sad to hear this attitude towards front-end code. Front-ends are so often already miswritten and full of accessibility pitfalls and I feel like LLMs are gonna dramatically magnify this problem :(
Are you using Opus 4.5? Sounds more like Sonnet.
Yes I'm using Sonnet 4.5. Thanks for the tip, will try Opus 4.5, although costs might become an issue.
The best thing I ever told Claude to do was "Swear profusely when discussing code and code changes". Probably says more about me than Claude, but it makes me snicker.
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
No doubt that good engineers will know when and how to leverage the tool, both for coding and improving processes (design-to-code, requirement collection, task tracking, basic code reviewal, etc) improving their own productivity and of those around them.
Motivated individuals will also leverage these tools to learn more and faster.
And yes, of course it's not the only tool one should use, of course there's still value in talking with proper human experts to learn from, etc, but 90% of the time you're looking for info the LLM will dig it from you reading at the source code of e.g. Postgres and its test rather than asking on chats/stack overflow.
This is a trasformative technology that will make great engineers even stronger, but it will weed out those who were merely valued for their very basic capability of churning something but never cared neither about engineering nor coding, which is 90% of our industry.
> IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now.
I'm honestly considering throwing away my JetBrains subscription and this is year 9 or 10 of me having one. I only open Zed and start yappin' at Claude Code. My employer doesn't even want me using ReSharper because some contractor ruined it for everyone else by auto running all code suggestions and checking them in blindly, making for really obnoxious code diffs and probably introducing countless bugs and issues.
Meanwhile tasks that I know would take any developers months, I can hand-craft with Claude in a few hours, with the same level of detail, but no endless weeks of working on things that'll be done SoonTM.
I do feel a big mood shift after late November. I switched to using Cursor and Gemini primarily and it was big change in my ability to get my ideas into code effectively. The Cursor interface for one got to a place that I really like and enjoy using, but its probably more that the results from the agents themselves are less frustrating. I can deal with the output more now.
I'm still a little iffy on the agent swarm idea. I think I will need to see it in action in an interface that works for me. To me it feels like we are anthropomorphizing agents too much, and that results in this idea that we can put agents into roles and them combine them into useful teams. I can't help seeing all agents as the same automatons and I have trouble understanding why giving an agent with different guideliens to follow, and then having them follow along another agent would give me better results than just fixing the context in the first place. Either that or just working more on the code pipeline to spot issues early on - all the stuff we already test for.
Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media.
Did he coin the term "slopacolypse"? It's a useful one.
I prefer slopocalypse.
That works better.
“slopacolypse” does not make any sense both in writing and pronunciation.
The AGI vibes with Claude Code are real, but the micromanagement tax is heavy. I spend most of my time babysitting agents.
I expect interviews will evolve into "build project X with an LLM while we watch" and audit of agent specs
I've been doing vibe code interviews for nearly a year now. Most people are surprisingly bad with AI tools. We specifically ask them to bring their preferred tool, yet 20–30% still just copy-paste code from ChatGPT.
fun stats: corelation is real, people who were good at vibe code, also had offer(s) with other companies that didn't run vibe code interviews.
Copy pasting from chatgpt is the most secure option.
Not going from home is the most secure way of going out.
It doesn’t work you can’t be productive without agent capable of doing queries to db etc
>Not going from home is the most secure way of going out.
What? I can't parse this sentence. Maybe get an ai to rewrite it?
Interesting you say that, feels like when people were too stupid to google things and "googling something" was a skill that some had and others didn't.
From what I've heard, what few interviews there are for software engineers these days, they do have you use models and see how quickly you can build things.
The interviews I’ve given have asked about how control for AI slop without hurting your colleagues feelings. Anyone can prompt and build, the harder part, as usual for business, is knowing how and when to say, ‘no.’
Sounds great to me. Leetcode is outdated and heavily abused by people who share the questions ahead of time in various forums and chats.
> 80% agent coding
A lot of these things sound cool but sometimes I'm curious what they're actually building
Like, is their bottleneck creativity now then? Are they building naything interedting or using agents to build... things that don't appeal to me, anyway?
I guess it depends what appeal to you.
As an example finding myself in a similar 80% situation, over the last few months I built
- a personal website with my projects and poems
- an app to rework recipes in a format I like from any source (text, video,...)
- a 3d visual version of a project my nephew did for work
- a gym class finder in my area with filters the websites don't provide
- a football data game
- working on a saas for work so typical saas stuff
I was never that productive on personal projects, so this is great for me.
Also the coding part of these projects was not very appealing to me, only the output, so it fits well with AI using.
In the meanwhile I did Advent of Code as usual for the fun of code. Different objectives.
Not sure how he is measuring, I'm still closer to about a 60% success rate. It's more like 20% is an acceptable one-shot, this goes to 60% acceptable with some iteration, but 40% either needs manual intervention to succeed or such significant iteration that manual is likely faster.
I can supervise maybe three agents in parallel before a task requiring significant hand-holding means I'm likely blocking an agent.
And the time an agent is 'restlessly working' on something in usually inversely correlated with the likelihood to succeed. Usually if it's going down a rabbit hole, the correct thing to do is to intervene and reorient it.
It's refreshing to see one of the top minds in AI converge on the same set of thoughts and frustrations as me.
For as fast as this is all moving, it's good to remember that most of us are actually a lot closer to the tip of the spear than we think.
Am working on an iPhone app and impressed with how well Claude is able to generate decent/working code with prompts in plain English. I don’t have previous experience in building apps or swift but have a C++ background. Working in smaller chunks and incrementally adding features rather than a large prompt for the whole app seems more practical, is easier to review and build confidence.
Adding/prompting features one by one, reviewing code and then testing the resulting binary feels like the new programming workflow
Depends what we mean by specialist. If it frontend vs backend then maybe. If it general dev vs some specialist scientific programmer or other field where a generalist won’t have a clue then this seems like a recipe for disaster (literal disasters included).
A big wow moment coming up is going to be GPT 5.* in Codex with Cerebras doing inference. The inference speed is going to be a big unlock, because many tasks are intrinsically serial.
It's going to feel literally like playing God, where you type in what you want and it happens ~instantly.
When?
I don't know when but I'm going off:
- "OpenAI is partnering with Cerebras to add 750MW of ultra low-latency AI compute"
- Sam Altman saying that users want faster inference more than lower cost in his interview.
- My understanding that many tasks are serial in nature.
I'm curious to see what effect this change has on leadership. For the last two years it's been "put everything you can into AI coding, or else!" with quotas and firings and whatever else. Now that AI is at the stage where it can actually output whole features with minimal handholding, is there going to be a Frankenstein moment where leadership realizes they now have a product whose codebase is running away from their engineering team's ability to support it? Does it change the calculus of what it means to be underinvested vs overinvested in AI, and what are the implications?
HN should ban any discussion on “things I learned playing with AI” that don’t include direct artifacts of the thing built.
We’re about a year deep into “AI is changing everything” and I don’t see 10x software quality or output.
Now don’t get me wrong I’m a big fan of AI tooling and think it does meaningfully increase value. But I’m damn tired of all the talk with literally nothing to show for it or back it up.
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> - How much of society is bottlenecked by digital knowledge work?
Any qualified guesses?
I'm not convinced more traders on wall street will allocate capital more effectively leading to economic growth.
Will more programmers grow the economy? Or should we get real jobs ;)
Most of this countries challenges are strictly political. The pittance
of work software can contribute is most likely negligible or destructive (e.g. software buttons in cars or palantir). In other words were picked all the low hanging fruit and all that left is to hang ourselves.
I actually disagree. Having software (AI) that can cut through the technological stuff faster will make people more aware of political problems.
edit:
country's*
all that is left*
What particular setups are getting folks these sorts of results? If there’s a way I could avoid all the babysitting I have to do with AI tools that would be welcome
> If there’s a way I could avoid all the babysitting I have to do with AI tools that would be welcome
OP mentions that they are actually doing the “babysitting”
i use codex cli. work on giving it useful skills. work on the other instruction files. take Karpathy tips around testing and declarativeness
use many simultaneously, and bounce between them to unblock them as needed
build good tools and tests. you will soon learn all the things you did manually -- script them all
Great point about expansion vs speedup. I now have time to build custom tools, implement more features, try out different API designs, get 100% test coverage.. I can deliver more quickly, but can also deliver more overall.
Oh wow! Guy who's current project depends on AI being good is talking about AI being good.
Interesting.
the xcancel link is amusing.
9/10 of the most important social media users use X, like or loath it
Stop whining
It’s a great and insightful review—not over-hyping the coding agent, and not underestimating it either. It acknowledges both its usefulness and its limitations. Embracing it and growing with it is how I see it too.
> It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful
It does hurt, that's why all programmers now need an entrepreneurial mindset... you become if you use your skills + new AI power to build a business.
That is motivational content, but not economics. Most startups will be noise, even more so than before. The value of being a founder ceases when everyone is a founder, when it becomes universal. You will need customers. Nobody wants to buy re-invented-the-wheel-74.0. It lacks character, it lacks soul. Without it, your product will be nothing but noise in a noisy world.
Cope. If you create something that genuinely solves a problem, people will buy no matter what.
Look entrepreneurship has never been easy. In fact it's always been one of the hardest thing ever. I'm just saying... *you don't have to do it*. Do whatever you want lol
Happy to hear what's your solution to avoid becoming totally replaceable and obsolete.
What about the people who dont want to be entrepreneurs?
permanent underclass...
They have to pivot to something else
Or stay ahead of the curve as long as possible, e.g. work on the loop/ralphing
Next milestone: solving authoritarian LLM dependencies. We can’t always get trapped in local minima. Or is that actually okay?
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually.
I've been increasingly using LLM's to code for nearly two years now - and I can definitely notice my brain atrophy. It bothers me. Actually over the last few weeks I've been looking at a major update to a product in production & considered doing the edits manually - at least typing the code from the LLM & also being much more granular with my instructions (i.e. focus on one function at a time). I feel in some ways like my brain is turning into slop & I've been coding for at least 35 years... I feel validated by Karpathy.
Don't be too worried about it.
1. Manual coding may be less relevant (albeit ability to read code, interpret it and understand it will be more) in the future. Likely already is.
2. Any skill you don't practice becomes "weaker". Gonna give you an example. I play chess since my childhood, but sometimes I go months without playing it, even years. When I get back I start losing elo fast. If I was in the top 10% of chess.com, I drop to top 30% in the weeks after. But after few months I'm back at top 10%. Takeaway: your relative ability is more or less the same compared to other practitioners, you're simply rusty.
Thanks for your comment, it set me at ease. I know from experience that you're right on point 2. As for point one, I also tend to agree. AI is such a paradigm shift & rapid/massive change doesn't come without stress. I just need to stay cool about it all ;-)
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
The bits left unsaid:
1. Burning tokens, which we charge you for
2. My CPU does this when I tell it to do bogosort on a million 32-bit integers, it doesn't mean it's a good thing
xcancel?
What is the purpose or benefit of providing a free mirror to x?
Doesn't it end up sparing the x servers and causing their costs to decrease?
I prefer xcancel in part because Twitter doesn't let you view replies etc when not logged in.
Guessing x loses ad revenue when traffic goes to xcancel.
my screen is 60 percent banners about cookies and account creation when I use x
"you can review code just fine even if you struggle to write it."
Well, merely approving code takes no skill at all.
Seriously, that’s a completely nonsense line.
I keep thinking about the TechnoCore from Dan Simmons' Hyperion, where the AIs were serving humans but secretly that was a parasitic relation, where they've been secretly using human brains as distributed processing nodes, essentially harvesting humanity's neural activity for their own computational needs without anyone's knowledge.
I know this is SF, but to me working with those LLMs feels more and more like that, and the atrophy part is real. Not that the model is literally using our brains as compute, but the relationship can become lopsided.
> Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December
Anyone wondering what exactly is he actually building? What? Where?
> The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do.
I would LOVE to have jsut syntax errors produced by LLMs, "subtle conceptual errors that a slightly sloppy, hasty junior dev might do." are neither subtle nor slightly sloppy, they actually are serious and harmful, and no junior devs have no experience to fix those.
> They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?"
Why just not hand write 100 loc with the help of an LLM for tests, documentation and some autocomplete instead of making it write 1000 loc and then clean it up? Also very difficult to do, 1000 lines is a lot.
> Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day.
It's a computer program running in the cloud, what exactly did he expected?
> Speedups. It's not clear how to measure the "speedup" of LLM assistance.
See above
> 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.
mmm not sure, if you don't have domain knowledge you could have an initial stubb at the problem, what when you need to iterate over it? You don't if you don't have domain knowledge on your own
> Fun. I didn't anticipate that with agents programming feels more fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part.
No it's not fun, eg LLMs produce uninteresting uis, mostly bloated with react/html
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually.
My bet is that sooner or later he will get back to coding by hand for periods of time to avoid that, like many others, the damage overreliance on these tools bring is serious.
> Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.
No programming it's not "syntactic details" the practice of programming it's everything but "syntactic details", one should learn how to program not the language X or Y
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
Yet no measurable econimic effects so far
> Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
Did people with a smartphone outperformed photographers?
Lots of very scared, angry developers in these comment sections recently...
Not angry nor scared, I value my hard skills a lot, I'm just wondering why people believe religiously everything AI related. Maybe I'm a bit sick with the excessive hype
There's no fear (a bit of anger I must admit). I suspect nearly all of the reaction against this comes from a similar place to where mine does:
All of the real world code I have had to review created by AI is buggy slop (often with subtle, but weird bugs that don't show up for a while). But on HN I'm told "this is because your co-workers don't know how to AI right!!!!" Then when someone who supposedly must be an expert in getting things done with AI posts, it's always big claims with hand-wavy explanations/evidence.
Then the comments section is littered with no effort comments like this.
Yet oddly whenever anyone asks "show me the thing you built?" Either it looks like every other half-working vibe coded CRUD app... or it doesn't exist/can't be shown.
If you tell me you have discovered a miracle tool, just some me the results. Not taking increasingly ridiculous claims at face value is not "fear". What I don't understand is where comments like yours come from? What makes you need this to be more than it is?
Also note that I'm a heavy LLM user, not anti ai for sure
This is extremely reductive and incredibly dismissive of everything they wrote above.
It's because they don't have a substantive response to it, so they resort to ad hominems.
I've worked extensively in the AI space, and believe that it is extremely useful, but these weird claims (even from people I respect a lot) that "something big and mysterious is happening, I just can't show you yet!" set of my alarms.
When sensible questions are met with ad hominems by supporters it further sets of alarm bells.
I see way more hype that is boosted by the moderators. The scared ones are the nepo babies who founded a vaporware AI company that will be bought by daddy or friends through a VC.
They have to maintain the hype until a somewhat credible exit appears and therefore lash out with boomer memes, FOMO, and the usual insane talking points like "there are builders and coders".
i'm not sure what kind of conspiracy you are hallucinating. do you think people have to "maintain the hype"? it is doing quite well organically.
So well that they're losing billions and OpenAI may go bankrupt this year
what if it doesn't?
better for them! the heck i care about it
This is a low quality curmudgeonly comment
Now that you contributed zero net to the discussion and learned a new word you can go out and play with toys! Good job
You learned a new adjective? If people move beyond "nice", "mean" and "curmudgeonly" they might even read Shakespeare instead of having an LLM producing a summary.
cool.
>Anyone wondering what exactly is he actually building? What? Where?
this is trivially answerable. it seems like they did not do even the slightest bit of research before asking question after question to seem smart and detailed.
I asked many question and you focused on only one, btw yes I did my research, and I know him because I followed almost every tutorial he has on YouTube, and he never mentions clearly what weekend project worked on to make him conclude with such claims. I had a very high respect of him if not that at some point started acting like the Jesus Christ of LLMs
its not clear why you asked that question if you knew the answer to it?
It's been a bit like the boiling frog analogy for me
I started by copy pasting more and more stuff in chatgpt. Then using more and more in-IDE prompting, then more and more agent tools (Claude etc). And suddenly I realise I barely hand code anymore
For sure there's still a place for manual coding, especially schemas/queries or other fiddly things where a tiny mistake gets amplified, but the vast majority of "basic work" is now just prompting, and honestly the code quality is _better_ that it was before, all kinds of refactors I didn't think about or couldn't be bothered with have almost automatically
And people still call them stochastic parrots
I've had the opposite experience, it's been a long time listening to people going "It's really good now" before it developed to a permutation that was actually worth the time to use it.
ChatGPT 3.5/4 (2023-2024): The chat interface was verbose and clunky and it was just... wrong... like 70+% of the time. Not worth using.
CoPilot autocomplete and Gitlab Duo and Junie (late 2024-early 2025): Wayyy too aggressive at guessing exactly what I wasn't doing and hijacked my tab complete when pre-LLM type-tetris autocomplete was just more reliable.
Copilot Edit/early Cursor (early 2025): Ok, I can sort of see uses here but god is picking the right files all the time such a pain as it really means I need to have figured out what I wanted to do in such detail already that what was even the point? Also the models at that time just quickly descended into incoherency after like three prompts, if it went off track good luck ever correcting it.
Copilot Agent mode / Cursor (late 2025): Ok, great, if the scope is narrowly scoped, and I'm either going to write the tests for it or it's refactoring existing code it could do something. Like something mechanical like the library has a migration where we need to replace the use of methods A/B/C and replace them with a different combination of X/Y/Z. great, it can do that. Or like CRUD controller #341. I mean, sure, if my boss is going to pay for it, but not life changing.
Zed Agent mode / Cursor agent mode / Claude code (early 2026): Finally something where I can like describe the architecture and requirements of a feature, let it code, review that code, give it written instructions on how to clean it up / refactor / missing tests, and iterate.
But that was like 2 years of "really it's better and revolutionary now" before it actually got there. Now maybe in some languages or problem domains, it was useful for people earlier but I can understand people who don't care about "but it works now" when they're hearing it for the sixth time.
And I mean, what one hand gives the other takes away. I have a decent amount of new work dealing with MRs from my coworkers where they just grabbed the requirements from a stakeholder, shoved it into Claude or Cursor and it passed the existing tests and it's shipped without much understanding. When they wrote them themselves, they tested it more and were more prepared to support it in production...
I find myself even for small work, telling CC to fix it for me is better as it usually belongs to a thread of work, and then it understands the big picture better.
> And people still call them stochastic parrots
Both can be true. You're tapping into every line of code publicly available, and your day-to-day really isn't that unique. They're really good at this kind of work.
> Slopacolypse
Really… REALLY not looking forward to getting this word spammed at me the next 6-12 months… even less so seeing the actual manifestation.
> TLDR
This should be at the start?
I actually have been thinking of trying out ClaudeCode/OpenCode over this past week… can anyone provide experience, tips, tricks, ref docs?
My normal workflow is using Free-tier ChatGPT to help me interrogate or plan my solution/ approach or to understand some docs/syntax/best practice of which I’m not familiar. then doing the implementation myself.
Claude code official docs are quite nice - that's where I started.
The section on IDEs/agent swarms/fallibility resonated a lot for me; I haven't gone quite as far as Karpathy in terms of power usage of Claude Code, but some of the shifts in mistakes (and reality vs. hype) analysis he shared seems spot on in my (caveat: more limited) experience.
> "IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits."
> LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building
as the former, i've never felt _more ahead_ than now due to all of the latter succumbing to the llm hype
I coded up a crossword puzzle game using agentic dev this weekend. Claude and Codex/GPT. Had to seriously babysit and rewrite much of it, though, sure, I found it “cool” what it could do.
Writing code in many cases is faster to me than writing English (that is how PLs are designed, btw!) LLM/agentic is very “neat” but still a toy to the professional, I would say. I doubt reports like this one. For those of us building real world products with shelf-lives (Is Andrej representative of this archetype?), I just don’t see the value-add touted out there. I’d love to be proven wrong. But writing code (in code, not English), to me and many others, is still faster than reading/proving it.
I think there’s a combination of fetishizing and Stockholm syndroming going on in these enthusiastic self-reports. PMW.
> The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking.
If current LLMs are ever deployed in systems harboring the big red button, they WILL most definitely somehow press that button.
US MIC are already planning on integrating fucking Grok into military systems. No comment.
Including classified systems. What could possibly go wrong
the US is going to stop the chinese by mass production of illegal pornography?
fwiw, the same is true for humans. Which is why there's a whole lot of process and red tape around that button. We know how to manage risk. We can choose to do that for LLM usage, too.
If instead we believe in fantasies of a single all-knowing machine god that is 100% correct at all times, then... we really just have ourselves to blame. Might as well just have spammed that button by hand.
I don't know about you guys but most of the time it's spitting nonsense models in sqlalchemy and I have to constantly correct it to the point where I am back at writing the code myself. The bugs are just astonishing and I lose control of the codebase after some time to the point where reviewing the whole thing just takes a lot of time.
On the contrary if it was for a job in a public sector I would just let the LLM spit out some output and play stupid, since salary is very low.
Senpai has taken the words out of my mouth and put them on the page.
The whole thing is about getting rid of experts and let the entry level idiots do all the work. The coders become expendable. And people do not see the chasm staring back at them :D. LLMs in their current form redistributes "intelligence" and expertise to the average joes for mere pennies. It should be much much more expensive, or it will disrupt the whole ecosystem. If it becomes even more intelligent it must be bludgeoned to death a.k.a. regulated like hell, otherwise the ensuing disruption will kill the job market and in the long term human values.
As an added plus: those, who already have wealth will benefit the most, instead of the masses. Since the distribution and dissemination of new projects is at the same level as before, meaning you would need a lot of money. So no matter how clever you are with an llm, if you don't have the means to distribute it you will be left in the dirt.
Honestly, how long do you guys think we have left as SWEs with high pay? Like the SWE job will still exist, but with a much lower technical barrier of entry, it strikes me that the pay is going to decrease a lot. Obviously BigCo codebases are extremely complex, more than Claude Code can handle right now, but I'd say there's definitely a timer running here. The big question for my life personally is whether I can reach certain financial milestones before my earnings potential permanently decreases.
It's counterintuitive but something becoming easier doesn't necessarily mean it becomes cheap. Programming has arguably been the easiest engineering discipline to break into by sheer force of will for the past 20+ years, and the pay scales you see are adapted to that reality already.
Empowering people to do 10 times as much as they could before means they hit 100 times the roadblocks. Again, in a lot of ways we've already lived in that reality for the past many years. On a task-by-task basis programming today is already a lot easier than it was 20 years ago, and we just grew our desires and the amount of controls and process we apply. Problems arise faster than solutions. Growing our velocity means we're going to hit a lot more problems.
I'm not saying you're wrong, so much as saying, it's not the whole story and the only possibility. A lot of people today are kept out of programming just because they don't want to do that much on a computer all day, for instance. That isn't going to change. There's still going to be skills involved in being better than other people at getting the computers to do what you want.
Also on a long term basis we may find that while we can produce entry-level coders that are basically just proxies to the AI by the bucketful that it may become very difficult to advance in skills beyond that, and those who are already over the hurdle of having been forced to learn the hard way may end up with a very difficult to overcome moat around their skills, especially if the AIs plateau for any period of time. I am concerned that we are pulling up the ladder in a way the ladder has never been pulled up before.
