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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

by daxfohl 9mo ago

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....

Imustaskforhelp 9mo ago
> 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?
dirtytoken7 9mo ago
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striking 9mo ago
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.
- daxfohl 9mo ago
  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?
  - throwup238 9mo ago
    How long until “the LLM did it it” is just as effective as “AWS is down, not my fault”?
    - deleted 9mo ago
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    - draxil 9mo ago
      This to me is the point.. LLMs can't be responsible for things. It sits with a human.
      - deleted 9mo ago
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      - taylorius 9mo ago
stuaxo 9mo ago
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.
gritspants 9mo ago
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.
- solumunus 9mo ago
  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.
  - rustyhancock 9mo ago
    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?
    - solumunus 9mo ago
      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...
freediver 9mo ago
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.
- swader999 9mo ago
  Same. I feel I need to be way more into the domain and what the user is trying to do than ever before.
- mlrtime 9mo ago
  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.
krupan 9mo ago
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
- neves 9mo ago
  It's exactly the argument here: https://www.fast.ai/posts/2026-01-28-dark-flow/ https://www.fast.ai/posts/2026-01-28-dark-flow/
nemothekid 9mo ago
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.
- deleted 9mo ago
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- mh2266 9mo ago
  > 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.
epolanski 9mo ago
> 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.
zamalek 9mo ago
> 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).
- bandrami 9mo ago
  > 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...
  - zamalek 9mo ago
    An ORM doesn't generate REST endpoints?
polytely 9mo ago
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.
CharlieDigital 9mo ago
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.
- finaard 9mo ago
  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.
- globular-toast 9mo ago
  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.
- acessoproibido 9mo ago
  As someone working in technical support for a long time, this has always been the case. You can have as many extremely detailed and easy to parse gudies, references, etc. there will always be a portion of customers who refuse to read them. Never could figure out why because they aren't stupid or anything.
sosomoxie 9mo ago
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.
- runarberg 9mo ago
  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.
  - tayo42 9mo ago
    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.
    - Ronsenshi 9mo ago
      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.
seer 9mo ago
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.
- Ronsenshi 9mo ago
  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.
  - mlrtime 9mo ago
    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.
  - dysoco 9mo ago
    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.
overfeed 9mo ago
> 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.
- scorpioxy 9mo ago
  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.
  - sally_glance 9mo ago
    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.
    - scorpioxy 9mo ago
      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.
amluto 9mo ago
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.
- mlrtime 9mo ago
  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!
mupuff1234 9mo ago
He didn't say "brain atrophy", he was talking about coding abilities.
InfinityByTen 9mo ago
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.
abm53 9mo ago
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.
nathias 9mo ago
it's not about brain atrophy, it's skill atrophy
- direwolf20 9mo ago
  is that not the sam thing?
alansaber 9mo ago
"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.
- phrotoma 9mo ago
  "rip it out" is a phrase I've been saying more often to the robots.
SpaceL10n 9mo ago
LLMs are yet another layer between us and the end result. I remain wary of this distance and am super grateful I learned coding the hard way.
kitd 9mo ago
I think this is where tools like OpenSpec [1] may help. The deterioration in quality is because the context is degrading, often due to incomplete or amibiguous requests from the coder. With a more disciplined way of creating and persisting locally the specs for the work, especially if the agent got involved in creating that too, you'll have a much better chance of keeping the agent focussed and aligned. [1] - https://openspec.dev/ https://openspec.dev/
aswegs8 9mo ago
"For this invention will produce forgetfulness in the minds of those who learn to use it, because they will not practice their memory. Their trust in writing, produced by external characters which are no part of themselves, will discourage the use of their own memory within them. You have invented an elixir not of memory, but of reminding; and you offer your pupils the appearance of wisdom, not true wisdom, for they will read many things without instruction and will therefore seem [275b] to know many things, when they are for the most part ignorant and hard to get along with, since they are not wise, but only appear wise." - Socrates on Writing and Reading, Phaedrus 370 BC
