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AI coding

• sothatsit 1y ago
  I really hate the comparison of AI to a compiler. It's not like I'm writing a codebase in English. I'm still writing codebases in programming languages. AI just helps manage all the text.
• p2detar 1y ago
  On a sidetrack, I was looking at the email address in the footer and wondered how cool it would have looked if geohot actually had a Hotmail address.
• coolThingsFirst 1y ago
  George, you post this after everyone already realized that LLMs plateaued. Before that you were crying about the singularity and how we're super close it and in 6 months AI will be basically Einstein. Nothing has given me more joy than to see this AI slop fail. Here's a 100 page document generated with AI and the other person will shrink it with AI at the end of the day those 2 lines of cognitive effort would've been a better deal than sending slop back and forth.
• ChrisMarshallNY 1y ago
  > AI makes you feel 20% more productive but in reality makes you 19% slower. How many more billions are we going to waste on this? Adderall is similar. It makes people feel a lot more productive, but research on its effectiveness[0] seems to show that, at best, we get only a mild improvement in productivity, and marked deterioration of cognitive abilities. [0] https://pmc.ncbi.nlm.nih.gov/articles/PMC6165228/ https://pmc.ncbi.nlm.nih.gov/articles/PMC6165228/
  • luckylion 1y ago
    Research on _13_ people, that's a very important caveat when evaluating something like adderal.
    • ChrisMarshallNY 1y ago
      I’m quite sure that there’s a ton more research on it. The drug’s been around for decades. Lots of time for plenty of studies. If legitimate research had found it to be drastically better, that study would definitely have been published in a big way. Unscientifically, I personally know quite a number of folks that sincerely believed that they couldn’t function without it, but have since learned that they do far better on their own. I haven’t met a single one that actually had their productivity decline (after an adjustment period, of course), after giving up Adderall. In fact, I know several, that have had their careers really take off, after giving it up.
      • luckylion 1y ago
        My point is that micro-studies like that on a tiny random (or even counter-indicated, "healthy") selection of the general population don't tell you much for drugs that do specific things. "Antibiotics don't improve your life, but can damage your health" would likely be the outcome on 13 randomly selected healthy individuals. But do the same study on 13 people with a bacterial infection susceptible to antibiotics and your results will be vastly different.
        • ChrisMarshallNY 1y ago
          I don't think that it matters, in this context, as a lot of folks here, have their minds made up, already, and won't let anything interfere. They'll need to learn, the same way I see lots of people learn. It's been around long enough, though, that all the Adderall-eating people should have established a Gattaca-like "elite," with all us "undermen," scrabbling around at their feet. Not sure why that never happened...
          • medvezhenok 1y ago
            As someone with diagnosed ADHD, and prescribed something similar: It's not that they make you superhuman - I don't think I'm any "smarter" on them. It's just that without them, my "productive" bursts would be at really random times (11PM or 2AM), and make it very hard to fit into a "normal" schedule with the rest of society (I would frequently fall asleep in classes or meetings). So it's more that it allows you to "rearrange" the same (or slightly larger) amount of work but into a more reasonable, traditional schedule. And for certain things, helps more than that. But it's not a miracle drug by any means.
  • diarrhea 1y ago
    Note that the study is just n=13 and on subjects without ADHD.
    • ChrisMarshallNY 1y ago
      That’s the deal. People without ADHD take it, believing that it makes them “super[wo]men.”
      • bdcravens 1y ago
        I had a problem client that I ended up firing and giving money back to about 15 years ago. Lot of red flags, but the breaking point was when they offered me adderall so I could "work faster". That said, I'll leave the conclusions about whether it's valuable for those with ADHD to the mental health professionals.
    • gobdovan 1y ago
      Thanks again, diarrhea
  • joefourier 1y ago
    I’m someone with ADHD who takes prescribed stimulants and they don’t make me work faster or smarter, they just make me work. Without them I’ll languish in an unfocused haze for hours, or zone in on irrelevant details until I realise I have an hour left in the day to get anything done. It could make me 20% less intelligent and it would still be worth it; this is obviously an extreme, but given the choice, I’d rather be an average developer that gets boring, functional code done on time than a dysfunctional genius who keeps missing deadlines and cannot be motivated to work on anything but the most exciting shiny new tech.
    • ChrisMarshallNY 1y ago
      I have family that had ADHD, as a kid (they called it “hyperactivity,” back then). He is also dyslexic. The ADHD was caught early, and treated, but the dyslexia was not. He thought he was a moron, for much of his early life, and his peers and employers did nothing to discourage that self-diagnosis. Since he learned of his dyslexia, and started treating it, he has been an engineer at Intel, for most of his career (not that I envy him, right now).