I think the senior devs will be fine. They're like lawyers at this point - everyone is too scared they'll screw up and will keep them around
The juniors though will radically have to upskill. The standard junior dev portfolio can be replicated by claude code in like three prompts
The game has changed and I don't think all the players are ready to handle it
Supply and demand. There will continue to be a need for engineers to manage these systems and get them to do the thing you actually want, to understand implications of design tradeoffs and help stakeholders weigh the pros and cons. Some people will be better at it than others. Companies will continue to pay high premiums for such people if their business depends on quality software.
I think to give yourself more context you should ask about the patterns that led to SWEs having such high pay in the last 10-15 years and why it is you expected it to stay that way.
I personally think the barrier is going to get higher, not lower. And we will be back expected to do more.
I think the pay is going to skyrocket for senior devs within a few years, as training juniors that can graduate past pure LLM usage becomes more and more difficult.
Day after day the global quality of software and learning resources will degrade as LLM grey goo consumes every single nook and cranny of the Internet. We will soon see the first signs of pure cargo cult design patterns, conventions and schemes that LLMs made up and then regurgitated. Only people who learned before LLMs became popular will know that they are not to be followed.
People who aren't learning to program without LLMs today are getting left behind.
Yeah, all of this. Plus companies have avoided hiring and training juniors for 3 or 4 years now (which is more related to interest rates than AI). Plus existing seniors who deskill themselves by outsourcing their brain to AI. Seniors who know actually what they're doing are going to be in greater demand.
That is assuming that LLMs plateau in capability, if they haven't already, which I think is highly likely.
> like the SWE job will still exist, but with a much lower technical barrier of entry
its opposite, now in addition to all other skills, you need skill how to handle giant codebases of viobe-coded mess using AI.
>LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
Quite insightful.
Imagine taking career advice from people who will never need to be employed again in order to survive.
Yes, typically you take since from people who've been successful at their career. Are you suggesting we should be taking career advice from high school freshmen instead?
I'm nitpicking on the atrophy bit. He can afford to have his skills or his brain atrophied. His followers though?
Nevermind the fact he became successful _because_ of his skills and his brain.
Are game developers vibe coding with agents?
It's such a visual and experiential thing that writing true success criteria it can iterate on seems like borderline impossible ahead of time.
It might be biased to Reddit/Twitter users but from what I've seen game developers seem to be much more averse towards using AI (even for coding) than other fields.
Which is curious since prototyping helps a lot in gamedev.
I don't "vibe code" but when I use an LLM with a game I usually branch out into several experiments which I don't have to commit to. Thus, it just makes that iteration process go faster.
Or slower, when the LLM doesn't understand what I want, which is a bigger issue when you spawn experiments from scratch (and have given limited context around what you are about to do).
A big problem is that a lot of game logic is done in visual scripting (e.g unreal blueprints) which AI tools have no idea about
I'm trying it out with Godot for my little side projects. It can handle writing the GUI files for nodes and settings. The workflow is asking cursor to change something, I review the code changes, then load up the game in Godot to check out the changes. Works pretty well. I'm curious if any Unity or Unreal devs are using it since I'm sure its a similar experience.
Vibe coding in Unreal Engine is of limited use. It obviously helps with C++, but so much of your time is doing things that are not C++. It hurts a lot that UE relies heavily on blueprints, if they were code you could just vibecode a lot of that.
So I'm curious, whats the actual quality control.
Like, do these guys actually dog food real user experience, or are they all admins with the fast lane to the real model while everyone outside the org has to go through the 10 layers of model sheding, caching and other means and methods of saving money.
We all know these models are expensive as fuck to run and these companies are degrading service, A+B testing, and the rest. Do they actually ponder these things directly?
Just always seems like people are on drugs when they talk about the capabilities, and like, the drugs could be pure shit (good) or ditch weed, and we call just act like the pipeline for drugs is a consistent thing but it's really not, not at this stage where they're all burning cash through infrastructure. Definitely, like drug dealers, you know they're cutting the good stuff with low cost cached gibberish.
> Definitely, like drug dealers, you know they're cutting the good stuff with low cost cached gibberish.
Can confirm. My partner's chatGPT wouldnt return anything useful for her given a specific query involving web use, while i got the desired result sitting side by side. She contacted support and they said nothing they can do about it, her account is in an A/B test group without some features removed. I imagine this saves them considerable resources despite still billing customers for them.
how much this is occurring is anyones guess
If you access a model through an openrouter provider it might be quantized (akin to being "cut with trash"), but when you go directly to Anthropic or OpenAI you are getting access to the same APIs as everyone else. Even top-brass folks within Microsoft use Anthropic and OpenAI proper (not worth the red-tape trouble to go directly through Azure). Also, the creator and maintainer of Claude, Boris Cherny, was a bit of an oddball but one of the comparatively nicer people at Anthropic, and he indicated he primarily uses the same Anthropic APIs as everyone else (which makes sense from a product development perspective).
The underlying models are all actually really undifferentiated under the covers except for the post-training and base prompts. If you eliminate the base prompts the models behave near identically.
A conspiracy would be a helluva lot more interesting and fun, but I've spoken to these folks firsthand and it seems they already have enough challenges keeping the beast running.
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I don't know if it's fair to call him an ai addict or deduce that his ego is bruised. But I do wonder whether karpathy's agentic llm experiences are based on actual production code or pet projects. Based on a few videos I have seen of his, I am guessing it's the latter. Also, he is a research scientist (probably a great one), not a software developer. I agree with the op that karpathy should not be given much attention in this topic i.e llms for software development.
"addict"
Great idea! Le's pathalogize another thing! I love quickly othering whole concepts and putting them in my brain's "bad" box so I can feel superior.
He said “…who has never written any production software…” yet you show toy projects instead.
Well done.
I don't agree with the parent commenters characterization of Karpathy, but these projects are just simple toy projects. They're educational material, not production level software.
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Once again, 80% of the comments here are from boomers.
HN used to be a proper place for people actually curious about technology
I'm almost a boomer and I agree. THis dichotomy is weird. I am retired EE and I love the ability to just have AI do whatever I want for me. I have it manage a 10 node proxmox cluster in my basement via ansible and terraform. I can finally do stuff I always wanted but had no time. I got sick of editing my kids sports videos for highlights in Davinci Resolve so just asked claude to write a simple app for me and then use all my random video cards in my boxes to render clips in parallel and so on. Tech is finally fun again when I do not have to dedicate days to understand some new framework. It does feel a little like late 1990's computing when everyone was making geocities webpages but those days were more fun. Now with local llms getting strong as well and speaking to my PC instead of typing it feels like SciFi, so yeah, I do not get this hacker news hand wringing about code craft.
So what is your workflow now with this app for kids sports highlights?
Also interested
Ya it's so weird lol
Instead of a 17 paragraph twitter post with a baffling TLDR at the end why not just record your screen and _demonstrate_ all of what you're describing?
Otherwise, I think you're incidentally right, your "ego" /is/ bruised, and you're looking for a way out by trying to prognosticate on the future of the technology. You're failing in two different ways.
I worry about the "brain atrophy" part, as I've felt this too. And not just atrophy, but even moreso I think it's evolving into "complacency".
Like there have been multiple times now where I wanted the code to look a certain way, but it kept pulling back to the way it wanted to do things. Like if I had stated certain design goals recently it would adhere to them, but after a few iterations it would forget again and go back to its original approach, or mix the two, or whatever. Eventually it was easier just to quit fighting it and let it do things the way it wanted.
What I've seen is that after the initial dopamine rush of being able to do things that would have taken much longer manually, a few iterations of this kind of interaction has slowly led to a disillusionment of the whole project, as AI keeps pushing it in a direction I didn't want.
I think this is especially true if you're trying to experiment with new approaches to things. LLMs are, by definition, biased by what was in their training data. You can shock them out of it momentarily, whish is awesome for a few rounds, but over time the gravitational pull of what's already in their latent space becomes inescapable. (I picture it as working like a giant Sierpinski triangle).
I want to say the end result is very akin to doom scrolling. Doom tabbing? It's like, yeah I could be more creative with just a tad more effort, but the AI is already running and the bar to seeing what the AI will do next is so low, so....
It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.
This would be fine if not for one thing: the meta-skill of learning to use the LLM depreciates too. Today's LLM is gonna go away someday, the way you have to use it will change. You will be on a forever treadmill, always learning the vagaries of using the new shiny model (and paying for the privilege!)
I'm not going to make myself dependent, let myself atrophy, run on a treadmill forever, for something I happen to rent and can't keep. If I wanted a cheap high that I didn't mind being dependent on, there's more fun ones out there.
> let myself atrophy, run on a treadmill forever, for something
You're lucky to afford the luxury not to atrophy.
It's been almost 4 years since my last software job interview and I know the drills about preparing for one.
Long before LLMs my skills naturally atrophy in my day job.
I remember the good old days of J2ME of writing everything from scratch. Or writing some graph editor for universiry, or some speculative, huffman coding algorithm.
That kept me sharp.
But today I feel like I'm living in that netflix series about people being in Hell and the Devil tricking them they're in Heaven and tormenting them: how on planet Earth do I keep sharp with java, streams, virtual threads, rxjava, tuning the jvm, react, kafka, kafka streams, aws, k8s, helm, jenkins pipelines, CI-CD, ECR, istio issues, in-house service discovery, hierarchical multi-regions, metrics and monitoring, autoscaling, spot instances and multi-arch images, multi-az, reliable and scalable yet as cheap as possible, yet as cloud native as possible, hazelcast and distributed systems, low level postgresql performance tuning, apache iceberg, trino, various in-house frameworks and idioms over all of this? Oh, and let's not forget the business domain, coding standards, code reviews, mentorships and organazing technical events. Also, it's 2026 so nobody hires QA or scrum masters anymore so take on those hats as well.
So LLMs it is, the new reality.
This is a very good point. Years ago working in a LAMP stack, the term LAMP could fully describe your software engineering, database setup and infrastructure. I shudder to think of the acronyms for today's tech stacks.
Ya I agree it's totally crazy.... but, do most app deployments need even half that stuff? I feel like most apps at most companies can just build an app and deploy it using some modern paas-like thing.
> I feel like most apps at most companies can just build an app and deploy it using some modern paas-like thing.
Most companies (in the global, not SV sense) would be well served by an app that runs in a Docker container in a VPS somewhere and has PostgreSQL and maybe Garage, RabbitMQ and Redis if you wanna get fancy, behind Apache2/Nginx/Caddy.
But obviously that’s not Serious Business™ and won’t give you zero downtime and high availability.
Though tbh most mid-size companies would also be okay with Docker Swarm or Nomad and the same software clustered and running behind HAProxy.
But that wouldn’t pad your CV so yeah.
> I happen to rent and can't keep
This is my fear - what happens if the AI companies can't find a path to profitability and shut down?
Businesses too. For two years it's been "throw everything into AI." But now that shit is getting real, are they really feeling so coy about letting AI run ahead of their engineering team's ability to manage it? How long will it be until we start seeing outages that just don't get resolved because the engineers have lost the plot?
From what I am seeing, no one is feeling coy simply because of the cost savings that management is able to show the higher-ups and shareholders. At that level, there's very little understanding of anything technical and outages or bugs will simply get a "we've asked our technical resources to work on it". But every one understands that spending $50 when you were spending $100 is a great achievement. That's if you stop and not think about any downsides. Said management will then take the bonuses and disappear before the explosions start with their resume glowing about all the cost savings and team leadership achievements. I've experienced this first hand very recently.
Of all the looming tipping points whereby humans could destroy the fabric of their existence, this one has to be the stupidest. And therefore the most likely.
How long until “the LLM did it it” is just as effective as “AWS is down, not my fault”?
Never because the only reason that works with Amazon is that everyone is down at the exact same time.
If you’re just a gladhander for an algorithm, what are you really needed for?
This to me is the point.. LLMs can't be responsible for things. It sits with a human.
I assume you're living in a city. You're already renting out a lot of things to others (security, electricity, water, food, shelter, transportation), what is different with white collar work?
>the city gets destroyed
vs.
>a company goes bankrupt or pivots
I can see a few differences.
I have deliberately moderated my use of AI in large part for this reason. For a solid two years now I've been constantly seeing claims of "this model/IDE/Agent/approach/etc is the future of writing code! It makes me 50x more productive, and will do the same for you!" And inevitabely those have all fallen by the wayside and been replaced by some new shiny thing. As someone who doesn't get intrinsic joy out of chasing the latest tech fad I usually move along and wait to see if whatever is being hyped really starts to take over the world.
This isn't to say LLMs won't change software development forever, I think they will. But I doubt anyone has any idea what kind of tools and approaches everyone will be using 5 or 10 years from now, except that I really doubt it will be whatever is being hyped up at this exact moment.
HN is where I keep hearing the “50× more productive” claims the most. I’ve been reading 2024 annual reports and 2025 quarterlies to see whether any of this shows up on the other side of the hype.
So far, the only company making loud, concrete claims backed by audited financials is Klarna and once you dig in, their improved profitability lines up far more cleanly with layoffs, hiring freezes, business simplification, and a cyclical rebound than with Gen-AI magically multiplying output. AI helped support a smaller org that eliminated more complicated financial products that have edge cases, but it didn’t create a step-change in productivity.
If Gen-AI were making tech workers even 10× more productive at scale, you’d expect to see it reflected in revenue per employee, margins, or operating leverage across the sector.
We’re just not seeing that yet.
> If Gen-AI were making tech workers even 10× more productive at scale, you’d expect to see it reflected in revenue per employee, margins, or operating leverage across the sector.
If we’re even just talking a 2x multiplier, it should show up in some externally verifiable numbers.
I have friends who make such 50x productivity claims. They are correct if we define productivity as creating untested apps and games and their features that will never ship --- or be purchased, even if they were to ship. Thus, “productivity” has become just another point of contention.
> It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.
I agree with the sentiment but I would have framed it differently. The LLM is a tool, just like code completion or a code generator. Right now we focus mainly on how to use a tool, the coding agent, to achieve a goal. This takes place at a strategic level. Prior to the inception of LLMs, we focused mainly on how to write code to achieve a goal. This took place at a tactical level, and required making decisions and paying attention to a multitude of details. With LLMs our focus shifts to a higher-level abstraction. Also, operational concerns change. When writing and maintaining code yourself, you focus on architectures that help you simplify some classes of changes. When using LLMs, your focus shifts to building context and aiding the model effectively implement their changes. The two goals seem related, but are radically different.
I think a fairer description is that with LLMs we stop exercising some skills that are only required or relevant if you are writing your code yourself. It's like driving with an automatic transmission vs manual transmission.
Previous tools have been deterministic and understandable. I write code with emacs and can at any point look at the source and tell you why it did what it did. But I could produce the same program with vi or vscode or whatever, at the cost of some frustration. But they all ultimately transform keystrokes to a text file in largely the same way, and the compiler I'm targeting changes that to asm and thence to binary in a predictable and visible way.
An LLM is always going to be a black box that is neither predictable nor visible (the unpredictability is necessary for how the tool functions; the invisibility is not but seems too late to fix now). So teams start cargo culting ways to deal with specific LLMs' idiosyncrasies and your domain knowledge becomes about a specific product that someone else has control over. It's like learning a specific office suite or whatever.
> An LLM is always going to be a black box that is neither predictable nor visible (the unpredictability is necessary for how the tool functions; the invisibility is not but seems too late to fix now)
So basically, like a co-worker.
That's why I keep insisting that anthropomorphising LLMs is to be embraced, not avoided, because it gives much better high-level, first-order intuition as to where they belong in a larger computing system, and where they shouldn't be put.
> So basically, like a co-worker.
Arguably, though I don't particularly need another co-worker. Also co-workers are not tools (except sometimes in the derogatory sense).
Sort of except it seems the more the co-worker does the job it atrophies my ability to understand.. So soon we'll all be that annoyingly ignorant manager saying, "I don't know, I want the button to be bigger". Yay?
> and can at any point look at the source and tell you why it did what it did
Even years later? Most people can’t unless there’s good comments and design. Which AI can replicate, so if we need to do that anyway, how is AI specially worse than a human looking back at code written poorly years ago?
I mean, Emacs's oldest source files are like 40 years old at this point, and yes they are in fact legible? I'm not sure what you're asking -- you absolutely can (and if you use it long enough, will) read the source code of your text editor.
Well especially the lisp parts!
The little experience I have with LLM confidently shows that LLMs are much better at navigating and modifying a well structured code base. And they struggle, sometimes to a point where they can't progress at all, if tasked to work on bad code. I mean, the kind of bad you always get after multiple rounds of unsupervised vibe coding.
I think I should write more about but I have been feeling very similar. I've been recently exploring using claude code/codex recently as the "default", so I've decided to implement a side project.
My gripe with AI tools in the past is that the kind of work I do is large and complex and with previous models it just wasn't efficient to either provide enough context or deal with context rot when working on a large application - especially when that application doesn't have a million examples online.
I've been trying to implement a multiplayer game with server authoritative networking in Rust with Bevy. I specifically chose Bevy as the latest version was after Claude's cut off, it had a number of breaking changes, and there aren't a lot of deep examples online.
Overall it's going well, but one downside is that I don't really understand the code "in my bones". If you told me tomorrow that I had optimize latency or if there was a 1 in 100 edge case, not only would I not know where to look, I don't think I could tell you how the game engine works.
In the past, I could not have ever gotten this far without really understanding my tools. Today, I have a semi functional game and, truth be told, I don't even know what an ECS is and what advantages it provides. I really consider this a huge problem: if I had to maintain this in production, if there was a SEV0 bug, am I confident enough I could fix it? Or am I confident the model could figure it out? Or is the model good enough that it could scan the entire code base and intuit a solution? One of these three questions have to be answered or else brain atrophy is a real risk.
I'm worried about that too. If the error is reproducible, the model can eventually figure it out from experience. But a ghost bug that I can't pattern? The model ends up in a "you're absolutely right" loop as it incorrectly guesses different solutions.
Are ghost bugs even real?
My first job had the Devs working front-line support years ago. Due to that, I learnt an important lessons in bug fixing.
Always be able to re-create the bug first.
There are no such thing as ghost bugs, you just need to ask the reporter the right questions.
Unless your code is multi-threaded, to which I say, good luck!
Historically I would have agreed with you. But since the rise of LLM-assisted coding, I've encountered an increasing number of things I'd call clear "ghost bugs" in single threaded code. I found a fun one today where invoking a process four times with a very specific access pattern would cause a key result of the second invocation to be overwritten. (It is not a coincidence, I don't think, that these are exactly the kind of bugs a genAI-as-a-service provider might never notice in production.)
I ran into similar issues with context rot on a larger backend project recently. I ended up writing a tool that parses the AST to strip out function bodies and only feeds the relevant signatures and type definitions into the prompt.
It cuts down the input tokens significantly which is nice for the monthly bill, but I found the main benefit is that it actually stops the model from getting distracted by existing implementation details. It feels a bit like overengineering but it makes reasoning about the system architecture much more reliable when you don't have to dump the whole codebase into the context window.
> I've been trying to implement a multiplayer game with server authoritative networking in Rust with Bevy. I specifically chose Bevy as the latest version was after Claude's cut off, it had a number of breaking changes, and there aren't a lot of deep examples online.
I am interested in doing something similar (Bevy. not multiplayer).
I had the thought that you ought be able to provide a cargo doc or rust-analyzer equivalent over MCP? This... must exist?
I'm also curious how you test if the game is, um... fun? Maybe it doesn't apply so much for a multiplayer game, I'm thinking of stuff like the enemy patterns and timings in a soulslike, Zelda, etc.
I did use ChatGPT to get some rendering code for a retro RCT/SimCity-style terrain mesh in Bevy and it basically worked, though several times I had to tell it "yeah uh nothing shows up", at which point is said "of course! the problem is..." and then I learned about mesh winding, fine, okay... felt like I was in over my head and decided to go to a 2D game instead so didn't pursue that further.
>I had the thought that you ought be able to provide a cargo doc or rust-analyzer equivalent over MCP? This... must exist?
I've found that there are two issues that arise that I'm not sure how to solve. You can give it docs and point to it and it can generally figure out syntax, but the next issue I see is that without examples, it kind of just brute forces problems like a 14 year old.
For example, the input system originally just let you move left and right, and it popped it into an observer function. As I added more and more controls, it began to litter with more and more code, until it was ~600 line function responsible for a large chunk of game logic.
While trying to parse it I then had it refactor the code - but I don't know if the current code is idiomatic. What would be the cargo doc or rust-analyzer equivalent for good architecture?
Im running into this same problem when trying to claude code for internal projects. Some parts of the codebase just have really intuitive internal frameworks and claude code can rip through them and provide great idiomatic code. Others are bogged down by years of tech debt and performance hacks and claude code can't be trusted with anything other than multi-paragraph prompts.
>I'm also curious how you test if the game is, um... fun?
Lucky enough for me this is a learning exercise, so I'm not optimizing for fun. I guess you could ask claude code to inject more fun.
> Eventually it was easier just to quit fighting it and let it do things the way it wanted.
I wouldn't have believed it a few tears ago if you told me the industry would one day, in lockstep, decide that shipping more tech-debt is awesome. If the unstated bet doesn't pay off, that is, AI development will outpace the rate it generates cruft, then there will be hell to pay.
As someone who's been commissioned many times before to work on or salvage "rescue projects" with huge amounts of tech debt, I welcome that day. Still not there yet though I am starting to feel the vibes shifting.
This isn't anything new of course. Previously it was with projects built by looking for the cheapest bidder and letting them loose on an ill-defined problem. And you can just imagine what kind of code that produced. Except the scale is much larger.
My favorite example of this was a project that simply stopped working due to the amount of bugs generated from layers upon layers of bad code that was never addressed. That took around 2 years of work to undo. Roughly 6 months to un-break all the functionality and 6 more months to clean up the core and then start building on top.
Are you not worried that the sibling comment is right and the solution to this will be "more AI" in the future? So instead of hiring a team of human experts to cleanup, management might just dump more money into some specialized AI refactoring platform or hire a single AI coordinator... Or maybe they skip to rebuild using AI faster, because AI is good at greenfield. Then they only need a specialized migration AI to automate the regular switchovers.
I used to be unconcerned, but I admit to be a little frightened of the future now.
Well, in general worrying about the future is not useful. Regardless of what you think, it is always uncertain. I specifically stay away from taking part in such speculative threads here on HN.
What's interesting to me though is that very similar promises were being made about AI in the 80s. Then came the "AI Winter" after the hype cycle and promises got very far from reality. Generative AI is the current cycle and who knows, maybe it can fulfill all the promises and hype. Or maybe not.
There's a lot of irrationality currently and until that settles down, it is difficult to see what is real and useful and what is smoke and mirrors.
Don't worry. This will create the demand for even more powerful models that are able to untangle the mess created by previous models.
Once we realize the kind of mess _those_ models created, well, we'll need even more capable models.
It's a variation on the theme of Kernighan insight about the more "clever" you are while coding the harder it will be to debug.