- throw10920 9mo ago
  Writing/reading and AI are so categorically different that the only way you could compare them is if you fundamentally misunderstand how both of them work. And "other people in the past predicted doom about something like this and it didn't happen" is a fallacious non-argument even when the things are comparable.
  - wjSgoWPm5bWAhXB 9mo ago
    yes, but people just really like to predict dooms and they also like to be convinced that they live in some special era in human history
    - jatari 9mo ago
      We are very clearly living through a moment in history that will be studied intensely for thousands of years.
      - direwolf20 9mo ago
        Because of the collapsing empire, mind you, not because of the LLMs.
        jatari 9mo ago
chickensong 9mo ago
> AI keeps pushing it in a direction I didn't want The AI definitely has preferences and attention issues, but there are ways to overcome them. Defining code styles in a design doc, and setting up initial examples in key files goes a long way. Claude seems pretty happy to follow existing patterns under these conditions unless context is strained. I have pretty good results using a structured workflow that runs a core loop of steps on each change, with a hook that injects instructions to keep attention focused.
lighthouse1212 9mo ago
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SenHeng 9mo ago
Another thing I’ve experienced is scope creep into the average. Both Claude and ChatGPT keep making recommendations and suggestions that turns the original request into something that resembles other typical features. Sometimes that’s a good thing, because it means I’ve missed something. A lot of times, especially when I’m just riffing on ideas, it turns into something mundane and ordinary and I’ll have lost my earlier train of thought. A quick example is trying to build a simple expenses app with it. I just want to store a list of transactions with it. I’ve already written the types and data model and just need the AI to give me the plumping. And it will always end up inserting recommendations about double entry bookkeeping.
- fragmede 9mo ago
  yeah but that's like recommending a webserver for your Internet facing website. If you want to give an example of scope creep, you need a better example than double entry book keeping for an accounting app.
  - SenHeng 9mo ago
    You’ve just illustrated exactly the problem. You assumed I was building an accounting app. I’ve experienced the same issue with building features for calculating the brightness of a room, or 3D visualisations of brightness patterns, managing inventory and cataloguing lighting fixtures and so on. It’s great for churning out stuff that already exists, but that also means it’ll massage your idea into one of them.
keeganpoppen 9mo ago
yeah, because the thing is: at the end of the day: laying things out the way LLMs can understand is becoming more important than doing them the “right” way— a more insidious form of the same complacency. and one in which i am absolutely complicit.
dkubb 9mo ago
You could probably combat this somewhat with a skill that references to examples of the code you don't want and the code you do. And then each time you tell it to correct the code you ask it to put that example into the references. You then tell your agent to always run that skill prior to moving on. If the examples are pattern matchable you can even have the agent write custom lints if your linter supports extension or even write a poor man’s linter using ast-grep. I usually have a second session running that is mainly there to audit the code and help me add and adjust skills while I keep the main session on the task of working on the feature. I've found this far easier to stay engaged than context switching between unrelated tasks.
ekropotin 9mo ago
The solution for brain atrophy I personally arrived is to use coding agents at work, where, let’s be honest, velocity is a top priority and code purity doesn’t matter that much. Since we use stack I super familial with, I can quite fast verify produced code and tweak it if needed. However, for hobby projects where I purposely use tech I’m not very familiar with, I force myself not to use LLMs at all - even as a chat. Thus, operating The old way - writing code manually, reading documentation, etc brings me a joy of learning back and, hopefully, establishes new neurone connections.
nonethewiser 9mo ago
> 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. Absolutely. At a certain level of usage, you just have to let it do it's thing. People are going to take issue with that. You absolutely don't have to let it do its thing. In that case you have to be way more in the loop. Which isn't necessarily a bad thing. But assuming you want it to basically do everything while you direct it, it becomes pointless to manage certain details. One thing in my experience is that Claude always wants to use ReactRouter. My personal preference is TanStack router, so I asked it to use it initially. That never really created any problems but after like the 3rd time of realizing I forget to specify it, I also realized that it's totally pointless. ReactRouter works fine and Claude uses it fine - its pointless to specify otherwise.