EDIT: Simplicity is a way out but it's hard under normal circumstances, now with this kind of pressure to ship fast because the colleague with the AI chimp can outperform you, aiming at simplicity will require some widespread understanding
"That's the brilliant part: when the winter comes the apes freeze to death!"
> ... few tears ago
Brilliant. Even if it was a typo.
> unstated bet
(except where it's been stated, championed, enforced, and ultimated in no unequivocal terms by every executive in the tech industry)
I'm yet to encounter an AI-bull who admits the LLM tendency towards creating tech debt- outside of footnotes stating it can be fixed by better prompting (with no examples), or solved by whatever tool they are selling
The industry decided that decades ago. We may like to talk about quality and forethought, but when you actually go to work, you quickly discover it doesn't matter. Small companies tell you "we gotta go fast", large companies demand clear OKRs and focusing on actually delivering impact - either way, no one cares about tech debt, because they see it as unavoidable fact of life. Even more so now, as ZIRP went away and no one can afford to pay devs to polish the turd ad infinitum. The mantra is, ship it and do the next thing, clean up the old thing if it ever becomes a problem.
And guess what, I'm finally convinced they're right.
Consider: it's been that way for decades. We may tell ourselves good developers write quality code given the chance, but the truth is, the median programmer is a junior with <5 years of experience, and they cannot write quality code to save their life. That's purely the consequence of rapid growth of software industry itself. ~all production code in the past few decades was written by juniors, it continues to be so today; those who advance to senior level end up mostly tutoring new juniors instead of coding.
Or, all that put another way: tech debt is not wrong. It's a tool, a trade-off. It's perfectly fine to be loaded with it, if taking it lets you move forward and earn enough to afford paying installments when they're due. Like with housing: you're better off buying it with lump payment, or off savings in treasury bonds, but few have that money on hand and life is finite, so people just get a mortgage and move on.
--
Edited to add: There's a silver lining, though. LLMs make tech debt legible and quantifiable.
LLMs are affected by tech debt even more than human devs are, because (currently) they're dumber, they have less cognitive capability around abstractions and generalizations[0]. They make up for it by working much faster - which is a curse in terms of amplifying tech debt, but also a blessing, because you can literally see them slowing down.
Developer productivity is hard to measure in large part because the process is invisible (happens in people's heads and notes), and cause-and-effect chains play out over weeks or months. LLM agents compress that to hours to days, and the process itself is laid bare in the chat transcript, easy to inspect and analyze.
The way I see it, LLMs will finally allow us to turn software development at tactical level from art into an engineering process. Though it might be too late for it to be of any use to human devs.
--
[0] - At least the out-of-distribution ones - quirks unique to particular codebase and people behind it.
I ran into a new problem today: "reading atrophy".
As in if the LLM doesn't know about it, some devs are basically giving up and not even going to RTFM. I literally had to explain to someone today how something works by...reading through the docs and linking them the docs with screenshots and highlighted paragraphs of text.
Still got push back along the lines of "not sure if this will work". It's. Literally. In. The. Docs.
That's not really a new thing now, it just shows differently.
15 years ago I was working in an environment where they had lots of Indians as cheap labour - and the same thing will show up in any environment where you go for hiring a mass of cheap people while looking more at the cost than at qualifications: You pretty much need to trick them into reading stuff that are relevant.
I remember one case where one had a problem they couldn't solve, and couldn't give me enough info to help remotely. In the end I was sitting next to them, and made them read anything showing up on the screen out loud. Took a few tries where they were just closing dialog boxes without reading it, but eventually we had that under control enough that they were able to read the error messages to me, and then went "Oh, so _that's_ the problem?!"
Overall interacting with a LLM feels a lot like interacting with one of them back then, even down to the same excuses ("I didn't break anything in that commit, that test case was never passing") - and my expectation for what I can get out of it is pretty much the same as back then, and approach to interacting with it is pretty similar. It's pretty much an even cheaper unskilled developer, you just need to treat it as such. And you don't pair it up with other unskilled developers.
The mere existence of the phrase "RTFM" shows that this phenomenon was already a thing. LLMs are the worst thing to happen to people who couldn't read before. When HR type people ask what my "superpower" is I'm so tempted to say "I can read", because I honestly feel like it's the only difference between me and people who suck at working independently.
I've been thinking along these lines. LLMs seem to have arrived right when we were all getting addicted to reels/tic tocks/whatever. For some reason we love to swipe, swipe, swipe, until we get something funny/interesting/shocking, that gives us a short-lasting dopamine hit (or whatever chemicals it is) that feels good for about 1 second, and we want MORE, so we keep swiping.
Using an LLM is almost exactly the same. You get the occasional, "wow! I've never seen it do that before!" moments (whether that thing it just did was even useful or not), get a short hit of feel goods, and then we keep using it trying to get another hit. It keeps providing them at just the right intervals for people to keep them going just like they do with tick tock
It's exactly the argument here:
https://www.fast.ai/posts/2026-01-28-dark-flow/
My disillusionment comes from the feeling I am just cosplaying my job. There is nothing to distinguish one cosplayer from another. I am just doordashing software, at this point, and I'm not in control.
What kind of software are you writing? Are you just a "code monkey" implementing perfectly described Jira tickets (no offense meant)? I cannot imagine feeling this way with what I'm working on, writing code is just a small part of it, most of the time is spent trying to figure out how to integrate the various (undocumented and actively evolving) external services involved together in a coherent, maintainable and resilient way. LLMs absolutely cannot figure this out themselves, I have to figure it out myself and then write it all in its context, and even then it mostly comes up with sub-par, unmaintainable solutions if I wasn't being precise engouh.
They are amazing for side projects but not for serious code with real world impact where most of the context is in multiple people's head.
I don’t get this at all. I’m using LLM’s all day and I’m constantly having to make smart architectural choices that other less experienced devs won’t be making. Are you just prompting and going with whatever the initial output is, letting the LLM make decisions? Every moderately sized task should start with a plan, I can spend hours planning, going off and thinking, coming back to the plan and adding/changing things, etc. Sometimes it will be days before I tell the LLM to “go”. I’m also constantly optimising the context available to the LLM, and making more specific skills to improve results. It’s very clear to me that knowledge and effort is still crucial to good long term output… Not everyone will get the same results, in fact everyone is NOT getting the same results, you can see this by reading the wildly different feedback on HN. To some LLM’s are a force multiplier while others claim they can’t get a single piece of decent output…
I think the way you’re using these tools that makes you feel this way is a choice. You’re choosing to not be in control and do as little as possible.
Exactly.
Once you start using it intelligently, the results can be really satisfying and helpful. People complaining about 1000 lines of codes being generated? Ask it to generate functions one at a time and make small implementations. People complaining about having to run a linter? Ask it to automatically run it after each code execution. People complaining about losing track? Have it log every modifications in a file.
I think you get my point. You need to treat it as a super powerful tool that can do so many things that you have to guide it if you want to have a result that conforms to what you have in mind.
One challenge is, are those decisions making tangible differences?
We won't know until the code being produced especially greenfields hits any kind of maturity 5 years+ atleast?
It's not that challenging, the answer is, it depends.
It's like a junior dev writing features for a product everyday vs a principle engineer. The junior might be adding a feature with O(n^2) performance while principle has seen this before and writes it O(log n).
If the feature never reaches significance, the "better" solution doesn't matter, but it might!
The principle may write once and it is solid and never touched, but the junior might be good enough to never need coming back to, same with a llm and the right operator.
What? Of course it makes a difference when I direct it away from a bad solution towards a good solution. I know as soon as I review the output and it has done what I asked, or it hasn't and I make a correction. Why would I need to wait 5 years? That makes no sense, I can see the output.
If you're using LLM's and you don't know what good/bad output looks like then of course you're going to have problems, but such a person would have the same problems without the LLM...
I find the atrophy and zoning out or context switching problematic, because it takes a few seconds/ minutes in "thinking" and then BAM! I have 500 lines of all sorts of buggy and problematic code to review and get a sycophantic, not-enough-mature entity to correct.
At some point, I find myself needing to disconnect out of overwhelm and frustration. Faster responses isn't necessarily better. I want more observability in the development process so that I can be a party to it. I really have felt that I need to orchestrate multiple agents working in tandem, playing sort of a bad-cop, good-cop and a maybe a third trying to moderate that discussion and get a fourth to effectively incorporate a human in the mix. But that's too much to integrate in my day job.
I’ve actually found the tool that inspires the most worry about brain atrophy to be Copilot. Vscode is full of flashing suggestions all over. A couple days ago, I wanted to write a very quick program, and it was basically impossible to write any of it without Copilot suggesting a whole series of ways to do what it thought I was doing. And it seems that MS wants this: the obvious control to turn it off is actually just “snooze.”
I found the setting and turned it off for real. Good riddance. I’ll use the hotkey on occasion.
Yes! I spent more time trying to figure out how to turn off that garbage copilot suggesting then I did editing this 5 year old python program.
I use claude daily, no problems with it. But vscode + copilot suggestions was garbage!
"I wanted the code to look a certain way, but it kept pulling back to the way it wanted to do things."
I would argue this is ok for front-end. For back-end? very, very bad- if you can't get a usable output do it by hand.
"rip it out" is a phrase I've been saying more often to the robots.
I've gone years without coding and when I come back to it, it's like riding a bike! In each iteration of my coding career, I have become a better developer, even after a large gap. Now I can "code" during my gap. Were I ever to hand-code again, I'm sure my skills would be there. They don't atrophy, like your ability to ride a bike doesn't atrophy. Yes you may need to warm back up, but all the connections in your brain are still there.
You might still have the skillset to write code, but depending on length of the break your knowledge of tools, frameworks, patterns would be fairly outdated.
I used to know a person like that - high in the company structure who would claim he was a great engineer, but all the actual engineers would make jokes about him and his ancient skills during private conversations.
I’d push back on this framing a bit. There's a subtle ageism baked into the assumption that someone who stepped away from day-to-day coding has "ancient skills" worth mocking.
Yes, specific frameworks and tooling knowledge atrophy without use, and that’s true for anyone at any career stage. A developer who spent three years exclusively in React would be rusty on backend patterns too. But you’re conflating current tool familiarity with engineering ability, and those are different things.
The fundamentals: system design, debugging methodology, reading and reasoning about unfamiliar code, understanding tradeoffs ... those transfer. Someone with deep experience often ramps up on new stacks faster than you’d expect, precisely because they’ve seen the same patterns repackaged multiple times.
If the person you’re describing was genuinely overconfident about skills they hadn’t maintained, that’s a fair critique. But "the actual engineers making jokes about his ancient skills" sounds less like a measured assessment and more like the kind of dismissiveness that writes off experienced people before seeing what they can actually do.
Worth asking: were people laughing because he was genuinely incompetent, or because he didn’t know the hot framework of the moment? Because those are very different things.
This has nothing to do about ageism. This applies to any person of any age who has ego big enough to think that their knowledge of industry is relevant after they take prolonged break and be socially inept enough to brag about how they are still "in".
I don't disagree with your point about fundamentals, but in an industry where there seems to be new JS framework any time somebody sneezes - latest tools are very much relevant too. And of course the big thing is language changes. The events I'm describing happened in the late 00s-early 10s. When language updates picked up steam: Python, JS, PHP, C++. Somebody who used C++ 98 can't claim to have up to date knowledge in C++ in 2015.
So to answer your question - people were laughing at his ego, not the fact that he didn't know some hot new framework.
I beg to differ. I started with C in the 90s, then C# in '05, then PHP in '12, then Go in '21. The things I learned in C still apply to Go, C#, and PHP. And I even started contributing to open source C projects in '24 ... all my skills and knowledge were still relevant. This sounds exactly like ageism to me, but I clearly have a different perspective than you.
Yes, we clearly have different perspectives. I observed an arrogant person who despite their multi-year break from engineering of any kind strongly believed that they still were as capable as engineers who remained in the field during that time.
Maybe you had to be there.
Have you ever learnt a foreign language (say Mongolian, or Danish) and then never spoken it, nor even read anything in it for over 10 years? It is not like riding a bike, it doesn’t just come back like that. You have to actually relearn the language, practice it, and you will suck at it for months. Comprehension comes first (within weeks) but you will be speaking with grammatical errors, mispronunciations, etc. for much longer. You won‘t have to learn the language from scratch, second time around is much easier, but you will have to put in the effort. And if you use google translate instead of your brain, you won‘t relearn the language at all. You will simply forget it.
Anecdotally, i burned out pretty hard and basically didn't open a text editor for half a year (unemployed too). Eventually i got an itch to write code again and it didn't really feel like I was really worse. Maybe it wasn't long enough atrophy but code doesn't seem to quite work like language though ime.
Six months is definitely not long enough of a break for skills to degrade. But it's not just skills, as I wrote in another comment, the biggest thing is knowledge of new tools, new versions of language and its features.
I'd say there's at most around 2 years of knowledge runtime (maybe with all this AI stuff this is even shorter). After that period if you don't keep your knowledge up to date it fairly quickly becomes obsolete.
Honestly, this seems very much like the jump from being an individual contributor to being an engineering manager.
The time it happened for me was rather abrupt, with no training in between, and the feeling was eerily similar.
You know _exactly_ why the best solution is, you talk to your reports, but they have minds of their own, as well as egos, and they do things … their own way.
At some point I stopped obsessing with details and was just giving guidance and direction only in the cases where it really mattered, or when asked, but let people make their own mistakes.
Now LLMs don’t really learn on their own or anything, but the feeling of “letting go of small trivial things” is sorta similar. You concentrate on the bigger picture, and if it chose to do an iterative for loop instead of using a functional approach the way you like it … well the tests still pass, don’t they.
The only issue is that as an engineering manager you reasonably expect that the team learns new things, improve their skills, in general grow as engineers. With AI and its context handling you're working with a team where each member has severe brain damage that affects their ability to form long term memories. You can rewire their brain to a degree teaching them new "skills" or giving them new tools, but they still don't actually learn from their mistakes or their experiences.
Being optimistic (or pessimistic heh), if things keep the trend then the models will evolve as well and will probably be quite better in one year than they are now.
As a manager I would encourage them to use the LLM tools. I would also encourage unit tests, e2e testing, testing coverages, CI pipelines automating the testing, automatic pr reviewing etc...
It's also peeking at the big/impactful changes and ignoring the small ones.
Your job isn't to make sure they don't have "brain damage" its to keep them productive and not shipping mistakes.
> I worry about the "brain atrophy" part, as I've felt this too. And not just atrophy, but even moreso I think it's evolving into "complacency".
Not trusting the ML's output is step one here, that keeps you intellectually involved - but it's still a far cry from solving the majority of problems yourself (instead you only solve problems ML did a poor job at).
Step two: I delineate interesting and uninteresting work, and Claude becomes a pair programmer without keyboard access for the latter - I bounce ideas off of it etc. making it an intelligent rubber duck. [Edit to clarify, a caveat is that] I do not bore myself with trivialities such as retrieving a customer from the DB in a REST call (but again, I do verify the output).
> I do not bore myself with trivialities such as retrieving a customer from the DB in a REST call
Genuine question, why isn't your ORM doing that? I see a lot of use cases for LLMs that seem to be more expensive ways to do snippets and frameworks...
My advice: keep it on a tight leash.
In the happy case where I have a good idea of the changes necessary, I will ask it to do small things, step by step, and examine what it does and commit.
In the unhappy case where one is faced with a massive codebase and no idea where to start, I find asking it to just “do the thing” generates slop, but enough for me to use as inspiration for the above.
My experience is the opposite - I haven't used my brain more in a while.. Typing characters was never what developers were valued for anyway. The joy of building is back too.
100% same, I had brain fog before the llms, I got tired of reading new docs over and over again for new languages. I became a manager and lost it all.
Now back to IC with 25+ years of experience + LLM = god mode, and its fun again.
Same. I feel I need to be way more into the domain and what the user is trying to do than ever before.
> Like if I had stated certain design goals recently it would adhere to them, but after a few iterations it would forget again and go back to its original approach, or mix the two, or whatever.
Context management, proper prompting and clear instructions, proper documentation are still relevant.
I feel like I'm still a couple steps behind in skill level as my lead and is trying to gain more experience I do wonder if I am shooting myself in the foot if I rely too much on AI at this stage. The senior engineer I'm trying to learn from can very effectively use ai because he has very good judgement of code quality, I feel like if I use AI too much I might lose out on chance to improve my judgement. It's a hard dilemma.
it's not about brain atrophy, it's skill atrophy
is that not the sam thing?
He didn't say "brain atrophy", he was talking about coding abilities.
> I want to say it's very akin to doom scrolling. Doom tabbing? It's like, yeah I could be more creative with just a tad more effort, but the AI is already running and the bar to seeing what the AI will do next is so low, so....
Yea exactly, Like we are just waiting so that it gets completed and after it gets completed then what? We ask it to do new things again.
Just as how if we are doom scrolling, we watch something for a minute then scroll down and watch something new again.
The whole notion of progress feels completely fake with this. Somehow I guess I was in a bubble of time where I had always end up using AI in web browsers (just as when chatgpt 3 came) and my workflow didn't change because it was free but recently changed it when some new free services dropped.
"Doom-tabbing" or complete out of the loop AI agentic programming just feels really weird to me sucking the joy & I wouldn't even consider myself a guy particular interested in writing code as I had been using AI to write code for a long time.
I think the problem for me was that I always considered myself a computer tinker before coder. So when AI came for coding, my tinkering skills were given a boost (I could make projects of curiosity I couldn't earlier) but now with AI agents in this autonomous esque way, it has come for my tinkering & I do feel replaced or just feel like my ability of tinkering and my interests and my knowledge and my experience is just not taken up into account if AI agent will write the whole code in multi file structure, run commands and then deploy it straight to a website.
I mean my point is tinkering was an active hobby, now its becoming a passive hobby, doom-tinkering? I feel like I have caught up on the feeling a bit earlier with just vibe from my heart but is it just me who feels this or?
What could be a name for what I feel?
[dead]
LLMs have some terrible patterns, don't know what do ? Just chuck a class named Service in.
Have to really look out for the crap.
The tenacity part is definitely true. I told it to keep trying when it kept getting stuck trying to spin up an Amazon Fargate service. I could feel its pain, and wanted to help, but I wanted to see whether the LLM could free itself from the thorny and treacherous AWS documentation forest. After a few dozen attempts and probably 50 KWh of energy it finally got it working, I was impressed. I could have done it faster myself, but the tradeoff would have been much higher blood pressure. Instead I relaxed and watched youtube while the LLM did its work.
Why am I not surprised that a blog was written about LLM coding going from 20% to 80% useful, yet all of the HN comments are still nit picking about some negative details rather than building positive ideas toward some progress...
Is the programmer ego really this fragile? At least luddites had an ideological reasoning, whereas here we just seem to have emotional reflexes.
It's because we see a bunch of people completely ignoring the missing 20% and flooding the world with complete slop. The push back is required to keep us sane, we need people reminding others that it's not at 100% yet even if it sometimes feels like it.
Then you have Anthropic that states on his own blog that engineers fully delegate to claude code only from 0 to 20% https://www.anthropic.com/research/how-ai-is-transforming-wo...
The fact that people keep pushing figures like 80% is total bs to me
I retired from paid sw dev work in 2020 when COVID arrived. I’ve worked on my small projects since with all development by hand. I’d followed the rise of AI, but not used it. Late last year I started a project that included reverse engineering some firmware that runs on an Intel 8096 based embedded processor. I’d never worked on that processor before. There are tools available, but they cost many $. So, I started to think about a simple disassembler. 2 weeks ago we decided to try Claude to see what it could do. We now have a disassembler, assembler and a partially working emulator. No doubt there are bugs and missing features and the code is a bit messy, but boy has it sped up the work. One thing did occur to me. Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
> Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
Definitely. Making small, single-purpose utilities with LLMs is almost as easy these days as googling for them on-line - much easier, in fact, if you account for time spent filtering out all the malware, adware, "to finish the process, register an account" and plain broken "tools" that dominate SERP.
Case in point, last time my wife needed to generate a few QR codes for some printouts for an NGO event, I just had LLM make one as a static, single-page client-side tool and hosted it myself -- because that was the fastest way to guarantee it's fast, reliable, free of surveillance economy bullshit, and doesn't employ URL shorteners (surprisingly common pattern that sometimes becomes a nasty problem down the line; see e.g. a high-profile case of some QR codes on food products leading to porn sites after shortlink got recycled).
Whatever happened to just typing "apt install qrencode"? It's definitely "fast, reliable, free of surveillance economy bullshit, and doesn't employ URL shorteners".
> LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
I’ve always said I’m a builder even though I’ve also enjoyed programming (but for an outcome, never for the sake of the code)
This perfectly sums up what I’ve been observing between people like me (builders) who are ecstatic about this new world and programmers who talk about the craft of programming, sometimes butting heads.
One viewpoint isn’t necessarily more valid, just a difference of wiring.
I noticed the same thing, but wasn't able to put it into words before reading that. Been experimenting with LLM-based coding just so I can understand it and talk intelligently about it (instead of just being that grouchy curmudgeon), and the thought in the back of my mind while using Claude Code is always:
"I got into programming because I like programming, not whatever this is..."
Yes, I'm building stupid things faster, but I didn't get into programming because I wanted to build tons of things. I got into it for the thrill of defining a problem in terms of data structures and instructions a computer could understand, entering those instructions into the computer, and then watching victoriously while those instructions were executed.
If I was intellectually excited about telling something to do this for me, I'd have gotten into management.
Same. This kind of coding feels like it got rid of the building aspect of programming that always felt nice, and it replaced it entirely with business logic concerns, product requirements, code reviews, etc. All the stuff I can generally take or leave. It's like I'm always in a meeting.
>If I was intellectually excited about telling something to do this for me, I'd have gotten into management.
Exactly this. This is the simplest and tersest way of explaining it yet.
Maybe I don't entirely get it, but what is stopping you to just continue coding?
Because you are not coding, you are building. I've been coding since I was 7 years old, now I'm building.
I'd go one step higher, we're not builders, we're problem solvers.
Sometimes the problem needs building, sometimes not.
I'm an Engineer, I see a problem and want to solve it. I don't care if I have to write code, have a llm build something new, or maybe even destroy something. I want to solve the problem for the business and move to the next one, most of the time it is having a llm write code though.
Same same. Writing the actual code is always a huge motivator behind my side projects. Yes, producing the outcome is important, but the journey taken to get there is a lot of fun for me.
I used Claude Code to implement a OpenAI 4o-vision powered receipt scanning feature in an expense tracking tool I wrote by hand four years ago. It did it in two or three shots while taking my codebase into account.
It was very neat, and it works great [^0], but I can't latch onto the idea of writing code this way. Powering through bugs while implementing a new library or learning how to optimize my test suite in a new language is thrilling.
Unfortunately (for me), it's not hard at all to see how the "builders" that see code as a means to an end would LOVE this, and businesses want builders, not crafters.
In effect, knowing the fundamentals is getting devalued at a rate I've never seen before.
[^0] Before I used Claude to implement this feature, my workflow for processing receipts looked like this: Tap iOS Shortcut, enter the amount, snap a pic of the receipt, type up the merchant, amount and description for the expense, then have the shortcut POST that to my expenses tracking toolkit which, then, POSTs that into a Google Sheet. This feature amounted the need for me to enter the merchant and amount. Unfortunately, it often took more time to confirm that the merchant, amount and date details OpenAI provided were correct (and correct it when details were wrong, which was most of the the time) than it did to type out those details manually, so I just went back to my manual workflow. However, the temptation to just glance at the details and tap "This looks correct" was extremely high, even if the info it generated was completely wrong! It's the perfect analogue to what I've been witnessing throughout the rise of the LLMs.
What I have enjoyed about programming is being able to get the computer to do exactly what I want. The possibilities are bounded by only what I can conceive in my mind. I feel like with AI that can happen faster.
> get the computer to do exactly what I want.
> with AI that can happen faster.
well, not exactly that.
For simple things it can. But then for more complex things that's where I step it
Have you an example of getting a coding chatbot to do exactly what you want?
https://gisthost.github.io/?a41ce6304367e2ced59cd237c576b817... - which built https://github.com/datasette/datasette-transactions exactly the way I wanted it to be built
The problem with replying to the proof-demanders is that they'll always pick it apart and find some reason it doesn't fit their definition. You must be familiar with that at this point.
Worse, they might even attempt to verify your claims e.g. "When AI 'builds a browser,' check the repo before believing the hype" https://www.theregister.com/2026/01/26/cursor_opinion/
> exactly the way I wanted it to be built
You verified each line?
What? Why would you want to?
See this is a perfect example of OPs statement! I don't care about the lines, I care about the output! It was never about the lines of code.
Your comment makes it very clear there are different viewpoints here. We care about problem->solution. You care about the actual code more than the solution.
> I don't care about the lines, I care about the output! It was never about the lines of code.
> Your comment makes it very clear there are different viewpoints here.
Agreed.
I care that code output not include leaked secrets, malware installation, stealth cryptomining etc.
Some others don't.
Is this a joke? Are you genuinely implying that no one has ever got an LLM to write code that does exactly what they want?
No. Mashing up other peoples' code scraped from the web is not what I'd call writing code.
Can you not see how you truly, deep down, are afraid you might be wrong?
It's clouding your vision.
This gets at the heart of the quality of results issues a lot of people are talking about elsewhere here. Right now, if you treat them as a system where you can tell it what you want and it will do it for you, you're building a sandcastle. Instead of that, also describe the correct data structures and appropriate algorithms to use against them, as well as the particulars of how you want the problem solved, it's a different situation altogether. Like most systems, the quality of output is in some way determined by the quality of input.
There is a strange insistence on not helping the LLM arrive at the best outcome in the subtext to this question a lot of times. I feel like we are living through the John Henry legend in real time
> I got into it for the thrill of defining a problem in terms of data structures and instructions a computer could understand, entering those instructions into the computer, and then watching victoriously while those instructions were executed.
You can still do that with Claude Code. In fact, Claude Code works best the more granular your instructions get.
> Claude Code works best the more granular your instructions get.
So best feed it machine code?
Funny you say that. Because I have never enjoyed management as much as being hands on and directly solving problems.
So maybe our common ground is that we are direct problem solvers. :-)
For some reason this makes me think of a jigsaw puzzle. People usually complete these puzzles because they enjoy the process where on the end you get a picture that you can frame if you want to. Some people seem to want to get the resulting picture. No interest in process at all.
I guess that's the same people who went to all those coding camps during their hay day because they heard about software engineering salaries. They just want the money.
When I last bought a Lego Technic set because I wanted to play with making mechanisms with gears and stuff, I assembled it according to the instructions, which was fun, and then the final result was also cool and I couldn't bear to dismantle it.
IMO, this isn't entirely a "new world" either, it is just a new domain where the conversation amplifies the opinions even more (weird how that is happening in a lot of places)
What I mean by that: you had compiled vs interpreted languages, you had types vs untyped, testing strategies, all that, at least in some part, was a conversation about the tradeoffs between moving fast/shipping and maintainability.
But it isn't just tech, it is also in methodologies and the words use, from "build fast and break things" and "yagni" to "design patterns" and "abstractions"
As you say, it is a different viewpoint... but my biggest concern with where are as industry is that these are not just "equally valid" viewpoints of how to build software... it is quite literally different stages of software, that, AFAICT, pretty much all successful software has to go through.
Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
This perspective is crucial. Scale is the great equalizer / demoralizer, scale of the org and scale of the systems. Systems become complex quickly, and verifiability of correctness and function becomes harder. Companies that built from day with AI and have AI influencing them as they scale, where does complexity begin to run up against the limitations of AI and cause regression? Or if all goes well, amplification?
makes sense if you are a data scientist where people need to be boxed into tidy little categories. but some people probably fall into both categories.
But how can you be a responsible builder if you don't have trust in the LLMs doing the "right thing"? Suppose you're the head of a software team where you've picked up the best candidates for a given project, in that scenario I can see how one is able to trust the team members to orchestrate the implementation of your ideas and intentions, with you not being intimately familiar with the details. Can we place the same trust in LLM agents? I'm not sure. Even if one could somehow prove that LLM are very reliable, the fact an AI agents aren't accountable beings renders the whole situation vastly different than the human equivalent.
Trust but verify:
I test all of the code I produce via LLMs, usually doing fairly tight cycles. I also review the unit test coverage manually, so that I have a decent sense that it really is testing things - the goal is less perfect unit tests and more just quickly catching regressions. If I have a lot of complex workflows that need testing, I'll have it write unit tests and spell out the specific edge cases I'm worried about, or setup cheat codes I can invoke to test those workflows out in the UI/CLI.
Trust comes from using them often - you get a feeling for what a model is good and bad at, and what LLMs in general are good and bad at. Most of them are a bit of a mess when it comes to UI design, for instance, but they can throw together a perfectly serviceable "About This" HTML page. Any long-form text they write (such as that About page) is probably trash, but that's super-easy to edit manually. You can often just edit down what they write: they're actually decent writers, just very verbose and unfocused.
I find it similar to management: you have to learn how each employee works. Unless you're in the Top 1%, you can't rely on every employee giving 110% and always producing perfect PRs. Bugs happen, and even NASA-strictness doesn't bring that down to zero.
And just like management, some models are going to be the wrong employee for you because they think your style guide is stupid and keep writing code how they think it should be written.
You don't simply put a body in a seat and get software. There are entire systems enabling this trust: college, resume, samples, referral, interviews, tests and CI, monitoring, mentoring, and performance feedback.
And accountability can still exist? Is the engineer that created or reviewed a Pull Request using Claude Code less accountable then one that used PICO?
> And accountability can still exist? Is the engineer that created or reviewed a Pull Request using Claude Code less accountable then one that used PICO?
The point is that in the human scenario, you can hold the human agents accountable. You cannot do that with AI. Of course, you as the orchestrator of agents will be accountable to someone, but you won't have the benefit of holding your "subordinates" accountable, which is what you do in a human team. IMO, this renders the whole situation vastly different (whether good or bad I'm not sure).
You can switch to another LLM provider or stop using them altogether. It's even easier than firing a developer.
It is as easy as getting rid of Microsoft Teams at your org.
Of course he is - because he invested so much less.
I remember leaving university going into my first engineering job, thinking "Where is all the engineering? All the problem solving and building complex system? All the math and science? Have I been demoted to a lowly programmer?"
Took me a few years to realize that this wasn't a universal feeling, and that many others found the programming tasks more fulfilling than any challenging engineering. I suppose this is merely another manifestation of the same phenomena.
There's more to it than just coding Vs building though.
For a long time in my career now I've been in a situation where I'd be able to build more if I was willing to abstract myself and become a slide-merchant/coalition-builder. I don't want to do this though.
Yet, I'm still quite an enthusiastic vibe-coder.
I think it's less about coding Vs building and more about tolerance for abstraction and politics. And I don't think there are that many people who are so intolerant of abstraction that they won't let agents write a bunch of code for them.
> > LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
This is much less significant than the fact LLMs split engineers on those who primarily like quality v. those who primarily like speed.
Maybe there's an intermediate category: people who like designing software? I personally find system design more engaging than coding (even though I enjoy coding as well). That's different from just producing an opaque artifact that seems to solve my problem.
I think he's really getting at something there. I've been thinking about this a lot (in the context of trying to understand the persistent-on-HN skepticism about LLMs), and the framing I came up with[1] is top-down vs. bottom-up dev styles, aka architecting code and then filling in implementations, vs. writing code and having architecture evolve.
[1] https://www.klio.org/theory-of-llm-dev-skepticism/
I like this framing. Nice typography btw, a pleasure to read.
I think there's a place for both.
We have services deployed globally serving millions of customers where rigor is really important.
And we have internal users who're building browser extensions with AI that provide valuable information about the interface they're looking at including links to the internal record management, and key metadata that's affecting content placement.
These tools could be handed out on Zip drives in the street and it would just show our users some of the metadata already being served up to them, but it's amazing to strip out 75% of the process of certain things and just have our user (in this case though, it's one user who is driving all of this, so it does take some technical inclination) build out these tools that save our editors so much time when doing this before would have been months and months and months of discovery and coordination and designs that probably wouldn't actually be as useful in the end after the wants of the user are diluted through 18 layers of process.
To me this is similar to car enthusiasms. Some people absolutely love to build their project car, it's a major part of the hobby for them. Others just love the experience of driving, so they buy ready cars or just pay someone to work on the car.
Alternatively, others just want to get to their destination.
I think the division is more likely tied to writing. You have to fundamentally change how you do your job, from one of writing a formal language for a compiler to one of writing natural language for a junior-goldfish-memory-allstar-developer, closer to management then to contributor.
This distinction to me separates the two primary camps
The new LLM centered workflow is really just a management job now.
Managers and project managers are valuable roles and have important skill sets. But there's really very little connection with the role of software development that used to exist.
It's a bit odd to me to include both of these roles under a single label of "builders", as they have so little in common.
EDIT: this goes into more detail about how coding (and soon other kinds of knowledge work) is just a management task now: https://www.oneusefulthing.org/p/management-as-ai-superpower...
i don't disagree. at some point LLM's might become good enough that we wouldn't need exact technical expertise.
I enjoy both and have ended up using AI a lot differently than vibe coders. I rarely use it for generating implementations, but I use it extensively for helping me understand docs/apis and more importantly, for debugging. AI saves me so much time trying to figure out why things aren’t working and in code review.
I deliberately avoid full vibe coding since I think doing so will rust my skills as a programmer. It also really doesn’t save much time in my experience. Once I have a design in mind, implementation is not the hard part.
So far I haven't seen it actually be effective at "building" in a work context with any complexity, and this despite some on our team desperately trying to make that the case.
I have! You have to be realistic about the projects. The more irreducible local context it needs, the less useful it will be. Great for greenfield code, oneshots, write once read once run for months.
Agreed. I don’t care for engineering or coding, and would gladly give it up the moment I can. I’m also running a one man business where every hour counts (and where I’m responsible for maintaining every feature).
The fact of the matter is LLMs produce lower quality at higher volumes in more time than it would take to write it myself, and I’m a very mediocre engineer.
I find this seperation of “coding” vs “building” so offensive. It’s basically just saying some people are only concerned with “inputs”, while others with “outputs”. This kind of rhetoric is so toxic.
It’s like saying LLM art is separating people into people who like to scribble, and people who like to make art.
I like building, but I don't fool myself into thinking it can be done by taking shortcuts. You could build something that looks like a house for half the cost but it won't be structurally sound. That's why I care about the details. Someone has to.
> I enjoy both and have ended up using AI a lot differently than vibe coders. I rarely use it for generating implementations, but I use it extensively for helping me understand docs/apis and more importantly, for debugging. AI saves me so much time trying to figure out why things aren’t working and in code review.
I had felt like this and still do but man, at some point, I feel like the management churn feels real & I just feel suffering from a new problem.
Suppose, I actually end up having services literally deployed from a single prompt nothing else. Earlier I used to have AI write code but I was interested in the deployment and everything around it, now there are services which do that really neatly for you (I also really didn't give into the agent hype and mostly used browsers LLM)
Like on one hand you feel more free to build projects but the whole joy of project completely got reduced.
I mean, I guess I am one of the junior dev's so to me AI writing code on topics I didn't know/prototyping felt awesome.
I mean I was still involved in say copy pasting or looking at the code it generates. Seeing the errors and sometimes trying things out myself. If AI is doing all that too, idk
For some reason, recently I have been disinterested in AI. I have used it quite a lot for prototyping but I feel like this complete out of the loop programming just very off to me with recent services.
I also feel like there is this sense of if I buy for some AI thing, to maximally extract "value" out of it.
I guess the issue could be that I can have vague terms or have a very small text file as input (like just do X alternative in Y lang) and I am now unable to understand the architectural decisions and the overwhelmed-ness out of it.
Probably gonna take either spec-driven development where I clearly define the architecture or development where I saw something primagen do recently which is that the AI will only manipulate code of that particular function, (I am imagining it for a file as well) and somehow I feel like its something that I could enjoy more because right now it feels like I don't know what I have built at times.
When I prototype with single file projects using say browser for funsies/any idea. I get some idea of what the code kind of uses with its dependencies and functions names from start/end even if I didn't look at the middle
A bit of ramble I guess but the thing which kind of is making me feel this is that I was talking to somebody and shwocasing them some service where AI + server is there and they asked for something in a prompt and I wrote it. Then I let it do its job but I was also thinking how I would architect it (it was some detect food and then find BMR, and I was thinking first to use any api but then I thought that meh it might be hard, why not use AI vision models, okay what's the best, gemini seems good/cheap)
and I went to the coding thing to see what it did and it actually went even beyond by using the free tier of gemini (which I guess didn't end up working could be some rate limit of my own key but honestly it would've been the thing I would've tried too)
So like, I used to pride myself on the architectural decisions I make even if AI could write code faster but now that is taken away as well.
I really don't want to read AI code so much so honestly at this point, I might as well write code myself and learn hands on but I have a problem with build fast in public like attitude that I have & just not finding it fun.
I feel like I should do a more active job in my projects & I am really just figuring out what's the perfect way to use AI in such contexts & when to use how much.
Thoughts?
I would agree that OAIs GPT-5 family of models is a phase change over GPT-4.
In the ChatGPT product this is not immediately obvious and many people would strongly argue their preference for 4. However, once you introduce several complex tools and make tool calling mandatory, the difference becomes stark.
I've got an agent loop that will fail nearly every time on GPT-4. It works sometimes, but definitely not enough to go to production. GPT-5 with reasoning set to minimal works 100% of the time. $200 worth of tokens and it still hasn't failed to select the proper sequence of tools. It sometimes gets the arguments to the tools incorrect, but it's always holding the right ones now.
I was very skeptical based upon prior experience but flipping between the models makes it clear there has been recent stepwise progress.
I'll probably be $500 deep in tokens before the end of the month. I could barely go $20 before I called bullshit on this stuff last time.
Pretty sure there wasn't extensive training on tooling beforehand. I mean, god, during GPT-3 even getting a reliable json output was a battle and there were dedicated packages for json inference.
> You realize that stamina is a core bottleneck to work
There has been a lot of research that shows that grit is far more correlated to success than intelligence. This is an interesting way to show something similar.
AIs have endless grit (or at least as endless as your budget). They may outperform us simply because they don't ever get tired and give up.
Full quote for context:
Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.
>They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day.
"Listen, and understand! That Terminator is out there! It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse, or fear. And it absolutely will not stop... ever, until you are dead!"
I realized a long time ago that I’m better at computer stuff not because I’m smarter but because I will sit there all day and night to figure something out while others will give up. I always thought that was my superpower in the job industry but now I’m not so sure if it will transfer to getting AI to do what I need done…
Same, I barely made it through Engineering school, but would stay up all night figuring out everything a computer could do (before the internet).
I did it because I enjoyed it, and still do. I just do it with LLMs now. There is more to figure out than ever before and things get created faster than I have time to understand them.
LLM should be enabling this, not making it more depressing.
If you ever work with LLMs you know that they quite frequently give up.
Sometimes it's a
or a"this feature would require extensive logic and changes to the existing codebase".
Sometimes they just declare their work done. Ignoring failing tests and builds.
You can nudge them to keep going but I often feel like, when they behave like this, they are at their limit of what they can achieve.
Nope, not for me, unless I tell it to.
Context matters, for an LLM just like a person. When I wrote code I'd add TODOs because we cannot context switch to another problem we see every time.
But you can keep the agent fixated on the task AND have it create these TODOs, but ultimately it is your responsibility to find them and fix them (with another agent).
If I tell it to implement something it will sometimes declare their work done before it's done. But if I give Claude Code a verifiable goal like making the unit tests pass it will work tirelessly until that goal is achieved. I don't always like the solution, but the tenacity everyone is talking about is there
> but the tenacity everyone is talking about is there
I always double-check if it doesn't simply exclude the failing test.
The last time I had this, I discovered it later in the process. When I pointed this out to the LLM, it responded, that it acknowledged thefact of ignoring the test in CLAUDE.md, and this is justified because [...]. In other words, "known issue, fuck off"
tenacity == while loop
> If you ever work with LLMs you know that they quite frequently give up.
If you try to single shot something perhaps. But with multiple shots, or an agent swarm where one agent tells another to try again, it'll keep going until it has a working solution.
Yeah exactly this is a scope problem, actual input/output size is always limited> I am 100% sure CC etc are using multiple LLM calls for each response, even though from the response streaming it looks like just one.
Using LLMs to clean those up is part of the workflow that you're responsible for (... for now). If you're hoping to get ideal results in a single inference, forget it.
The tenacity aspect makes me worried about the paper clip AI misalignment scenario more than before.
> AIs have endless grit (or at least as endless as your budget).
That is the only thing he doesn't address: the money it costs to run the AI. If you let the agents loose, they easily burn north of 100M tokens per hour. Now at $25/1M tokens that gets quickly expensive. At some point, when we are all drug^W AI dependent, the VCs will start to cash in on their investments.
LLMs do not have grit or tenacity. Tenacity doesn't desribe a machine that doesn't need sleep or experience tiredness, or stress. Grit doesn't describe a chatbot that will tirelessly spew out answers and code because it has no stake or interest in the result, never perceives that it doesn't know something, and never reflects on its shortcomings.
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
I was thinking about this the other day as relates to the DevOps movement.
The DevOps movement started as a way to accelerate and improve the results of dev<->ops team dynamics. By changing practices and methods, you get acceleration and improvement. That creates "high-performing teams", which is the team form of a 10x engineer. Whether or not you believe in '10x engineers', a high-performing team is real. You really can make your team deploy faster, with fewer bugs. You have to change how you all work to accomplish it, though.
To get good at using AI for coding, you have to do the same thing: continuous improvement, changing workflows, different designs, development of trust through automation and validation. Just like DevOps, this requires learning brand new concepts, and changing how a whole team works. This didn't get adopted widely with DevOps because nobody wanted to learn new things or change how they work. So it's possible people won't adapt to the "better" way of using AI for coding, even if it would produce a 10x result.
If we want this new way of working to stick, it's going to require education, and a change of engineering culture.
This is an interesting thing that I'm contemplating. I also do believe that (perhaps with very few exceptions) there are no "10x engineers" by themselves, but engineers that thrive 10x more in a context or another (like, I'm sure Jeff Dean is an absolutely awesome engineer - but if you took him out of Google and plugged him into IBM - would he have had the same impact?)
With that in mind - I think one very unexplored area is "how to make the mixed AI-human teams successful". Like, I'm fairly convinced AI changes things, but to get to the industrialization of our craft (which is what management seems to want - and, TBH, something that makes sense from an economic pov), I feel that some big changes need to happen, and nobody is talking about that too much. What are the changes that need to happen? How do we change things, if we are to attempt such industrialization?
I'm pretty happy with Copilot in VS Code. Type what change I want Claude to make in the Copilot panel, and then use the VS Code in context diffs to accept or reject the proposed changes. While being able to make other small changes on my own.
So I think this tracks with Karpathy's defense of IDEs still being necessary ?
Has anyone found it practical to forgo IDEs almost entirely?
I've found copilot chat is able to do everything I need. I tried the Claude plugin for vscode and it was a noticeably worse experience for me.
Mind you copilot has only supported agent mode relatively recently.
I really like the way copilot does changes in such a way you can accept or reject and even revert to point in time in the chat history without using git. Something about this just fits right with how my brain works. Using Claude plugin just felt like I had one hand tied behind my back.
I find Claude Code in VS Code is sometimes horribly inefficient. I tell it to replace some print-statements with proper logging in the one file I have open and it first starts burning tokens to understand the codebase for the 13th time today, despite not needing to and having it laid out in the CLAUDE.md already.
I have been assigning issues to copilot in Github. It will then create a pull request and work on and report back on the issue in the PR. I will pull the code and make small changes locally using VSCode when needed.
But what I like about this setup is that I have almost all the context I need to review the work in a single PR. And I can go back and revisit the PR if I ever run into issues down the line. Plus you can run sessions in parallel if needed, although I don't do that too much.
Are you letting it run your tests and run little snippets of code to try them out (like "python -c 'import module; print(module.something())'") or are you just using it to propose diffs for you to accept or reject?
This stuff gets a whole lot more interesting when you let it start making changes and testing them by itself.
Coplilot is not on par with cc or cursor even
I use it to access Claude. So what's the difference?
This stuff is a little messy and opaque, but the performance of the same model in different harnesses depends a lot on how context is managed. The last time I tried Copilot, it performed markedly worse for similar tasks compared to Claude Code. I suspect that Copilot was being very aggressive in compressing context to save on token cost, but I'm not 100% certain about this.
Also note that with Claude models, Copilot might allocate a different number of thinking tokens compared to Claude Code.
Things may have changed now compared to when I tried it out, these tools are in constant flux. In general I've found that harnesses created by the model providers (OpenAI/Codex CLI, Anthropic/Claude Code, Google/Gemini CLI) tend to be better than generalist harnesses (cheaper too, since you're not paying a middleman).
Different harnesses and agentic environments produce different results from the same model. Claude Code and Cursor are the best IME and Copilot is by far the worst.
Why not? You can select Opus 4.5, Gemini 3 Pro, and others.
Claude Code is a CLI tool which means it can do complete projects in a single command. Also has fantastic tools for scaffolding and harnessing the code. You can define everything from your coding style to specific instructions for designing frontpages, integrating payments, etc.
It's not about the model. It's about the harness
Claude Code is a CLI tool which means it can do complete projects in a single command
https://github.com/features/copilot/cli/
This would make some sense if VS Code didn't have a terminal built into it. The LLMs have the same bash capabilities in either form.
Huh? There is nothing stopping copilot from doing an entire project in one go.
Ive done it 10s of times.
it's not a model limit anymore, it's tools , skills, background agents, etc. It's an entire agentic environment.
Github copilot has support for this stuff as well. Agent skills, background/subagents, etc.
I used CC in year age and it was not good. But one month ago I paid for max and started to rebuild my company web shop using it.
It is like plowing land with hand one year age and now is like I'm in brend new John Deere. It's amazing.
Of course its not perfect but if you understand code and problem it needs to solve then it works really good.