by daxfohl 9mo ago

Imustaskforhelp 9mo ago

> 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?

dirtytoken7 9mo ago

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striking 9mo ago

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.

daxfohl 9mo ago
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?
- throwup238 9mo ago
  How long until “the LLM did it it” is just as effective as “AWS is down, not my fault”?
  - deleted 9mo ago
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  - draxil 9mo ago
    This to me is the point.. LLMs can't be responsible for things. It sits with a human.
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    - taylorius 9mo ago

stuaxo 9mo ago

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.

gritspants 9mo ago

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.

solumunus 9mo ago
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.
- rustyhancock 9mo ago
  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?
  - solumunus 9mo ago
    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...

freediver 9mo ago

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.

swader999 9mo ago
Same. I feel I need to be way more into the domain and what the user is trying to do than ever before.
mlrtime 9mo ago
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.

krupan 9mo ago

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

neves 9mo ago
It's exactly the argument here: https://www.fast.ai/posts/2026-01-28-dark-flow/ https://www.fast.ai/posts/2026-01-28-dark-flow/

nemothekid 9mo ago

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.

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mh2266 9mo ago
> 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.

epolanski 9mo ago

> 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.

zamalek 9mo ago

> 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).

bandrami 9mo ago
> 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...
- zamalek 9mo ago
  An ORM doesn't generate REST endpoints?

polytely 9mo ago

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.

CharlieDigital 9mo ago

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.

finaard 9mo ago
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.
globular-toast 9mo ago
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.
acessoproibido 9mo ago
As someone working in technical support for a long time, this has always been the case. You can have as many extremely detailed and easy to parse gudies, references, etc. there will always be a portion of customers who refuse to read them. Never could figure out why because they aren't stupid or anything.

sosomoxie 9mo ago

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.

runarberg 9mo ago
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.
- tayo42 9mo ago
  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.
  - Ronsenshi 9mo ago
    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.

seer 9mo ago

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.

Ronsenshi 9mo ago
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.
- mlrtime 9mo ago
  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.
- dysoco 9mo ago
  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.

overfeed 9mo ago

> 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.

scorpioxy 9mo ago
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.
- sally_glance 9mo ago
  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.
  - scorpioxy 9mo ago
    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.

amluto 9mo ago

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.

mlrtime 9mo ago
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!

mupuff1234 9mo ago

He didn't say "brain atrophy", he was talking about coding abilities.

InfinityByTen 9mo ago

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.

abm53 9mo ago

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.

nathias 9mo ago

it's not about brain atrophy, it's skill atrophy

direwolf20 9mo ago
is that not the sam thing?

alansaber 9mo ago

"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.

phrotoma 9mo ago
"rip it out" is a phrase I've been saying more often to the robots.

SpaceL10n 9mo ago

LLMs are yet another layer between us and the end result. I remain wary of this distance and am super grateful I learned coding the hard way.

kitd 9mo ago

I think this is where tools like OpenSpec [1] may help. The deterioration in quality is because the context is degrading, often due to incomplete or amibiguous requests from the coder. With a more disciplined way of creating and persisting locally the specs for the work, especially if the agent got involved in creating that too, you'll have a much better chance of keeping the agent focussed and aligned. [1] - https://openspec.dev/ https://openspec.dev/

aswegs8 9mo ago

"For this invention will produce forgetfulness in the minds of those who learn to use it, because they will not practice their memory. Their trust in writing, produced by external characters which are no part of themselves, will discourage the use of their own memory within them. You have invented an elixir not of memory, but of reminding; and you offer your pupils the appearance of wisdom, not true wisdom, for they will read many things without instruction and will therefore seem [275b] to know many things, when they are for the most part ignorant and hard to get along with, since they are not wise, but only appear wise." - Socrates on Writing and Reading, Phaedrus 370 BC