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
This is true... Equally I've seen it dive into a rabbit hole, make some changes that probably aren't the right direction... and then keep digging.
This is way more likely with Sonnet, Opus seems to be better at avoiding it. Sonnet would happily modify every file in the codebase trying to get a type error to go away. If I prompt "wait, are you off track?" it can usually course correct. Again, Opus seems way better at that part too.
Admittedly this has improved a lot lately overall.
I don't understand why anyone finds it interesting that a machine, or chatbot, never tires or gets demoralized. You have to anthromorphize the LLM before you can even think of those possibilities. A tractor never tires or gets demoralized either, because it can't. Chatbots don't "dive into a rabbit hole ... and then keep digging" because they have superhuman tenacity, they do it because that's what software does. If I ask my laptop to compute the millionth Fibonacci number it doesn't sigh and complain, and I don't think it shows any special qualities unless I compare it to a person given the same job.
You're a machine. You're literally a wet, analog device converting some forms of energy into other forms just like any other machine as you work, rest, type out HN comments, etc. There is nothing special about the carbon atoms in your body -- there's no metadata attached to them marking them out as belonging to a Living Person. Other living-person-machines treat "you" differently than other clusters of atoms only because evolution has taught us that doing so is a mutually beneficial social convention.
So, since you're just a machine, any text you generate should be uninteresting to me -- correct?
Alternatively, could it be that a sufficiently complex and intricate machine can be interesting to observe in its own right?
If humans are machines, they are still a subset of machines and they (among other animals) are the only ones who can be demotivated and so it is still a mistake to assume an entirely different kind of machine would have those properties.
>Other living-person-machines treat "you" differently than other clusters of atoms only because evolution has taught us that doing so is a mutually beneficial social convention
Evolution doesn't "teach" anything. It's just an emergent property of the fact that life reproduces (and sometimes doesn't). If you're going to have this radically reductionist view of humanity, you can't also treat evolution as having any kind of agency.
maybe its just me doing stuff that's out the usual loop
even dealing with api's that have MCP servers the so called agents make a mess of everything.
my stuff is just regular data stuff - ingest data from x - transform it | make it real time - then pipe it to y
LLM coding splits up engineers based on those who primarily like building and those who primarily like code reviews and quality assessment. I definitely don’t love the latter (especially when reviewing decisions not made by a human with whom I can build long-term personal rapport).
After certain experience threshold of making things from scratch, “coding” (never particularly liked that term) has always been 99% building, or architecture, and I struggle to see how often a well-architected solution today, with modern high-level abstractions, requires so much code that you’d save significant time and effort by not having to just type, possibly with basic deterministic autocomplete, exactly what you mean (especially considering you would have to also spend time and effort reviewing whatever was typed for you if you used a non-deterministic autocomplete).
See, I don't take it that extreme: LLMs make fantastic, never-before seen quality autocompletes. I hacked together a Neovim plugin that prompts an LLM to "finish this function" on command, and it's a big time save for the menial plumbing type operations. Think things like "this api I use expects JSON that encodes some subset of SQL, I want all the dogs with Ls in their name that were born on a Tuesday". Given an example of such API (or if the documentation ended up in its training), LLMs will consistently one-shot stuff like that.
Asking it to do entire projects? Dumb. You end up with spaghetti, unless you hand-hold it to a point that you might as well be using my autocomplete method.
Depends on the scope of the project. If it's small, and you direct it correctly, it can one-shot yes. Or 2-3-shot.
Agree with Karpathy's take. Finally a down to Earth analysis from a respected source in the AI space. I guess I'll be using slopocalypse a lot more now :)
> I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media
It has arrived. Github will be most affected thanks to git-terrorists at Apna College refusing to take down that stupid tutorial. IYKYK.
The respect is unwarranted.
He ran Teslas ML division, but still doesnt know what a simple kalman filter is (in the sense where he claimed that lidar would be hard to integrate with cameras).
The Kalman filter examples I've seen always involve estimating a very simple quantity, like the location of a single 3D point, from noisy sensors. It's clear how multiple estimates can be combined into a new estimate.
I'd guess that cameras on a self-driving car are trying to estimate something much more complex, something like 3D surfaces labeled with categories ("person", "traffic light", etc.). It's not obvious to me how estimates of such things from multiple sensors and predictions can be sensibly and efficiently combined to produce a better estimate. For example, what if there is a near red object in front of a distant red background, so that the camera estimates just a single object, but the lidar sees two?
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually... > Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.
Until you struggle to review it as well. Simple exercise to prove it - ask LLM to write a function in familiar programming language, but in the area you didn't invest learning and coding yourself. Try reviewing some code involving embedding/SIMD/FPGA without learning it first.
People would struggle to review code in a completely unfamiliar domain or part of the stack even before LLMs.
That’s why you need to write code to learn it.
No-one has ever learned skill just by reading/observing
How would you find yourself in that situation before AI?
No, because they wouldn't be so foolish as to try it.
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
Somewhere, there are GPUs/NPUs running hot. You send all the necessary data, including information that you would never otherwise share. And you most likely do not pay the actual costs. It might become cheaper or it might not, because reasoning is a sticking plaster on the accuracy problem. You and your business become dependent on this major gatekeeper. It may seem like a good trade-off today. However, the personal, professional, political and societal issues will become increasingly difficult to overlook.
This quote stuck out to me as well, for a slightly different reason.
The “tenacity” referenced here has been, in my opinion, the key ingredient in the secret sauce of a successful career in tech, at least in these past 20 years. Every industry job has its intricacies, but for every engineer who earned their pay with novel work on a new protocol, framework, or paradigm, there were 10 or more providing value by putting the myriad pieces together, muddling through the ever-waxing complexity, and crucially never saying die.
We all saw others weeded out along the way for lacking the tenacity. Think the boot camp dropouts or undergrads who changed majors when first grappling with recursion (or emacs). The sole trait of stubbornness to “keep going” outweighs analytical ability, leetcode prowess, soft skills like corporate political tact, and everything else.
I can’t tell what this means for the job market. Tenacity may not be enough on its own. But it’s the most valuable quality in an employee in my mind, and Claude has it.
There is an old saying back home: an idiot never tires, only sweats.
Claude isn't tenacious. It is an idiot that never stops digging because it lacks the meta cognition to ask 'hey, is there a better way to do this?'. Chain of thought's whole raison d'etre was so the model could get out of the local minima it pushed itself in. The issue is that after a year it still falls into slightly deeper local minima.
This is fine when a human is in the loop. It isn't what you want when you have a thousand idiots each doing a depth first search on what the limit of your credit card is.
> it lacks the meta cognition to ask 'hey, is there a better way to do this?'.
Recently had an AI tell me this code (that it wrote) is a mess and suggested wiping it and starting from scratch with a more structure plan. That seems to hint at some meta cognition outlines
Haha, it has the human developer traits of thinking all old code is garbage, failing to identify oneself as the dummy who wrote this particular code, and wanting to start from scratch.
It's like NIH syndrome but instead "not invented here today". Also a very human thing.
More like NIITS: Not Invented in this Session.
Perhaps. I've had LLMs tell me some code is deeply flawed garbage that should be rewritten about code that exact same LLM wrote minutes before. It could be a sign of deep meta cognition, or it might be due to some cognitive gaps where it has no idea why it did something a minute ago and suddenly has a different idea.
Someone will say "you just need to instruct Claude.md to be more meta and do a wiggum loop on it"
I asked Claude to analyze something and report back. It thought for a while said “Wow this analysis is great!” and then went back to thinking before delivering the report. They’re auto-sycophantic now!
Metacognition As A Service, you say?
Running on the Meta Cognition Protocol server near you.
You’ll get sued by Meta for this!
I think that’s called “consulting”.
lol no it doesn’t. It hints at convincing language models
I mean, not always. I've seen Claude step back and reconsider things after hitting a dead end, and go down a different path. There are also workflows, loops that can increase the likelihood of this occurring.
[dead]
This is a major concern for junior programmers. For many senior ones, after 20 (or even 10) years of tenacious work, they realize that such work will always be there, and they long ago stopped growing on that front (i.e. they had already peaked). For those folks, LLMs are a life saver.
At a company I worked for, lots of senior engineers become managers because they no longer want to obsess over whether their algorithm has an off by one error. I think fewer will go the management route.
(There was always the senior tech lead path, but there are far more roles for management than tech lead).
I feel like if you're really spending a ton of time on off by one errors after twenty years in the field you haven't actually grown much and have probably just spent a ton of time in a single space.
Otherwise you'd be senior staff to principle range and doing architecture, mentorship, coordinating cross team work, interviewing, evaluating technical decisions, etc.
I got to code this week a bit and it's been a tremendous joy! I see many peers at similar and lower levels (and higher) who have more years and less technical experience and still write lots of code and I suspect that is more what you're talking about. In that case, it's not so much that you've peaked, it's that there's not much to learn and you're doing a bunch of the same shit over and over and that's of course tiring.
I think it also means that everything you interact with outside your space does feel much harder because of the infrequency with which you have interacted with it.
If you've spent your whole career working the whole stack from interfaces to infrastructure then there's really not going to be much that hits you as unfamiliar after a point. Most frameworks recycle the same concepts and abstractions, same thing with programming languages, algorithms, data management etc.
But if you've spent most of your career in one space cranking tickets, those unknown corners are going to be as numerous as the day you started and be much more taxing.
That's just sad. Right when I found love in what I do, my work has no value anymore.
Aren't you still better off than the rest of us who found what they love + invested decades in it before it lost its value. Isn't it better to lose your love when you still have time to find a new one?
I don't think so. Those of us who found what we love and invested decades into it got to spend decades getting paid well to do what we love.
Depends on if their new love provides as much money as their old one, which is probably not likely. I'd rather have had those decades to stash and invest.
A lot of pre-faang engineers dont have the stash you're thinking about. What you meant was "right when I found a lucrative job that I love". What was going on in tech these last 15 years, unfortunately, probably was once in a lifetime.
It's crazy to think back in the 80's programmers had "mild" salaries despite programming back then being worlds more punishing. No libraries, no stack exchange, no forums, no endless memory and infinite compute. If you had a challenging bug you better also be proficient in reading schematics and probing circuits.
on the bright side software evolved much more slowly in the 80s. You could go very far by being an expert in 1 thing.
People had real offices with actual quiet focus time.
User expectations were also much lower.
pros and cons i guess?
This is genuinely such a good take
Especially on the topic of value! We are all intuitively aware that value is highly contextual, but get in a knot trying to rationalize value long past genuine engagement!
Imagine a senior dev who just approves PRs, approves production releases, and prioritizes bug reports and feature requests. LLM watches for errors ceaslessly, reports an issue. Senior dev reviews the issue and assigns a severity to it. Another LLM has a backlog of features and errors to go solve, it makes a fix and submits a PR after running tests and verifying things work on its end.
Why are we pretending like the need for tenacity will go away? Certain problems are easier now. We can tackle larger problems now that also require tenacity.
Even right at this very moment where we have a high-tenacity AI, I'd argue that working with the AI -- that is to say, doing AI coding itself and dealing with the novel challenges that brings requires a lot of stubborn persistence.
Fittingly, George Hinton toiled away for years in relative obscurity before finally being recognized for his work. I was always quite impressed by his "tenacity".
So although I don't think he should have won the Nobel Prize because not really physics, I felt his perseverance and hard work should merit something.
I still find in these instances there's at least a 50% chance it has taken a shortcut somewhere: created a new, bigger bug in something that just happened not to have a unit test covering it, or broke an "implicit" requirement that was so obvious to any reasonable human that nobody thought to document it. These can be subtle because you're not looking for them, because no human would ever think to do such a thing.
Then even if you do catch it, AI: "ah, now I see exactly the problem. just insert a few more coins and I'll fix it for real this time, I promise!"
The value extortion plan writes itself. How long before someone pitches the idea that the models explicitly almost keep solving your problem to get you to keep spending? Would you even know?
First time I've seen this idea, I have a tingling feeling it might become reality sooner rather than later.
That’s far-fetched. It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise. High value to user + lower compute costs = better pricing power and better margins overall.
> far-fetched
Remember Google?
Once it was far-fetched that they would make the search worse just to show you more ads. Now, it is a reality.
With tokens, it is even more direct. The more tokens users spend, the more money for providers.
> Now, it is a reality.
What are the details of this? I'm not playing dumb, and of course I've noticed the decline, but I thought it was a combination of losing the battle with SEO shite and leaning further and further into a 'give the user what you think they want, rather than what they actually asked for' philosophy.
https://www.wheresyoured.at/the-men-who-killed-google/
Only if you are paying per token on the API. If you are paying a fixed monthly fee then they lose money when you need to burn more tokens and they lose customers when you can’t solve your problems within that month and max out your session limits and end up with idle time which you use to check if the other providers have caught up or surpassed your current favourite.
Indeed, unlimited plan seems like the only way that makes sense to not have it be guaranteed to be abused by the provider
> It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise.
Unless you’re paying by the token.
I was thinking more of deliberate backdoor in code. RCE is an obvious example, but another one could be bias. "I'm sorry ma'am, computer says you are ineligable for a bank account." These ideas aren't new. They were there in 90s already when we still thought about privacy and accountability regarding technology, and dystopian novels already described them long, long ago.
The free market proposition is that competition (especially with Chinese labs and grok) means that Anthropic is welcome to do that. They're even welcome to illegally collude with OpenAi such that ChatGPT is similarly gimped. But switching costs are pretty low. If it turns out I can one shot an issue with Qwen or Deepseek or Kimi thinking, Anthropic loses not just my monthly subscription, but everyone else's I show that too. So no, I think that's some grade A conspiracy theory nonsense you've got there.
It’s not that crazy. It could even happen by accident in pursuit of another unrelated goal. And if it did, a decent chunk of the tech industry would call it “revealed preference” because usage went up.
LLMs became sycophantic and effusive because those responses were rated higher during RLHF, until it became newsworthy how obviously eager-to-please they got, so yes, being highly factually correct and "intelligent" was already not the only priority.
> But switching costs are pretty low
Switching costs are currently low. Once you're committed to the workflow the providers will switch to prepaying for a year's worth of tokens.
To be clear I don't think that's what they're doing intentionally. Especially on a subscription basis, they'd rather me maximize my value per token, or just not use them. Lulling users into using tokens unproductively is the worst possible option.
The way agents work right now though just sometimes feels that way; they don't have a good way of saying "You're probably going to have to figure this one out yourself".
This is a good point. For example if you have access to a bunch of slot machines, one of them is guaranteed to hit the jackpot. Since switching from one slot machine to another is easy, it is trivial to go from machine to machine until you hit the big bucks. That is why casinos have such large selections of them (for our benefit).
"for our benefit" lol! This is the best description of how we are all interacting with LLMs now. It's not working? Fire up more "agents" ala gas town or whatever
last time I was at a casino I checked to see what company built the machines, imagine my surprise that it was (by my observation) a single vendor.
As a rational consumer, how would you distinguish between some intentional "keep pulling the slot machine" failure rate and the intrinsic failure rate?
I feel like saying "the market will fix the incentives" handwaves away the lack of information on internals. After all, look at the market response to Google making their search less reliable - sure, an invested nerd might try Kagi, but Google's still the market leader by a long shot.
In a market for lemons, good luck finding a lime.
FWIW, kagi is better than Google
Is this from a page of dating apps playbook?
> These can be subtle because you're not looking for them
After any agent run, I'm always looking the git comparison between the new version and the previous one. This helps catch things that you might otherwise not notice.
And after manually coding I often have an LLM review the diff. 90% of the problems it finds can be discounted, but it’s still a net positive.
And there is this paradox where it becomes harder to detect the problems as the models 'improve'.
You are using it wrong, or are using a weak model if your failure rate is over 50%. My experience is nothing like this. It very consistently works for me. Maybe there is a <5% chance it takes the wrong approach, but you can quickly steer it in the right direction.
you are using it on easy questions. some of us are not.
A lot of people are getting good results using it on hard things. Obviously not perfect, but > 50% success.
That said, more and more people seem to be arriving at the conclusion that if you want a fairly large-sized, complex task in a large existing codebase done right, you'll have better odds with Codex GPT-5.2-Codex-XHigh than with Claude Code Opus 4.5. It's far slower than Opus 4.5 but more likely to get things correct, and complete, in its first turn.
I think a lot of it comes down to how well the user understands the problem, because that determines the quality of instructions and feedback given to the LLM.
For instance, I know some people have had success with getting claude to do game development. I have never bothered to learn much of anything about game development, but have been trying to get claude to do the work for me. Unsuccessful. It works for people who understand the problem domain, but not for those who don't. That's my theory.
It works for hard problems when the person already solves it and just needs the grunt work done
It also works for problems that have been solved a thousand times before, which impresses people and makes them think it is actually solving those problems
Which matches what they are. They're first and foremost pattern recognition engines extraordinaire. If they can identify some pattern that's out of whack in your code compared to something in the training data, or a bug that is similar to others that have been fixed in their training set, they can usually thwack those patterns over to your latent space and clean up the residuals. If comparing pattern matching alone, they are superhuman, significantly.
"Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape. Their ability to pattern match makes reasoning seem more powerful than it actually is. If your bug is within some reasonable distance of a pattern it has seen in training, reasoning can get it over the final hump. But if your problem is too far removed from what it has seen in its latent space, it's not likely to figure it out by reasoning alone.
>"Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape.
What do you mean by this? Especially for tasks like coding where there is a deterministic correct or incorrect signal it should be possible to train.
> It also works for problems that have been solved a thousand times before
So you mean it works on almost all problems?
Don’t use it for hard questions like this then; you wouldn’t use a hammer to cut a plank, you’d try to make a saw instead
> It might become cheaper or it might not
If it does not, this is going to be first technology in the history of mankind that has not become cheaper.
(But anyway, it already costs half compared to last year)
> But anyway, it already costs half compared to last year
You could not have bought Claude Opus 4.5 at any price one year ago I'm quite certain. The things that were available cost half of what they did then, and there are new things available. These are both true.
I'm agreeing with you, to be clear.
There are two pieces I expect to continue: inference for existing models will continue to get cheaper. Models will continue to get better.
Three things, actually.
The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].
[0]: https://blog.irvingwb.com/blog/2018/09/a-critical-appraisal-...
interesting post. i wonder if these people go back and introspect on how incorrect they have been? do they feel the need to address it?
No, people do not do that.
This is harmless when it comes to tech opinions but causes real damage in politics and activism.
People get really attached to ideals and ideas, and keep sticking to those after they fail to work again and again.
i don't think it is harmless or we are incentivising people to just say whatever they want without any care for truth. people's reputations should be attached to their predictions.
Some people definitely do but how do they go and address it? A fresh example in that it addresses pure misinformation. I just screwed up and told some neighbors garbage collection was delayed for a day because of almost 2ft of snow. Turns out it was just food waste and I was distracted checking the app and read the notification poorly.
I went back to tell them (do not know them at all just everyone is chattier digging out of a storm) and they were not there. Feel terrible and no real viable remedy. Hope they check themselves and realize I am an idiot. Even harder on the internet.
Do _you_ do that?
i try to yes
As a user of LLMs since GPT-3 there was noticeable stagnation in LLM utility after the release of GPT-4. But it seems the RLHF, tool calling, and UI have all come together in the last 12 months. I used to wonder what fools could be finding them so useful to claim a 10x multiplier - even as a user myself. These days I’m feeling more and more efficiency gains with Claude Code.
That's the thing people are missing, the models plateaued a while ago, still making minor gains to this day, but not huge ones. The difference is now we've had time to figure out the tooling. I think there's still a ton of ground to cover there and maybe the models will improve given that the extra time, but I think it's foolish to consider people who predicted that completely wrong. There are also a lot of mathematical concerns that will cause problems in the near and distant future. Infinite progress is far from a given, we're already way behind where all the boosters thought we'd be my now.
I believe Sam Altman, perhaps the greatest grifter in today’s Silicon Valley, claimed that software engineering would be obsolete by the end of last year.
> The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].
Everybody who bet against Moore's Law was wrong ... until they weren't.
And AI is the reaction to Moore's Law having broken. Nobody gave one iota of damn about trying to make programming easier until the chips couldn't double in speed anymore.
This is exactly backwards: Dennard scaling stopped. Moore’s Law has continued and it’s what made training and running inference on these models practical at interactive timescales.
You are technically correct. The best kind of correct.
However, most people don't know the difference between the proper Moore's Law scaling (the cost of a transistor halves every 2 years) which is still continuing (sort of) and the colloquial version (the speed of a transistor doubles every 2 years) which got broken when Dennard scaling ran out. To them, Moore's Law just broke.
Nevertheless, you are reinforcing my point. Nobody gave a damn about improving the "programming" side of things until the hardware side stopped speeding up.
And rather than try to apply some human brainpower to fix the "programming" side, they threw a hideous number of those free (except for the electricity--but we don't mention that--LOL) transistors at the wall to create a broken, buggy, unpredictable machine simulacrum of a "programmer".
(Side note: And to be fair, it looks like even the strong form of Moore's Law is finally slowing down, too)
If you can turn a few dollars of electricity per hour into a junior-level programmer who never gets bored, tired, or needs breaks, that fundamentally changes the economics of information technology.
And in fact, the agentic looped LLMs are executing much better than that today. They could stop advancing right now and still be revolutionary.
That's not true. Many technologies get more expensive over time, as labor gets more expensive or as certain skills fall by the wayside, not everything is mass market. Have you tried getting a grandfather clock repaired lately?
Repairing grandfather clocks isn't more expensive now because it's gotten any harder; it's because the popularity of grandfather clocks is basically nonexistent compared to anything else to tell time.
"repairing a unique clock" getting costlier doesn't mean technology hasn't gotten cheaper.
check out whether clocks have gotten cheaper in general. the answer is that it has.
there is no economy of scale here in repairing a single clock. its not relevant to bring it up here.
Clocks prices have gone up since 2020. Unless a cheaper better way to make clocks has emerged inflation causes prices to grow.
Luxury watches have gone up, 'clocks' as a technology is cheaper than ever.
You can buy one for 90 cents on temu.
The landing cost for that 90 cent watch has gone way up. Shipping and to some degree taxes has pushed the price higher.
that's not the technology
of course it's silly to talk about manufacturing methods and yield and cost efficiency without having an economy to embed all of this into, but ... technology got cheaper means that we have practical knowledge of how to make cheap clocks (given certain supply chains, given certain volume, and so and so)
we can make very cheap very accurate clocks that can be embedded into whatever devices, but it requires the availability of fabs capable of doing MEMS components, supply materials, etc.
not true, clocks have gone down after accounting for inflation. verified using ChatGPT.
You can't account for inflation because the price increase is inflation.
you can look at a basket of goods that doesn't have your specific product and compare directly
but inflation is the general price level increase, this can be used as a deflator to get the price of whatever product in past/future money amount to see how the price of the product changed in "real" terms (ie. relative to the general price level change)
this is not true
Instead of advancing tenuous examples you could suggest a realistic mechanism by which costs could rise, such as a Chinese advance on Taiwan, effecting TSMC, etc.
Time-keeping is vastly cheaper. People don't want grandfather clocks. They want to tell time. And they can, more accurately, more easily, and much cheaper than their ancestors.
No. You don't get to make "technology gets more expensive over time" statements for deprecated technologies.
Getting a bespoke flintstone axe is also pretty expensive, and has also absolutely no relevance to modern life.
These discussions must, if they are to be useful, center in a population experience, not in unique personal moments.
I purchased a 5T drive in 2019 and the price is higher now despite newer better drives going on the market since.
Not much has down in price over the last few years.
Price volatility exists.
Meanwhile the overall price of storage has been going down consistently: https://ourworldindata.org/grapher/historical-cost-of-comput...
okay how about the Francis Scott Key Bridge?
https://marylandmatters.org/2025/11/17/key-bridge-replacemen...
You will get a different bridge. With very different technology. Same as "I can't repair my grandfather clock cheaply".
In general, there are several things that are true for bridges that aren't true for most technology:
* Technology has massively improved, but most people are not realizing that. (E.g. the Bay Bridge cost significantly more than the previous version, but that's because we'd like to not fall down again in the next earthquake) * We still have little idea how to reason about the cost of bridges in general. (Seriously. It's an active research topic) * It's a tiny market, with the major vendors forming an oligopoly * It's infrastructure, not a standard good * The buy side is almost exclusively governments.
All of these mean expensive goods that are completely non-repeatable. You can't build the same bridge again. And on top of that, in a distorted market.
But sure, the cost of "one bridge, please" has gone up over time.
This seems largely the same as any other technology. The prices of new technologies go down initially as we scale up and optimize it's production, but as soon as demand fades, due to newer technology or whatever, the cost of that technology goes up again.
> But sure, the cost of "one bridge, please" has gone up over time.
Even if you adjust for inflation?
Bought any RAM lately? Phone? GPU in the last decade?
The latest iphone has gone down in price? It's double. I guess the marketing is working.
"Pens are not cheaper, look at this Montblanc" is not a good faith response.
'84 Motorola DynaTAC - ~$12k AfI (adjusted for inflation)
'89 MicroTAC ~$8k AfI
'96 StarTAC ~$2k AfI
`07 iPhone ~$673 AfI
The current average smartphone sells for around $280. Phones are getting cheaper.
Or riding in an uber?
Sure, running an LLM is cheaper, but the way we use LLMs now requires way more tokens than last year.
10x more tokens today cost less than than half of X tokens from ~mid 2024.
ok but the capabilities are also rising. what point are you trying to make?
That it's not getting cheaper?
But it is, capability adjusted, which is the only way it makes sense. You can definitely produce last years capability at a huge discount.
you are wrong. https://epoch.ai/data-insights/llm-inference-price-trends
this is accounting for the fact that more tokens are used.
The chart shows that they’re right though. Newer models cost more than older models. Sure they’re better but that’s moot if older models are not available or can’t solve the problem they’re tasked with.
this is incorrect. the cost to achieve the same task by old models is way higher than by new models.
> Newer models cost more than older models
where did you see this?
On the link you shared, 4o vs 3.5 turbo price per 1m tokens.
There’s no such thing as ”same task by old model”, you might get comparable results or you might not (and this is why the comparison fail, it’s not a comparison), the reason you pick the newer models is to increase chances of getting a good result.
> The dataset for this insight combines data on large language model (LLM) API prices and benchmark scores from Artificial Analysis and Epoch AI. We used this dataset to identify the lowest-priced LLMs that match or exceed a given score on a benchmark. We then fit a log-linear regression model to the prices of these LLMs over time, to measure the rate of decrease in price. We applied the same method to several benchmarks (e.g. MMLU, HumanEval) and performance thresholds (e.g. GPT-3.5 level, GPT-4o level) to determine the variation across performance metrics
This should answer. In your case, GPT-3.5 definitely is cheaper per token than 4o but much much less capable. So they used a model that is cheaper than GPT-3.5 that achieved better performance for the analysis.
OpenAI has always priced newer models lower than older ones.
not true! 4o was costlier than 3.5 turbo
https://platform.openai.com/docs/pricing
Not according to their pricing table. Then again I’m not sure what OpenAI model versions even mean anymore, but I would assume 5.2 is in the same family as 5 and 5.2-pro as 5-pro
Check GPT 5.2 vs it's predecessor the 'o' series of reasoning models.
I don't think computation is going to become more expensive, but there are techs that have become so: Nuclear power plants. Mobile phones. Oil extraction.
(Oil rampdown is a survival imperative due to the climate catastrophe so there it's a very positive thing of course, though not sufficient...)
Not true. Bitcoin has continued to rise in cost since its introduction (as in the aggregate cost incurred to run the network).
LLMs will face their own challenges with respect to reducing costs, since self-attention grows quadratically. These are still early days, so there remains a lot of low hanging fruit in terms of optimizations, but all of that becomes negligible in the face of quadratic attention.
> bitcoin
so close! that is a commodity
For Bitcoin that’s by design!
There are plenty of technologies that have not become cheaper, or at least not cheap enough, to go big and change the world. You probably haven't heard of them because obviously they didn't succeed.
cheaper doesnt mean cheap enough to be viable after the bills come due
Concorde?
Supersonic jet engines, rockets to the moon, nuclear power plants, etc. etc. all have become more expensive. Superconductors were discovered in 1911, and we have been making them for as long as we have been making transistors in the 1950s, yet superconductors show no sign of becoming cheaper any time soon.
There have been plenty of technologies in history which do not in fact become cheaper. LLMs are very likely to become such, as I suspect their usefulness will be superseded by cheaper (much cheaper in fact) specialized models.
> you most likely do not pay the actual costs. It might become cheaper or it might not
Why would this be the first technology that doesn't become cheaper at scale over time?
> And you most likely do not pay the actual costs.
This is one of the weakest anti AI postures. "It's a bubble and when free VC money stops you'll be left with nothing". Like it's some kind of mystery how expensive these models are to run.
You have open weight models right now like Kimi K2.5 and GLM 4.7. These are very strong models, only months behind the top labs. And they are not very expensive to run at scale. You can do the math. In fact there are third parties serving these models for profit.
The money pit is training these models (and not that much if you are efficient like chinese models). Once they are trained, they are served with large profit margins compared to the inference cost.
OpenAI and Anthropic are without a doubt selling their API for a lot more than the cost of running the model.
Humans run hot too. Once you factor in the supply chain that keeps us alive, things become surprisingly equivalent.
Eating burgers and driving cars around costs a lot more than whatever # of watts the human brain consumes.
I mean, “equivalent” is an understatement! There’s a reason Claude Code costs less than hiring a full time software engineer…
> Somewhere, there are GPUs/NPUs running hot.
Running at their designed temperature.
> You send all the necessary data, including information that you would never otherwise share.
I've never sent the type of data that isn't already either stored by GitHub or a cloud provider, so no difference there.
> And you most likely do not pay the actual costs.
So? Even if costs double once investor subsidies stop, that doesn't change much of anything. And the entire history of computing is that things tend to get cheaper.
> You and your business become dependent on this major gatekeeper.
Not really. Switching between Claude and Gemini or whatever new competition shows up is pretty easy. I'm no more dependent on it than I am on any of another hundred business services or providers that similarly mostly also have competitors.
My agent struggled for 45 minutes because it tried to do `go run` on a _test.go file, which the compiler repeatedly exited after posting an error message that files named like this cannot be executed using the run command.
So yeah, that wasted a lot of GPU cycles for a very unimpressive result, but with a renewed superficial feeling of competence
I don’t understand this pov. Unfortunately, id pay 10k mo for my cc sub. I wish I could invest in anthropic, they’re going to be the most profitable company on earth
To me this tenacity is often like watching someone trying to get a screw into board using a hammer.
There’s often a better faster way to do it, and while it might get to the short term goal eventually, it’s often created some long term problems along the way.
> And you most likely do not pay the actual costs
Oh my lord you absolutely do not. The costs to oai per token inference ALONE are at least 7x. AT LEAST and from what I’ve heard, much higher.
We can observe how much generic inference providers like deepinfra or together-ai charge for large SOTA models. Since they are not subsidized and they don’t charge 7x of OpenAI, that means OAI also doesn’t have outrageously high per-token costs.
It is also amazing seeing Linux kernel work, scheduling threads, proving interrupts and API calls all without breaking a sweat or injuring its ACL.
With optimizations and new hardware, power is almost a negligible cost. You can get 5.5M tokens/s/MW[1] for kimi k2(=20M/KWH=181M tokens/$) which is 400x cheaper than current pricing. It's just Nvidia/TSMC/other manufacturers eating up the profit now because they can. My bet is that China will match current Nvidia within 5 years.
[1]: https://developer-blogs.nvidia.com/wp-content/uploads/2026/0...
Electricity is negligible but the dominant cost is the hardware depreciation itself. Also inference is typically memory bandwidth bound so you are limited by how fast you can move weights rather than raw compute efficiency.
Yes, because the margin is like 80% for Nvidia, and 80% again for the manufacturers like Samsung and TSMC. Once the fixed cost like R and D is amortized the same node technology and hardware capacity could be just few single digit percent of current.
AI genius discover brute forcing... what a time to be alive. /s
Like... bro that's THE foundation of CS. That's the principle of The bomb in Turing's time. One can still marvel at it but it's been with us since the beginning.
Are there good guides about how to write Agents or good repos with examples? Also, are there big differences between how you would write one in Codex cli vs Claude code? Can there be run on it interchangeably?
So what is he even coding there all the time?
Does anybody have any info on what he is actually working on besides all the vibe-coding tweets?
There seems to be zero output from they guy for the past 2 years (except tweets)
> There seems to be zero output from they guy for the past 2 years (except tweets)
Well, he made Nanochat public recently and has been improving it regularly [1]. This doesn't preclude that he might be working on other projects that aren't public yet (as part of his work at Eureka Labs).
1: https://github.com/karpathy/nanochat
So, it's generative pre-trained transformers again?
Helper scripts for APIs for applications and tools I know well. LLMs have made my work bearable. Many software providers expose great apis, but expert use cases require data output/input that relies on 50-500 line scripts. Thanks to the models post gpt4.5 most requirements are solvable in 15 minutes when they could have taken multiple workdays to write and check by hand. The only major gap is safe ad-hoc environments to run these in. I provide these helper functions for clients that would love to keep the runtime in the same data environment as the tool, but not all popular software support FaaS style environments that provide something like a simple python env.
He's building Eureka Labs[1], an AI-first education company (can't wait to use it). He's both a strong researcher[2] and an unusually gifted technical communicator. His recent videos[3] are excellent educational material.
More broadly though: someone with his track record sharing firsthand observations about agentic coding shouldn't need to justify it by listing current projects. The observations either hold up or they don't.
[1] https://x.com/EurekaLabsAI
[2] PhD in DL, early OpenAI, founding head of AI at Tesla
[3] https://www.youtube.com/@AndrejKarpathy/videos
I don’t know, but it’s interesting that he and many others come up with this “we should act like LLMs are junior devs”. There is a reason why most junior devs work on fairly separate parts of products, most of the time parts which can be removed or replaced easily, and not an integral part of products: because their code is usually quite bad. Like every few lines contains issues, suboptimal solutions, and full with architectural problems. You basically never trust junior devs with core product features. Yet, we should pretend that an “LLM junior dev” is somehow different. These just signal to me that these people don’t work on serious code.
This is the first question I ask, and every time I get the answer of some monolith that supposedly solves something. Imo, this is completely fine for any personal thing, I am happy when someone says they made an API to compare weekly shopping prices from the stores around them, or some recipe, this makes sense.
However more often than not, someone is just building a monolithic construction that will never be looked at again. For example, someone found that HuggingFace dataloader was slow for some type of file size in combination with some disk. What does this warrant? A 300000+ line non-reviewed repo to fix this issue. Not a 200-line PR to HuggingFace, no you need to generate 20% of the existing repo and then slap your thing on there.
For me this is puzzling, because what is this for? Who is this for? Usually people built these things for practice, but now its generated, so its not for practice because you made very little effort on it. The only thing I can see that its some type of competence signaling, but here again, if the engineer/manager looking knows that this is generated, it does not have the type of value that would come with such signaling. Either I am naive and people still look at these repos and go "whoa this is amazing", or it's some kind of induced egotrip/delusion where the LLM has convinced you that you are the best builder.
> - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
Starcraft and Factorio are exactly what it is not. Starcraft has a loooot of micro involved at any level beyond mid level play, despite all the "pro macros and beats gold league with mass queens" meme videos. I guess it could be like Factorio if you're playing it by plugging together blueprint books from other people but I don't think that's how most people play.
At that level of abstraction, it's more like grand strategy if you're to compare it to any video game? You're controlling high level pushes and then the units "do stuff" and then you react to the results.
I think the StarCraft analogy is fine, you have to compare it not to macro and micro RTS play, but to INDIVIDUAL UNITS. For your whole career until now, you have been a single Zergling or Probe. Now you are the Commander.
Except that pro starcraft player still micro-manage every single Zergling or probe when necessary, while vibe coders just right click on the ennemy base and hope it'll go well
It's like the Victoria 3 combat system. You just send an army and a general to a given front and let them get to work with no micro. Easy! But of course some percentage of the time they do something crazy like deciding to redeploy from your existential Franco-Prussian war front to a minor colonial uprising...
I wish the people who wrote this let us know what king of codebases they are working on. They seem mostly useless in a sufficiently large codebase especially when they are messy and interactions aren't always obvious. I don't know how much better Claude is than ChatGPT, but I can't get ChatGPT to do much useful with an existing large codebase.
This is an antidotal example, but I released this last week after 3 months of work on it as a "nights and weekdends" project: https://apps.apple.com/us/app/skyscraper-for-bluesky/id67541...
I've been working in the mobile space since 2009, though primarily as a designer and then product manager. I work in kinda a hybrid engineering/PM job now, and have never been a particularly strong programmer. I definitely wouldn't have thought I could make something with that polish, let alone in 3 months.
That code base is ~98% Claude code.
I don’t know if “antidotal example” is a pun or a typo but I quite like it.
Lol typing on my phone during lunch and meant anecdotal. But let's leave it anyways. :)
That is fun.
Not sure if it's an American pronunciation thing, but I had to stare at that long and hard to see the problem and even after seeing it couldn't think of how you could possibly spell the correct word otherwise.
> Not sure if it's an American pronunciation thing
It's a bad American pronunciation thing like "Febuwary" and "nuculer".
If you pronounce the syllables correctly, "an-ec-dote", "Feb-ru-ar-y", "nu-cle-ar" the spellings follow.
English has it's fair share of spelling stupidities, but if people don't even pronounce the words correctly there is no hope.
Are you using Codex?
I'm not sure how big your repos are but I've been effective working with repos that have thousands of files and tens of thousands of lines of code.
If you're just prototyping it will hit wall when things get unwieldy but that's normally a sign that you need to refactor a bit.
Super strict compiler settings, static analysis, comprehensive tests, and documentation help a lot. As does basic technical design. After a big feature is shipped I do a refactor cycle with the LLM where we do a comprehensive code review and patch things up. This does require human oversight because the LLMs are still lacking judgement on what makes for good code design.
The places where I've seen them be useless is working across repositories or interfacing with things like infrastructure.
It's also very model-dependent. Opus is a good daily driver but Codex is much better are writing tests for some reason. I'll often also switch to it for hard problems that Claude can't solve. Gemini is nice for 'I need a prototype in the next 10 minutes', especially for making quick and dirty bespoke front-ends where you don't care about the design just the functionality.
> tens of thousands of lines of code
Perhaps this is part of it? Tens of thousands of lines of code seems like a very small repo to me.
It's important to understand that he's talking about a specific set of models that were release around november/december, and that we've hit a kind of inflection point in model capabilities. Specifically Anthropic's Opus 4.5 model.
I never paid any attention to different models, because they all felt roughly equal to me. But Opus 4.5 is really and truly different. It's not a qualitative difference, it's more like it just finally hit that quantitative edge that allows me to lean much more heavily on it for routine work.
I highly suggest trying it out, alongside a well-built coding agent like the one offered by Claude Code, Cursor, or OpenCode. I'm using it on a fairly complex monorepo and my impressions are much the same as Karpathy's.
People have said this about every single model release.
Claude and Codex are CLI tools you use to give the LLM context about the project on your local machine or dev environment. The fact that you're using the name "ChatGPT" instead of Codex leads me to believe you're talking about using the web-based ChatGPT interface to work on a large codebase, which is completely beside the point of the entire discussion. That's not the tool anyone is talking about here.
Almost always, notes like these are going to be about greenfield projects.
Trying to incorporate it in existing codebases (esp when the end user is a support interaction or more away) is still folly, except for closely reviewed and/or non-business-logic modifications.
That said, it is quite impressive to set up a simple architecture, or just list the filenames, and tell some agents to go crazy to implement what you want the application to do. But once it crosses a certain complexity, I find you need to prompt closer and closer to the weeds to see real results. I imagine a non-technical prompter cannot proceed past a certain prototype fidelity threshold, let alone make meaningful contributions to a mature codebase via LLM without a human engineer to guide and review.
I'm using it on a large set of existing codebases full of extremely ugly legacy code, weird build systems, tons of business logic and shipping directly to prod at neckbreaking growth over the last two years, and it's delivering the same type of value that Karpathy writes about.
That was true for me, but is no longer.
It's been especially helpful in explaining and understanding arcane bits of legacy code behavior my users ask about. I trigger Claude to examine the code and figure out how the feature works, then tell it to update the documentation accordingly.
> I trigger Claude to examine the code and figure out how the feature works, then tell it to update the documentation accordingly.
And how do you verify its output isn't total fabrication?
Your docs are a contact. You can verify that contract using integration tests
Contract? These docs are information answering user queries. So if you use a chatbot to generate them, I'd like to be reasonably sure they aren't laden with the fabricated misinformation for which these chatbots are famous.
These models do well changing brownfield applications that have tests because the constraints on a successful implementation are tight. Their solutions can be automatically augmented by research and documentation.
I don't exactly disagree with this but I have seen models simply deleting the tests, or updating the tests to pass and declaring the failures were "unrelated to my changes", so it helpfully fixed them
Yes. You have to treat the model like an eager yet incompetent worker, i.e. don't go full yolo mode and review everything they do.
For me, in just the golang server instance and the core functional package, `cloc` reports over 40k lines of code, not counting other supporting packages. I spent the last week having Claude rip out the external auth system and replace it with a home-grown one (and having GPT-codex review its changes). If anything, Claude makes it easier on me as a solo founder with a large codebase. Rather than having to re-familiarize myself with code I wrote a year ago, I describe it at a high level, point Claude to a couple of key files, and then tell it to figure out what it needs to do. It can use grep, language server, and other tools to poke around and see what's going on. I then have it write an "epic" in markdown containing all the key files, so that future sessions already know the key files to read.
I really enjoyed the process. As TFA says, you have to keep a close eye on it. But the whole process was a lot less effort, and I ended up doing mor than I would otherwise have done.
At my dayjob my team uses it on our main dashboard, which is a pretty large CRUD application. The frontend (Vue) is a horrible mess, as it was originally built by people who know just enough to be dangerous. Over time people have introduced new standards without cleaning up the old code - for example, we have three or four different state management techologies.
For this the LLM struggles a bit, but so does a human. The main issues are it messes up some state that it didnt realise was used elsewhere, and out test coverage is not great. We've seen humans make exactly the same kind of mistakes. We use MCP for Figma so most of the time it can get a UI 95% done, just a few tweaks needed by the operator.
On the backend (Typescript + Node, good test coverage) it can pretty much one-shot - from a plan - whatever feature you give it.
We use opus-4.5 mostly, and sometimes gpt-5.2-codex, through Cursor. You aren't going to get ChatGPT (the web interface) to do anything useful, switch to Cursor, Codex or Claude Code. And right now it is worth paying for the subscription, you don't get the same quality from cheaper or free models (although they are starting to catch up, I've had promising results from GLM-4.7).
Another personal example. I spent around a month last year in January on this application: https://apps.apple.com/us/app/salam-prayer-qibla-quran/id674...
I had never used Swift before that and was able to use AI to whip up a fairly full-featured and complex application with a decent amount of code. I had to make some cross-cutting changes along the way as well that impacted quite a few files and things mostly worked fine with me guiding the AI. Mind you this was a year ago so I can only imagine how much better I would fare now with even better AI models. That whole month was spent not only on coding but on learning Swift enough to fix problems when AI started running into circles and then learning about Xcode profiler to optimize the application for speed and improving perf.
I don't know how big sufficiently large codebase is, but we have a 1mil loc Java application, that is ~10years old, and runs POS systems, and Claude Code has no issues with it. We have done full analyses with output details each module, and also used it to pinpoint specific issues when described. Vibe coding is not used here, just analysis.
> They seem mostly useless in a sufficiently large codebase especially when they are messy and interactions aren't always obvious.
What type of documents do you have explaining the codebase and its messy interactions, and have you provided that to the LLM?
Also, have you tried giving someone brand new to the team the exact same task and information you gave to the LLM, and how effective were they compared to the LLM?
> I don't know how much better Claude is than ChatGPT, but I can't get ChatGPT to do much useful with an existing large codebase.
As others have pointed out, from your comment, it doesn't sound like you've used a tool dedicated for AI coding.
(But even if you had, it would still fail if you expect LLMs to do stuff without sufficient context).
The code base I work on at $dayjob$ is legacy, has few files with 20k lines each and a few more with around 10k lines each. It's hard to find things and connect dots in the code base. Dont think LLMs able to navigate and understand code bases of that size yet. But have seen lots of seemingly large projects shown here lately that involve thousands of files and millions of lines of code.
I’ve found that LLMs seem to work better on LLM-generated codebases.
Commercial codebases, especially private internal ones, are often messy. It seems this is mostly due to the iterative nature of development in response to customer demands.
As a product gets larger, and addresses a wider audience, there’s an ever increasing chance of divergence from the initial assumptions and the new requirements.
We call this tech debt.
Combine this with a revolving door of developers, and you start to see Conway’s law in action, where the system resembles the organization of the developers rather than the “pure” product spec.
With this in mind, I’ve found success in using LLMs to refactor existing codebases to better match the current requirements (i.e. splitting out helpers, modularizing, renaming, etc.).
Once the legacy codebase is “LLMified”, the coding agents seem to perform more predictably.
YMMV here, as it’s hard to do large refactors without tests for correctness.
(Note: I’ve dabbled with a test first refactor approach, but haven’t gone to the lengths to suggest it works, but I believe it could)
are LLM codebases not messy?
Claude by default, unless I tell it not to, will write stuff like:
instead of the very simple boolean logic that could express this in one line, with the "this code does what it obviously does" comments added all over the place.generally unless you tell it not to, it does things in very verbose ways that most humans would never do, and since there's an infinite number of ways that it can invent absurd verbosity, it is hard to preemptively prompt against all of them.
to be clear, I am getting a huge amount of value out of it for executing a bunch of large refactors and "modernization" of a (really) big legacy codebase at scale and in parallel. but it's not outputting the sort of code that I see when someone prompts it "build a new feature ...", and a big part of my prompts is screaming at it not to do certain things or to refuse the task if it at any point becomes unsure.