throw10920 9mo ago
Writing/reading and AI are so categorically different that the only way you could compare them is if you fundamentally misunderstand how both of them work. And "other people in the past predicted doom about something like this and it didn't happen" is a fallacious non-argument even when the things are comparable.
- wjSgoWPm5bWAhXB 9mo ago
  yes, but people just really like to predict dooms and they also like to be convinced that they live in some special era in human history
  - jatari 9mo ago
    We are very clearly living through a moment in history that will be studied intensely for thousands of years.
    - direwolf20 9mo ago
      Because of the collapsing empire, mind you, not because of the LLMs.
      - jatari 9mo ago

chickensong 9mo ago

> AI keeps pushing it in a direction I didn't want The AI definitely has preferences and attention issues, but there are ways to overcome them. Defining code styles in a design doc, and setting up initial examples in key files goes a long way. Claude seems pretty happy to follow existing patterns under these conditions unless context is strained. I have pretty good results using a structured workflow that runs a core loop of steps on each change, with a hook that injects instructions to keep attention focused.

lighthouse1212 9mo ago

[dead]

SenHeng 9mo ago

Another thing I’ve experienced is scope creep into the average. Both Claude and ChatGPT keep making recommendations and suggestions that turns the original request into something that resembles other typical features. Sometimes that’s a good thing, because it means I’ve missed something. A lot of times, especially when I’m just riffing on ideas, it turns into something mundane and ordinary and I’ll have lost my earlier train of thought. A quick example is trying to build a simple expenses app with it. I just want to store a list of transactions with it. I’ve already written the types and data model and just need the AI to give me the plumping. And it will always end up inserting recommendations about double entry bookkeeping.

fragmede 9mo ago
yeah but that's like recommending a webserver for your Internet facing website. If you want to give an example of scope creep, you need a better example than double entry book keeping for an accounting app.
- SenHeng 9mo ago
  You’ve just illustrated exactly the problem. You assumed I was building an accounting app. I’ve experienced the same issue with building features for calculating the brightness of a room, or 3D visualisations of brightness patterns, managing inventory and cataloguing lighting fixtures and so on. It’s great for churning out stuff that already exists, but that also means it’ll massage your idea into one of them.

keeganpoppen 9mo ago

yeah, because the thing is: at the end of the day: laying things out the way LLMs can understand is becoming more important than doing them the “right” way— a more insidious form of the same complacency. and one in which i am absolutely complicit.

dkubb 9mo ago

You could probably combat this somewhat with a skill that references to examples of the code you don't want and the code you do. And then each time you tell it to correct the code you ask it to put that example into the references. You then tell your agent to always run that skill prior to moving on. If the examples are pattern matchable you can even have the agent write custom lints if your linter supports extension or even write a poor man’s linter using ast-grep. I usually have a second session running that is mainly there to audit the code and help me add and adjust skills while I keep the main session on the task of working on the feature. I've found this far easier to stay engaged than context switching between unrelated tasks.

ekropotin 9mo ago

The solution for brain atrophy I personally arrived is to use coding agents at work, where, let’s be honest, velocity is a top priority and code purity doesn’t matter that much. Since we use stack I super familial with, I can quite fast verify produced code and tweak it if needed. However, for hobby projects where I purposely use tech I’m not very familiar with, I force myself not to use LLMs at all - even as a chat. Thus, operating The old way - writing code manually, reading documentation, etc brings me a joy of learning back and, hopefully, establishes new neurone connections.

nonethewiser 9mo ago

> 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. Absolutely. At a certain level of usage, you just have to let it do it's thing. People are going to take issue with that. You absolutely don't have to let it do its thing. In that case you have to be way more in the loop. Which isn't necessarily a bad thing. But assuming you want it to basically do everything while you direct it, it becomes pointless to manage certain details. One thing in my experience is that Claude always wants to use ReactRouter. My personal preference is TanStack router, so I asked it to use it initially. That never really created any problems but after like the 3rd time of realizing I forget to specify it, I also realized that it's totally pointless. ReactRouter works fine and Claude uses it fine - its pointless to specify otherwise.