Yeah to be clear it will have the same issues as a flyby contributor if prompted to.
Meaning if you ask it “handle this new condition” it will happily throw in a hacky conditional and get the job done.
I’ve found the most success in having it reason about the current architecture (explicitly), and then to propose a set of changes to accomplish the task (2-5 ways), review, and then implement the changes that best suit the scope of the larger system.
The failure mode is missing constraints, not “coding skill”. Treat the model as a generator that must operate inside an explicit workflow: define the invariant boundaries, require a plan/diff before edits, run tests and static checks, and stop when uncertainty appears. That turns “hacky conditional” behaviour into controlled change.
Yes, exactly.
The LLM is onboarding to your codebase with each context window, all it knows is what it’s seen already.
Surely because LLM generated code is part of the training data for the model, so code/patterns it can work with is closer to its training data.
I successfully use Claude Code in a large complex codebase. It's Clojure, perhaps that helps (Clojure is very concise, expressive and hence token-dense).
Perhaps it's harder to "do Closure wrong" than it is to do JavaScript or Python or whatever other extremely flexible multi-paradigm high-level language
Having spent 3 years of my career working with Clojure, I think it actually gives you even more rope to shoot yourself with than Python/JS.
E.g. macros exist in Clojure but not Python/JS, and I've definitely been plenty stumped by seeing them in the codebase. They tend to be used in very "clever" patterns.
On the other hand, I'm a bit surprised Claude can tackle a complex Clojure codebase. It's been a while since I attempted using an LLM for Clojure, but at the time it failed completely (I think because there is relatively little training data compared to other mainstream languages). I'll have to check that out myself
If you have a ChatGPT account, there's nothing stopping you from installing codex cli and using your chatgpt account with it. I haven't coded with ChatGPT for weeks. Maybe a month ago I got utility out of coding with codex and then having ChatGPT look at my open IDE page to give comments, but since 5.2 came out, it's been 100% codex.
Try Claude code. It’s different.
After you tried it, come back.
I think its not Claude code per se itself but rather the (Opus 4.5 model?) or something in an agentic workflow.
I tried a website which offered the Opus model in their agentic workflow & I felt something different too I guess.
Currently trying out Kimi code (using their recent kimi 2.5) for the first time buying any AI product because got it for like 1.49$ per month. It does feel a bit less powerful than claude code but I feel like monetarily its worth it.
Y'know you have to like bargain with an AI model to reduce its pricing which I just felt really curious about. The psychology behind it feels fascinating because I think even as a frugal person, I already felt invested enough in the model and that became my sunk cost fallacy
Shame for me personally because they use it as a hook to get people using their tool and then charge next month 19$ (I mean really Cheaper than claude code for the most part but still comparative to 1.49$)
1. Write good documentation, architecture, how things work, code styling, etc.
2. Put your important dependencies source code in the same directory. E.g. put a `_vendor` directory in the project, in it put the codebase at the same tag you're using or whatever: postgres, redis, vue, whatever.
3. Write good plans and requirements. Acceptance criteria, context, user stories, etc. Save them in markdown files. Review those multiple times with LLMs trying to find weaknesses. Then move to implementation files: make it write a detailed plan of what it's gonna change and why, and what it will produce.
4. Write very good prompts. LLMs follow instructions well if they are clear "you should proactively do X", is a weak instruction if you mean "you must do X".
5. LLMs are far from perfect, and full of limits. Karpathy sums their cons very well in his long list. If you don't know their limits you'll mismanage the expectations and not use them when they are a huge boost and waste time on things they don't cope well with. On top of that: all LLMs are different in their "personality", how they adhere to instruction, how creative they are, etc.
I've been trying Claude on my large code base today. When I give it the requirements I'd give an engineer and so "do it" it just writes garbage that doesn't make sense and doesn't seem to even meet the requirements (if it does I can't follow how - though I'll admit to giving up before I understood what it did, and I didn't try it on a real system). When I forced it to step back and do tiny steps - in TDD write one test of the full feature - it did much better - but then I spent the next 5 hours adjusting the code it wrote to meet our coding standards. At least I understand the code, but I'm not sure it is any faster (but it is a lot easier to see things wrong than come up with green field code).
Which is to say you have to learn to use the tools. I've only just started, and cannot claim to be an expert. I'll keep using them - in part because everyone is demanding I do - but to use them you clearly need to know how to do it yourself.
Have you tried showing it a copy of your coding standards?
I also find pointing it to an existing folder full of code that conforms to certain standards can work really well.
At least some of them that it violated it has seen.
Yeah let's share all your IP for the vague promise that it will somehow work ;)
You just gave me a revelation as to why some people report being unable to get decent results out of coding agents!
(Shrug) If you're not willing to make that tradeoff, you'll be outcompeted by people who are. Your call.
I've been playing around with the "Superpowers" [0] plugin in Claude Code on a new small project and really like it. Simple enough to understand quickly by reading the GitHub repo and seems to improve the output quality of my projects.
There's basically a "brainstorm" /slash command that you go back and forth with, and it places what you came up with in docs/plans/YYYY-MM-DD-<topic>-design.md.
Then you can run a "write-plan" /slash command on the docs/plans/YYYY-MM-DD-<topic>-design.md file, and it'll give you a docs/plans/YYYY-MM-DD-<topic>-implementation.md file that you can then feed to the "execute-plan" /slash command, where it breaks everything down into batches, tasks, etc, and actually implements everything (so three /slash commands total.)
There's also "GET SHIT DONE" (GSD) [1] that I want to look at, but at first glance it seems to be a bit more involved than Superpowers with more commands. Maybe it'd be better for larger projects.
[0] https://github.com/obra/superpowers
[1] https://github.com/glittercowboy/get-shit-done
it's all about the context. observe what files it opened, etc. good luck
Also I never see anyone talking about code reviews, which is one of the primary ways that software engineering departments manage liability. We fired someone recently because they couldn’t explain any of the slop they were trying to get merged. Why tf would I accept the liability of managing code that someone else can’t even explain?
I guess this is fine when you don’t have customers or stakeholders that give a shit lol.
They build Claude Code fully with Claude Code.
Which is equal parts praise and damnation. Claude Code does do a lot of nice things that people just kind of don't bother for time cost / reward when writing TUIs that they've probably only done because they're using AI heavily, but equally it has a lot of underbaked edges (like accidentally shadowing the user's shell configuration when it tries to install terminal bindings for shift-enter even though the terminal it's configuring already sends a distinct shift-enter result), and bugs (have you ever noticed it just stop, unfinished?).
i haven't used Claude Code but come on.. it is a production level quality application used seriously by millions.
Look up the flickering issue. The program was created by dunces.
If you haven't used it, how can you judge its quality level?
Ah, now I understand why @autocomplete suddenly got broken between versions and still not fixed )
What do you even mean by "ChatGPT"? Copy pasting code into chatgpt.com?
AI assisted coding has never been like that, which would be atrocious. The typical workflow was using Cursor with some model of your choice (almost always an Anthropic model like sonnet before opus 4.5 released). Nowadays (in addition to IDEs) it's often a CLI tool like Claude Code with Opus or Codex CLI with GPT Codex 5.2 high/xhigh.
chatGPT is not made to write code. Get out of stone age :)
I'm afraid that we're entering a time when the performance difference between the really cutting edge and even the three-month-old tools is vast
If you're using plain vanilla chatgpt, you're woefully, woefully out of touch. Heck, even plain claude code is now outdated
Why is plain Claude code outdated? I thought that’s what most people are using right now that are AI forward. Is it Ralph loops now that’s the new thing?
Plain Claude Code doesn’t have enough scaffolding to handle large projects
At a base level, people are “upgrading” their Claude Code with custom skills and subagents - all text files saved in .claude/agents|skills.
You can also use their new tasks primitive to basically run a Ralph-like loop
But at the edges, people are using multiple instances, each handling different aspects in parallel - stuff like Gas Town
Tbf you can still get a lot of mileage out of vanilla Claude Code. But I’ve found that even adding a simple frontend design skill improves the output substantially
Is there anywhere where we can learn more about creating your own agents/skills? Maybe some decent public repos that you could recommend.
You can just ask Claude to create them. They’re just markdown files
Anthropic’s own repo is as good place as any
https://github.com/anthropics/skills
People who just let the agent code for them, how big of a codebase are you working on? How complex (i.e. is it a codebase that junior programmers could write and maintain)?
I've been an EM for the last 10 of my 25 year Software Engineering career. Coding is, frankly, boring to me anymore, even though I enjoyed doing it most of my career. I had this project I wanted to exist in world but couldn't be bothered to get started.
Decided to figure out what this "vibe coding" nonsense is, and now there's a certain level of joy to all of this again. Being able to clearly define everything using markdown contexts before any code is even written has been a great way to brain dump those 25 years of experience and actually watch something sane get produced.
Here are the stats Claude Code gave me:
In case anyone is curious, here was my epiphany project from 2 weeks ago: https://github.com/boj/the-project
I then realized I could feed it everything it ever needed to know. Just create a docs/* folder and tell it to read that every session.
Through discovery I learned about CLAUDE.md, and adding skills.
Now I have an /analyst, /engineer, and /devops that I talk to all day with their own logic and limitations, as well as the more general project CLAUDE.md, and dozens of docs/* files we collaborate on.
I'm at the point I'm running happy.engineering on my phone and don't even need to sit in front of the computer anymore.
Interesting!
I wonder if this line
> It will configure an auth_backend.rs and wire up a basic user
over a big enough number of projects will lead to at least 2-3 different user names.
How much did this type of project cost you to make?
rust compiler and redox operating system with modified Qemu for Mac Vulcan metal pipeline ... probably not junior stuff
you might think I'm kidding but Search redox on github, you will find that project and the anonymous contributions
I am curious. What do you want us to see in that github repo?
Feels like a combination of writing very detailed task descriptions and reviewing junior devs. It's horrible. I very much hope this won't be my job.
Minor nitpick: The original measure of a 10x programmer was not the productivity multiplier max/mean, but rather max/min.
> the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*
I have a professor who has researched auto generated code for decades and about six months ago he told me he didn't think AI would make humans obsolete but that it was like other incremental tools over the years and it would just make good coders even better than other coders. He also said it would probably come with its share of disappointments and never be fully autonomous. Some of what he said was a critique of AI and some of it was just pointing out that it's very difficult to have perfect code/specs.
I can sense two classes of coders emerging.
Billionaire coder: a person who has "written" billion lines.
Ordinary coders : people with only couple of thousands to their git blame.
Boris Cherny (Claude Code creator) replies to Andrej Karpathy
https://xcancel.com/bcherny/status/2015979257038831967
I think in less than a year writing code manually will be akin to doing arithmetic problems by hand. Sure you can still code manually, but it's going to be a lot faster to use an LLM (calculator).
People keep using these analogies but I think these are fundamentally different things.
1. hand arithmetic -> using a calculator
2. assembly -> using a high level language
3. writing code -> making an LLM write code
Number 3 does not belong. Number 3 is a fundamentally different leap because it's not based on deterministic logic. You can't depend on an LLM like you can depend on a calculator or a compiler. LLMs are totally different.
There are definitely parallels though. eg you could swap out your compiler for a different one that produces slightly different assembly. Similarly a LLM may implement things differently…but if it works do we care? Probably no more than when you buy software you don’t care precisely what compiler optimisation were used. The precise deterministicness isn’t a key feature
With the llm, it might work or it might not. If it doesn't work, then you have to keep iterating and hand holding it to make it work. Sometimes that process is less optimal than writing the code manually. With a calculator, you can be sure that the first attempt will work. An idiot with a calculator can still produce correct results. An idiot with an llm often cannot outside trivial solutions.
> but if it works so we care?
It often doesn't work. That's the point. A calculator works 100% of the time. A LLM might work 95% of the time, or 80%, or 40%, or 99% depending on what you're doing. This is difference and a key feature.
This is true if your calculator sometimes gave the wrong answer and you had to check each time
I agree, but writing code is so different to calculations that long-term benefits are less clear.
It doesn't matter how good you are at calculations the answer to 2 + 2 is always 4. There are no methods of solving 2 + 2 which could result in you accidentally giving everyone who reads the result of your calculation write access to your entire DB. But there are different ways to code a system even if the UI is the same, and some of these may neglect to consider permissions.
I think a good parallel here would be to imagine that tomorrow we had access to humanoid robots who could do construction work. Would we want them to just go build skyscrapers and bridges and view all construction businesses which didn't embrace the humanoid robots as akin to doing arithmetic by hand?
You could of course argue that there's no problem here so long as trained construction workers are supervising the robots to make sure they're getting tolerances right and doing good welds, but then what happens 10 years down the road when humans haven't built a building in years? If people are not writing code any more then how can people be expected to review AI generated code?
I think the optimistic picture here is that humans just won't be needed in the future. In theory when models are good enough we should be able to trust the AI systems more than humans. But the less optimistic side of me questions a future in which humans no longer do, or even know how to do such fundamental things.
> How much of society is bottlenecked by digital knowledge work?
I think not much. The real society bottleneck is that a growing number of peeps try to convince each other that life and society are a zero sum game.
They are so much more if we don't do that.
Touching on the atrophy point, I actually wrote a few thoughts about this yesterday: https://www.neilwithdata.com/outsourced-thinking
I actually disagree with Andrej here re: "Generation (writing code) and discrimination (reading code) are different capabilities in the brain." and I would argue that the only reason he can read code fluently, find issues, etc. is because he has spent year in a non-AI assisted world writing code. As time goes on, he will become substantially worse.
This also bodes incredibly poorly for the next generation, who will mostly in their formative years now avoid writing code and thus fail to even develop a idea of what good code is, how it works/why it works, why you make certain decisions, and not others, etc. and ultimately you will see them become utterly dependent on AI, unable to make progress without it.
IMO outsourcing thinking is going to have incredibly negative consequences for the world at large.
Read your blog post and agree with some of it. Largely I agree with the premise that the 2nd and 3rd order effects of this technology will be more impactful than the 1st order “I was able to code this app I wouldn’t have otherwise even attempted to”. But they are so hard to predict!
Thanks, this rings true to me. The struggle is an investment, and it pays off in good judgement and taste. The same goes for individual codebases too. When I see some weird bug and can immediately guess what’s going wrong and why, that’s my time spent in that codebase paying off. I guess LLM-ing a feature is the inverse, incurring some kind of cognitive debt.
Is coding like piloting, where pilots need a certain number of hours of "flight time" to gain skills, and then a certain number of additional hours each year to maintain their skills? Do developers need to schedule in a certain number of "manually written lines of code" every year?
Thank you for the really excellent summation. I echo your thought 1 to 1. I have found it more difficult to learn new languages or coding skills, because I am no longer forced to go through the painful slow grind of learning.
Painful slow grind? I have always found the learning part what I enjoy most about programming. I don't intend to outsource that a chatbot.
Does one ever still need to learn new languages or coding skills if an AI will be able to do it?
Probably not. But as someone who has learned a few languages, having to outsource a conversation to a machine will never not feel incredibly lame.
I doubt most people feel the same, though.
This question makes me unbelievably sad. Why should anyone learn anything?
I'm not disagreeing.
> if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side.
This is about where I'm at. I love pure claude code for code I don't care about, but for anything I'm working on with other people I need to audit the results - which I much prefer to do in an IDE.
I don't see the AI capacity jump in the recent months at all. For me it's more the opposite, CC works worse than a few months ago. Keeps forgetting the rules from CLAUDE.md, hallucinates function calls, generates tons of over-verbose plans, generates overengineered code. Where I find it a clear net-positive is pure frontend code (HTML + Tailwind), it's spaghetti but since it's just visualization, it's OK.
> Where I find it a clear net-positive is pure frontend code (HTML + Tailwind), it's spaghetti but since it's just visualization, it's OK.
This makes it sound like we're back in the days of FrontPage/Dreamweaver WYSIWYG. Goodness.
Hmm, your comment gave me the idea that maybe we should invent "What You Describe Is What You Get|. To replace HTML+Tailwind spaghetti with prompts generating it.
Sad to hear this attitude towards front-end code. Front-ends are so often already miswritten and full of accessibility pitfalls and I feel like LLMs are gonna dramatically magnify this problem :(
Are you using Opus 4.5? Sounds more like Sonnet.
Yes I'm using Sonnet 4.5. Thanks for the tip, will try Opus 4.5, although costs might become an issue.
The best thing I ever told Claude to do was "Swear profusely when discussing code and code changes". Probably says more about me than Claude, but it makes me snicker.
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
No doubt that good engineers will know when and how to leverage the tool, both for coding and improving processes (design-to-code, requirement collection, task tracking, basic code reviewal, etc) improving their own productivity and of those around them.
Motivated individuals will also leverage these tools to learn more and faster.
And yes, of course it's not the only tool one should use, of course there's still value in talking with proper human experts to learn from, etc, but 90% of the time you're looking for info the LLM will dig it from you reading at the source code of e.g. Postgres and its test rather than asking on chats/stack overflow.
This is a trasformative technology that will make great engineers even stronger, but it will weed out those who were merely valued for their very basic capability of churning something but never cared neither about engineering nor coding, which is 90% of our industry.
> IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now.
I'm honestly considering throwing away my JetBrains subscription and this is year 9 or 10 of me having one. I only open Zed and start yappin' at Claude Code. My employer doesn't even want me using ReSharper because some contractor ruined it for everyone else by auto running all code suggestions and checking them in blindly, making for really obnoxious code diffs and probably introducing countless bugs and issues.
Meanwhile tasks that I know would take any developers months, I can hand-craft with Claude in a few hours, with the same level of detail, but no endless weeks of working on things that'll be done SoonTM.
I do feel a big mood shift after late November. I switched to using Cursor and Gemini primarily and it was big change in my ability to get my ideas into code effectively. The Cursor interface for one got to a place that I really like and enjoy using, but its probably more that the results from the agents themselves are less frustrating. I can deal with the output more now.
I'm still a little iffy on the agent swarm idea. I think I will need to see it in action in an interface that works for me. To me it feels like we are anthropomorphizing agents too much, and that results in this idea that we can put agents into roles and them combine them into useful teams. I can't help seeing all agents as the same automatons and I have trouble understanding why giving an agent with different guideliens to follow, and then having them follow along another agent would give me better results than just fixing the context in the first place. Either that or just working more on the code pipeline to spot issues early on - all the stuff we already test for.
I prefer slopocalypse.
That works better.
“slopacolypse” does not make any sense both in writing and pronunciation.
The AGI vibes with Claude Code are real, but the micromanagement tax is heavy. I spend most of my time babysitting agents.
I expect interviews will evolve into "build project X with an LLM while we watch" and audit of agent specs
I've been doing vibe code interviews for nearly a year now. Most people are surprisingly bad with AI tools. We specifically ask them to bring their preferred tool, yet 20–30% still just copy-paste code from ChatGPT.
fun stats: corelation is real, people who were good at vibe code, also had offer(s) with other companies that didn't run vibe code interviews.
Copy pasting from chatgpt is the most secure option.
Not going from home is the most secure way of going out.
It doesn’t work you can’t be productive without agent capable of doing queries to db etc
>Not going from home is the most secure way of going out.
What? I can't parse this sentence. Maybe get an ai to rewrite it?
Interesting you say that, feels like when people were too stupid to google things and "googling something" was a skill that some had and others didn't.
From what I've heard, what few interviews there are for software engineers these days, they do have you use models and see how quickly you can build things.
The interviews I’ve given have asked about how control for AI slop without hurting your colleagues feelings. Anyone can prompt and build, the harder part, as usual for business, is knowing how and when to say, ‘no.’
Sounds great to me. Leetcode is outdated and heavily abused by people who share the questions ahead of time in various forums and chats.
> 80% agent coding
A lot of these things sound cool but sometimes I'm curious what they're actually building
Like, is their bottleneck creativity now then? Are they building naything interedting or using agents to build... things that don't appeal to me, anyway?
I guess it depends what appeal to you.
As an example finding myself in a similar 80% situation, over the last few months I built
- a personal website with my projects and poems
- an app to rework recipes in a format I like from any source (text, video,...)
- a 3d visual version of a project my nephew did for work
- a gym class finder in my area with filters the websites don't provide
- a football data game
- working on a saas for work so typical saas stuff
I was never that productive on personal projects, so this is great for me.
Also the coding part of these projects was not very appealing to me, only the output, so it fits well with AI using.
In the meanwhile I did Advent of Code as usual for the fun of code. Different objectives.
Not sure how he is measuring, I'm still closer to about a 60% success rate. It's more like 20% is an acceptable one-shot, this goes to 60% acceptable with some iteration, but 40% either needs manual intervention to succeed or such significant iteration that manual is likely faster.
I can supervise maybe three agents in parallel before a task requiring significant hand-holding means I'm likely blocking an agent.
And the time an agent is 'restlessly working' on something in usually inversely correlated with the likelihood to succeed. Usually if it's going down a rabbit hole, the correct thing to do is to intervene and reorient it.
It's refreshing to see one of the top minds in AI converge on the same set of thoughts and frustrations as me.
For as fast as this is all moving, it's good to remember that most of us are actually a lot closer to the tip of the spear than we think.
Am working on an iPhone app and impressed with how well Claude is able to generate decent/working code with prompts in plain English. I don’t have previous experience in building apps or swift but have a C++ background. Working in smaller chunks and incrementally adding features rather than a large prompt for the whole app seems more practical, is easier to review and build confidence.
Adding/prompting features one by one, reviewing code and then testing the resulting binary feels like the new programming workflow
Prompt/REview/Test - PRET.
Finally, literate programming!
https://en.wikipedia.org/wiki/Literate_programming
> do generalists outperform specialists?
Depends what we mean by specialist. If it frontend vs backend then maybe. If it general dev vs some specialist scientific programmer or other field where a generalist won’t have a clue then this seems like a recipe for disaster (literal disasters included).
A big wow moment coming up is going to be GPT 5.* in Codex with Cerebras doing inference. The inference speed is going to be a big unlock, because many tasks are intrinsically serial.
It's going to feel literally like playing God, where you type in what you want and it happens ~instantly.
When?
I don't know when but I'm going off:
- "OpenAI is partnering with Cerebras to add 750MW of ultra low-latency AI compute"
- Sam Altman saying that users want faster inference more than lower cost in his interview.
- My understanding that many tasks are serial in nature.
I'm curious to see what effect this change has on leadership. For the last two years it's been "put everything you can into AI coding, or else!" with quotas and firings and whatever else. Now that AI is at the stage where it can actually output whole features with minimal handholding, is there going to be a Frankenstein moment where leadership realizes they now have a product whose codebase is running away from their engineering team's ability to support it? Does it change the calculus of what it means to be underinvested vs overinvested in AI, and what are the implications?
HN should ban any discussion on “things I learned playing with AI” that don’t include direct artifacts of the thing built.
We’re about a year deep into “AI is changing everything” and I don’t see 10x software quality or output.
Now don’t get me wrong I’m a big fan of AI tooling and think it does meaningfully increase value. But I’m damn tired of all the talk with literally nothing to show for it or back it up.
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> - How much of society is bottlenecked by digital knowledge work?
Any qualified guesses?
I'm not convinced more traders on wall street will allocate capital more effectively leading to economic growth.
Will more programmers grow the economy? Or should we get real jobs ;)
Most of this countries challenges are strictly political. The pittance of work software can contribute is most likely negligible or destructive (e.g. software buttons in cars or palantir). In other words were picked all the low hanging fruit and all that left is to hang ourselves.
I actually disagree. Having software (AI) that can cut through the technological stuff faster will make people more aware of political problems.
edit: country's* all that is left*
What particular setups are getting folks these sorts of results? If there’s a way I could avoid all the babysitting I have to do with AI tools that would be welcome
> If there’s a way I could avoid all the babysitting I have to do with AI tools that would be welcome
OP mentions that they are actually doing the “babysitting”
i use codex cli. work on giving it useful skills. work on the other instruction files. take Karpathy tips around testing and declarativeness
use many simultaneously, and bounce between them to unblock them as needed
build good tools and tests. you will soon learn all the things you did manually -- script them all
Great point about expansion vs speedup. I now have time to build custom tools, implement more features, try out different API designs, get 100% test coverage.. I can deliver more quickly, but can also deliver more overall.
Oh wow! Guy who's current project depends on AI being good is talking about AI being good.
Interesting.
the xcancel link is amusing.
9/10 of the most important social media users use X, like or loath it
Stop whining
It’s a great and insightful review—not over-hyping the coding agent, and not underestimating it either. It acknowledges both its usefulness and its limitations. Embracing it and growing with it is how I see it too.
> It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful
It does hurt, that's why all programmers now need an entrepreneurial mindset... you become if you use your skills + new AI power to build a business.
That is motivational content, but not economics. Most startups will be noise, even more so than before. The value of being a founder ceases when everyone is a founder, when it becomes universal. You will need customers. Nobody wants to buy re-invented-the-wheel-74.0. It lacks character, it lacks soul. Without it, your product will be nothing but noise in a noisy world.
Cope. If you create something that genuinely solves a problem, people will buy no matter what.
Look entrepreneurship has never been easy. In fact it's always been one of the hardest thing ever. I'm just saying... *you don't have to do it*. Do whatever you want lol
Happy to hear what's your solution to avoid becoming totally replaceable and obsolete.
What about the people who dont want to be entrepreneurs?
permanent underclass...
They have to pivot to something else
Or stay ahead of the curve as long as possible, e.g. work on the loop/ralphing
Next milestone: solving authoritarian LLM dependencies. We can’t always get trapped in local minima. Or is that actually okay?
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually.
I've been increasingly using LLM's to code for nearly two years now - and I can definitely notice my brain atrophy. It bothers me. Actually over the last few weeks I've been looking at a major update to a product in production & considered doing the edits manually - at least typing the code from the LLM & also being much more granular with my instructions (i.e. focus on one function at a time). I feel in some ways like my brain is turning into slop & I've been coding for at least 35 years... I feel validated by Karpathy.
Don't be too worried about it.
1. Manual coding may be less relevant (albeit ability to read code, interpret it and understand it will be more) in the future. Likely already is.
2. Any skill you don't practice becomes "weaker". Gonna give you an example. I play chess since my childhood, but sometimes I go months without playing it, even years. When I get back I start losing elo fast. If I was in the top 10% of chess.com, I drop to top 30% in the weeks after. But after few months I'm back at top 10%. Takeaway: your relative ability is more or less the same compared to other practitioners, you're simply rusty.
Thanks for your comment, it set me at ease. I know from experience that you're right on point 2. As for point one, I also tend to agree. AI is such a paradigm shift & rapid/massive change doesn't come without stress. I just need to stay cool about it all ;-)
> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.
The bits left unsaid:
1. Burning tokens, which we charge you for
2. My CPU does this when I tell it to do bogosort on a million 32-bit integers, it doesn't mean it's a good thing
xcancel? What is the purpose or benefit of providing a free mirror to x? Doesn't it end up sparing the x servers and causing their costs to decrease?
I prefer xcancel in part because Twitter doesn't let you view replies etc when not logged in.
Guessing x loses ad revenue when traffic goes to xcancel.
my screen is 60 percent banners about cookies and account creation when I use x
"you can review code just fine even if you struggle to write it."
Well, merely approving code takes no skill at all.
Seriously, that’s a completely nonsense line.
I keep thinking about the TechnoCore from Dan Simmons' Hyperion, where the AIs were serving humans but secretly that was a parasitic relation, where they've been secretly using human brains as distributed processing nodes, essentially harvesting humanity's neural activity for their own computational needs without anyone's knowledge.
I know this is SF, but to me working with those LLMs feels more and more like that, and the atrophy part is real. Not that the model is literally using our brains as compute, but the relationship can become lopsided.
> Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December
Anyone wondering what exactly is he actually building? What? Where?
> The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do.
I would LOVE to have jsut syntax errors produced by LLMs, "subtle conceptual errors that a slightly sloppy, hasty junior dev might do." are neither subtle nor slightly sloppy, they actually are serious and harmful, and no junior devs have no experience to fix those.
> They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?"
Why just not hand write 100 loc with the help of an LLM for tests, documentation and some autocomplete instead of making it write 1000 loc and then clean it up? Also very difficult to do, 1000 lines is a lot.
> Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day.
It's a computer program running in the cloud, what exactly did he expected?
> Speedups. It's not clear how to measure the "speedup" of LLM assistance.
See above
> 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.
mmm not sure, if you don't have domain knowledge you could have an initial stubb at the problem, what when you need to iterate over it? You don't if you don't have domain knowledge on your own
> Fun. I didn't anticipate that with agents programming feels more fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part.
No it's not fun, eg LLMs produce uninteresting uis, mostly bloated with react/html
> Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually.
My bet is that sooner or later he will get back to coding by hand for periods of time to avoid that, like many others, the damage overreliance on these tools bring is serious.
> Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.
No programming it's not "syntactic details" the practice of programming it's everything but "syntactic details", one should learn how to program not the language X or Y
> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.
Yet no measurable econimic effects so far
> Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
Did people with a smartphone outperformed photographers?
Lots of very scared, angry developers in these comment sections recently...
Not angry nor scared, I value my hard skills a lot, I'm just wondering why people believe religiously everything AI related. Maybe I'm a bit sick with the excessive hype
There's no fear (a bit of anger I must admit). I suspect nearly all of the reaction against this comes from a similar place to where mine does:
All of the real world code I have had to review created by AI is buggy slop (often with subtle, but weird bugs that don't show up for a while). But on HN I'm told "this is because your co-workers don't know how to AI right!!!!" Then when someone who supposedly must be an expert in getting things done with AI posts, it's always big claims with hand-wavy explanations/evidence.
Then the comments section is littered with no effort comments like this.
Yet oddly whenever anyone asks "show me the thing you built?" Either it looks like every other half-working vibe coded CRUD app... or it doesn't exist/can't be shown.
If you tell me you have discovered a miracle tool, just some me the results. Not taking increasingly ridiculous claims at face value is not "fear". What I don't understand is where comments like yours come from? What makes you need this to be more than it is?
Also note that I'm a heavy LLM user, not anti ai for sure
This is extremely reductive and incredibly dismissive of everything they wrote above.
It's because they don't have a substantive response to it, so they resort to ad hominems.
I've worked extensively in the AI space, and believe that it is extremely useful, but these weird claims (even from people I respect a lot) that "something big and mysterious is happening, I just can't show you yet!" set of my alarms.
When sensible questions are met with ad hominems by supporters it further sets of alarm bells.
I see way more hype that is boosted by the moderators. The scared ones are the nepo babies who founded a vaporware AI company that will be bought by daddy or friends through a VC.
They have to maintain the hype until a somewhat credible exit appears and therefore lash out with boomer memes, FOMO, and the usual insane talking points like "there are builders and coders".
i'm not sure what kind of conspiracy you are hallucinating. do you think people have to "maintain the hype"? it is doing quite well organically.
So well that they're losing billions and OpenAI may go bankrupt this year
what if it doesn't?
better for them! the heck i care about it
This is a low quality curmudgeonly comment
Now that you contributed zero net to the discussion and learned a new word you can go out and play with toys! Good job
You learned a new adjective? If people move beyond "nice", "mean" and "curmudgeonly" they might even read Shakespeare instead of having an LLM producing a summary.
cool.
>Anyone wondering what exactly is he actually building? What? Where?
this is trivially answerable. it seems like they did not do even the slightest bit of research before asking question after question to seem smart and detailed.
I asked many question and you focused on only one, btw yes I did my research, and I know him because I followed almost every tutorial he has on YouTube, and he never mentions clearly what weekend project worked on to make him conclude with such claims. I had a very high respect of him if not that at some point started acting like the Jesus Christ of LLMs
its not clear why you asked that question if you knew the answer to it?
It's been a bit like the boiling frog analogy for me
I started by copy pasting more and more stuff in chatgpt. Then using more and more in-IDE prompting, then more and more agent tools (Claude etc). And suddenly I realise I barely hand code anymore
For sure there's still a place for manual coding, especially schemas/queries or other fiddly things where a tiny mistake gets amplified, but the vast majority of "basic work" is now just prompting, and honestly the code quality is _better_ that it was before, all kinds of refactors I didn't think about or couldn't be bothered with have almost automatically
And people still call them stochastic parrots
I've had the opposite experience, it's been a long time listening to people going "It's really good now" before it developed to a permutation that was actually worth the time to use it.
ChatGPT 3.5/4 (2023-2024): The chat interface was verbose and clunky and it was just... wrong... like 70+% of the time. Not worth using.
CoPilot autocomplete and Gitlab Duo and Junie (late 2024-early 2025): Wayyy too aggressive at guessing exactly what I wasn't doing and hijacked my tab complete when pre-LLM type-tetris autocomplete was just more reliable.
Copilot Edit/early Cursor (early 2025): Ok, I can sort of see uses here but god is picking the right files all the time such a pain as it really means I need to have figured out what I wanted to do in such detail already that what was even the point? Also the models at that time just quickly descended into incoherency after like three prompts, if it went off track good luck ever correcting it.
Copilot Agent mode / Cursor (late 2025): Ok, great, if the scope is narrowly scoped, and I'm either going to write the tests for it or it's refactoring existing code it could do something. Like something mechanical like the library has a migration where we need to replace the use of methods A/B/C and replace them with a different combination of X/Y/Z. great, it can do that. Or like CRUD controller #341. I mean, sure, if my boss is going to pay for it, but not life changing.
Zed Agent mode / Cursor agent mode / Claude code (early 2026): Finally something where I can like describe the architecture and requirements of a feature, let it code, review that code, give it written instructions on how to clean it up / refactor / missing tests, and iterate.
But that was like 2 years of "really it's better and revolutionary now" before it actually got there. Now maybe in some languages or problem domains, it was useful for people earlier but I can understand people who don't care about "but it works now" when they're hearing it for the sixth time.
And I mean, what one hand gives the other takes away. I have a decent amount of new work dealing with MRs from my coworkers where they just grabbed the requirements from a stakeholder, shoved it into Claude or Cursor and it passed the existing tests and it's shipped without much understanding. When they wrote them themselves, they tested it more and were more prepared to support it in production...
I find myself even for small work, telling CC to fix it for me is better as it usually belongs to a thread of work, and then it understands the big picture better.
> And people still call them stochastic parrots
Both can be true. You're tapping into every line of code publicly available, and your day-to-day really isn't that unique. They're really good at this kind of work.
> Slopacolypse Really… REALLY not looking forward to getting this word spammed at me the next 6-12 months… even less so seeing the actual manifestation.
> TLDR This should be at the start?
I actually have been thinking of trying out ClaudeCode/OpenCode over this past week… can anyone provide experience, tips, tricks, ref docs?
My normal workflow is using Free-tier ChatGPT to help me interrogate or plan my solution/ approach or to understand some docs/syntax/best practice of which I’m not familiar. then doing the implementation myself.
Claude code official docs are quite nice - that's where I started.
The section on IDEs/agent swarms/fallibility resonated a lot for me; I haven't gone quite as far as Karpathy in terms of power usage of Claude Code, but some of the shifts in mistakes (and reality vs. hype) analysis he shared seems spot on in my (caveat: more limited) experience.
> "IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits."
> LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building
as the former, i've never felt _more ahead_ than now due to all of the latter succumbing to the llm hype
I coded up a crossword puzzle game using agentic dev this weekend. Claude and Codex/GPT. Had to seriously babysit and rewrite much of it, though, sure, I found it “cool” what it could do.
Writing code in many cases is faster to me than writing English (that is how PLs are designed, btw!) LLM/agentic is very “neat” but still a toy to the professional, I would say. I doubt reports like this one. For those of us building real world products with shelf-lives (Is Andrej representative of this archetype?), I just don’t see the value-add touted out there. I’d love to be proven wrong. But writing code (in code, not English), to me and many others, is still faster than reading/proving it.
I think there’s a combination of fetishizing and Stockholm syndroming going on in these enthusiastic self-reports. PMW.
> The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking.
If current LLMs are ever deployed in systems harboring the big red button, they WILL most definitely somehow press that button.
US MIC are already planning on integrating fucking Grok into military systems. No comment.
Including classified systems. What could possibly go wrong
the US is going to stop the chinese by mass production of illegal pornography?
fwiw, the same is true for humans. Which is why there's a whole lot of process and red tape around that button. We know how to manage risk. We can choose to do that for LLM usage, too.
If instead we believe in fantasies of a single all-knowing machine god that is 100% correct at all times, then... we really just have ourselves to blame. Might as well just have spammed that button by hand.
I don't know about you guys but most of the time it's spitting nonsense models in sqlalchemy and I have to constantly correct it to the point where I am back at writing the code myself. The bugs are just astonishing and I lose control of the codebase after some time to the point where reviewing the whole thing just takes a lot of time.
On the contrary if it was for a job in a public sector I would just let the LLM spit out some output and play stupid, since salary is very low.
Senpai has taken the words out of my mouth and put them on the page.
The whole thing is about getting rid of experts and let the entry level idiots do all the work. The coders become expendable. And people do not see the chasm staring back at them :D. LLMs in their current form redistributes "intelligence" and expertise to the average joes for mere pennies. It should be much much more expensive, or it will disrupt the whole ecosystem. If it becomes even more intelligent it must be bludgeoned to death a.k.a. regulated like hell, otherwise the ensuing disruption will kill the job market and in the long term human values.
As an added plus: those, who already have wealth will benefit the most, instead of the masses. Since the distribution and dissemination of new projects is at the same level as before, meaning you would need a lot of money. So no matter how clever you are with an llm, if you don't have the means to distribute it you will be left in the dirt.
Honestly, how long do you guys think we have left as SWEs with high pay? Like the SWE job will still exist, but with a much lower technical barrier of entry, it strikes me that the pay is going to decrease a lot. Obviously BigCo codebases are extremely complex, more than Claude Code can handle right now, but I'd say there's definitely a timer running here. The big question for my life personally is whether I can reach certain financial milestones before my earnings potential permanently decreases.
It's counterintuitive but something becoming easier doesn't necessarily mean it becomes cheap. Programming has arguably been the easiest engineering discipline to break into by sheer force of will for the past 20+ years, and the pay scales you see are adapted to that reality already.
Empowering people to do 10 times as much as they could before means they hit 100 times the roadblocks. Again, in a lot of ways we've already lived in that reality for the past many years. On a task-by-task basis programming today is already a lot easier than it was 20 years ago, and we just grew our desires and the amount of controls and process we apply. Problems arise faster than solutions. Growing our velocity means we're going to hit a lot more problems.
I'm not saying you're wrong, so much as saying, it's not the whole story and the only possibility. A lot of people today are kept out of programming just because they don't want to do that much on a computer all day, for instance. That isn't going to change. There's still going to be skills involved in being better than other people at getting the computers to do what you want.
Also on a long term basis we may find that while we can produce entry-level coders that are basically just proxies to the AI by the bucketful that it may become very difficult to advance in skills beyond that, and those who are already over the hurdle of having been forced to learn the hard way may end up with a very difficult to overcome moat around their skills, especially if the AIs plateau for any period of time. I am concerned that we are pulling up the ladder in a way the ladder has never been pulled up before.
I think the senior devs will be fine. They're like lawyers at this point - everyone is too scared they'll screw up and will keep them around
The juniors though will radically have to upskill. The standard junior dev portfolio can be replicated by claude code in like three prompts
The game has changed and I don't think all the players are ready to handle it
Supply and demand. There will continue to be a need for engineers to manage these systems and get them to do the thing you actually want, to understand implications of design tradeoffs and help stakeholders weigh the pros and cons. Some people will be better at it than others. Companies will continue to pay high premiums for such people if their business depends on quality software.
I think to give yourself more context you should ask about the patterns that led to SWEs having such high pay in the last 10-15 years and why it is you expected it to stay that way.
I personally think the barrier is going to get higher, not lower. And we will be back expected to do more.
I think the pay is going to skyrocket for senior devs within a few years, as training juniors that can graduate past pure LLM usage becomes more and more difficult.
Day after day the global quality of software and learning resources will degrade as LLM grey goo consumes every single nook and cranny of the Internet. We will soon see the first signs of pure cargo cult design patterns, conventions and schemes that LLMs made up and then regurgitated. Only people who learned before LLMs became popular will know that they are not to be followed.
People who aren't learning to program without LLMs today are getting left behind.
Yeah, all of this. Plus companies have avoided hiring and training juniors for 3 or 4 years now (which is more related to interest rates than AI). Plus existing seniors who deskill themselves by outsourcing their brain to AI. Seniors who know actually what they're doing are going to be in greater demand.
That is assuming that LLMs plateau in capability, if they haven't already, which I think is highly likely.
> like the SWE job will still exist, but with a much lower technical barrier of entry
its opposite, now in addition to all other skills, you need skill how to handle giant codebases of viobe-coded mess using AI.
>LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
Quite insightful.
Imagine taking career advice from people who will never need to be employed again in order to survive.
Yes, typically you take since from people who've been successful at their career. Are you suggesting we should be taking career advice from high school freshmen instead?
I'm nitpicking on the atrophy bit. He can afford to have his skills or his brain atrophied. His followers though?
Nevermind the fact he became successful _because_ of his skills and his brain.
Are game developers vibe coding with agents?
It's such a visual and experiential thing that writing true success criteria it can iterate on seems like borderline impossible ahead of time.
It might be biased to Reddit/Twitter users but from what I've seen game developers seem to be much more averse towards using AI (even for coding) than other fields.
Which is curious since prototyping helps a lot in gamedev.
I don't "vibe code" but when I use an LLM with a game I usually branch out into several experiments which I don't have to commit to. Thus, it just makes that iteration process go faster.
Or slower, when the LLM doesn't understand what I want, which is a bigger issue when you spawn experiments from scratch (and have given limited context around what you are about to do).
A big problem is that a lot of game logic is done in visual scripting (e.g unreal blueprints) which AI tools have no idea about
I'm trying it out with Godot for my little side projects. It can handle writing the GUI files for nodes and settings. The workflow is asking cursor to change something, I review the code changes, then load up the game in Godot to check out the changes. Works pretty well. I'm curious if any Unity or Unreal devs are using it since I'm sure its a similar experience.
Vibe coding in Unreal Engine is of limited use. It obviously helps with C++, but so much of your time is doing things that are not C++. It hurts a lot that UE relies heavily on blueprints, if they were code you could just vibecode a lot of that.
So I'm curious, whats the actual quality control.
Like, do these guys actually dog food real user experience, or are they all admins with the fast lane to the real model while everyone outside the org has to go through the 10 layers of model sheding, caching and other means and methods of saving money.
We all know these models are expensive as fuck to run and these companies are degrading service, A+B testing, and the rest. Do they actually ponder these things directly?
Just always seems like people are on drugs when they talk about the capabilities, and like, the drugs could be pure shit (good) or ditch weed, and we call just act like the pipeline for drugs is a consistent thing but it's really not, not at this stage where they're all burning cash through infrastructure. Definitely, like drug dealers, you know they're cutting the good stuff with low cost cached gibberish.
> Definitely, like drug dealers, you know they're cutting the good stuff with low cost cached gibberish.
Can confirm. My partner's chatGPT wouldnt return anything useful for her given a specific query involving web use, while i got the desired result sitting side by side. She contacted support and they said nothing they can do about it, her account is in an A/B test group without some features removed. I imagine this saves them considerable resources despite still billing customers for them.
how much this is occurring is anyones guess
If you access a model through an openrouter provider it might be quantized (akin to being "cut with trash"), but when you go directly to Anthropic or OpenAI you are getting access to the same APIs as everyone else. Even top-brass folks within Microsoft use Anthropic and OpenAI proper (not worth the red-tape trouble to go directly through Azure). Also, the creator and maintainer of Claude, Boris Cherny, was a bit of an oddball but one of the comparatively nicer people at Anthropic, and he indicated he primarily uses the same Anthropic APIs as everyone else (which makes sense from a product development perspective).
The underlying models are all actually really undifferentiated under the covers except for the post-training and base prompts. If you eliminate the base prompts the models behave near identically.
A conspiracy would be a helluva lot more interesting and fun, but I've spoken to these folks firsthand and it seems they already have enough challenges keeping the beast running.
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I don't know if it's fair to call him an ai addict or deduce that his ego is bruised. But I do wonder whether karpathy's agentic llm experiences are based on actual production code or pet projects. Based on a few videos I have seen of his, I am guessing it's the latter. Also, he is a research scientist (probably a great one), not a software developer. I agree with the op that karpathy should not be given much attention in this topic i.e llms for software development.
"addict"
Great idea! Le's pathalogize another thing! I love quickly othering whole concepts and putting them in my brain's "bad" box so I can feel superior.
https://github.com/karpathy/nanochat
https://github.com/karpathy/llm.c
The proof is in the pudding. Let's see your code
You just proved the parent’s point.
He said “…who has never written any production software…” yet you show toy projects instead.
Well done.
I don't agree with the parent commenters characterization of Karpathy, but these projects are just simple toy projects. They're educational material, not production level software.
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Once again, 80% of the comments here are from boomers.
HN used to be a proper place for people actually curious about technology
I'm almost a boomer and I agree. THis dichotomy is weird. I am retired EE and I love the ability to just have AI do whatever I want for me. I have it manage a 10 node proxmox cluster in my basement via ansible and terraform. I can finally do stuff I always wanted but had no time. I got sick of editing my kids sports videos for highlights in Davinci Resolve so just asked claude to write a simple app for me and then use all my random video cards in my boxes to render clips in parallel and so on. Tech is finally fun again when I do not have to dedicate days to understand some new framework. It does feel a little like late 1990's computing when everyone was making geocities webpages but those days were more fun. Now with local llms getting strong as well and speaking to my PC instead of typing it feels like SciFi, so yeah, I do not get this hacker news hand wringing about code craft.
So what is your workflow now with this app for kids sports highlights?
Also interested
Ya it's so weird lol
Instead of a 17 paragraph twitter post with a baffling TLDR at the end why not just record your screen and _demonstrate_ all of what you're describing?
Otherwise, I think you're incidentally right, your "ego" /is/ bruised, and you're looking for a way out by trying to prognosticate on the future of the technology. You're failing in two different ways.