  • hereme888 1y ago
    You have to realize that ADD meds are meant only for people with ADD, not healthy people at the prime of their life. Excess neurochemicals can have the opposite effect. Their benefits when used as intended are solidly documented in research literature.
  • leptons 1y ago
    I suppose you've never used Adderall during coding sessions before (and assuming you don't have a prescription for ADHD).
    • ChrisMarshallNY 1y ago
      I used it when it was called Ritalin. It interacts with alcohol, too. Interesting stuff (not exactly the same stuff, but damn similar). A lot of it. Other stuff, too, that makes it look like a cup of weak tea, but it’s been 45 years. I’m pretty familiar with the illusion of productivity.
      • leptons 1y ago
        Adderall is not Ritalin. They may both be stimulants, but they are different. They have different active ingredients, mechanisms of action, durations of action. Not really that similar in my experience. Ritalin is kind of weak, honestly. >I’m pretty familiar with the illusion of productivity II do not have ADHD and I'm familiar with successfully using Aderrall for actual productivity. YMMV. Turns out not everyone's brain chemistry is the same.
        • ChrisMarshallNY 1y ago
          Eh. Not worth arguing over. I know the drill. I just make sure there's a place to go, if the wheels fall off. Cheers.
• Eikon 1y ago
  Even though I don’t buy that LLMs are going to replace developers and quite agree with what is said, this is more of a critique of LLMs as English-to-code translators. LLMs are very useful for many other things. Researching concepts, for one, has become so much easier, especially for things where you don’t know anything yet and would have a hard time to even formulate a search engine query.
  • ChrisMarshallNY 1y ago
    I’ve found that ChatGPT and Perplexity are great tools for finding “that article I skimmed a year ago that talked about…”.
  • fleebee 1y ago
    I agree. I think a better analogy than a compiler is a search engine that has an excellent grasp of semantics but is also drunk and schizophrenic. LLMs are really valuable for finding information that you aren't able to formulate a proper search query for. To get the most out of them, ask them to point you to reliable sources instead of explaining directly. Even then, it pays to be very critical of where they're leading you to. To make an LLM the primary window through which you seek new information is extremely precarious epistemologically. Personally, I'd use it as a last resort.
• urbandw311er 1y ago
  That was a really great read. Not saying I agree with it all, I’m maybe more in the camp that believes AI assisted coding is a time-saver but it’s refreshing (and overdue) to have a counterpoint to the deafening and repetitive drumbeat of the VC-backed hype machine.
• m00dy 1y ago
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  • deleted 1y ago
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• martini333 1y ago
  I agree. I use LLM to do things like brainstorm, explaining programming concepts and debug. I will not use it to write code. The output is not good enough, and I feel dumber. I only see the worst of my programming collegues coding with AI. And the results are actual trash. They have no actual understanding of the code "they" are writing, and they have no idea how to actually debug what "they" made, if LLM is not helpful. I can smell the technical debt.
  • pydry 1y ago
    Me too. I used to be a bit more open minded on this topic but im increasingly viewing any programmers who use AI for anything other than brainstorming and looking stuff up/explaining it as simply bad at what they do.
  • KronisLV 1y ago
    Is this fundamentally different from them copy pasting code from StackOverflow or random blog posts, without understanding it? You know, aside from AI making it super easy and fast to generate this tech debt in whatever amounts they desire?
    • maplethorpe 1y ago
      Even when copy-pasting an entire function from stack overflow, you generally still need to have some understanding of what the inputs and outputs are, even if it remains somewhat of a black box, so that you can plug it into your existing code. AI removes that need. You don't need to know what the function does at all, so your brain devotes no energy towards remembering or understanding it.
• viraptor 1y ago
  There's a lot of complaining about current compilers / languages / codebase in similar posts, but barely any ideas for how to make them better. It doesn't seem surprising that people go for the easier problem (make the current process simpler with LLMs) than for the harder one (change the whole programming landscape to something new and actually make it better).
• Earw0rm 1y ago
  How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities? Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off. (And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
  • cycomanic 1y ago
    > How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities? Because geo fenced driving in a few select cities with very favourable conditions is not what was promised. That's the crux. They promised us that we have self drive anywhere at anytime at the press of a button. > Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off. I'm not sure how you get to 6 figures revenue. Assuming the car makes $100 per hour for 24x7 52 weeks a year we still fall short of 1 million. But let's assume you're right $300M revenue (not profit, are they even operating at a plus even disregarding R&D costs?) on investment of >10 billion (probably more like 100), seems like the definition of hype. > (And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there). What I don't understand with this argument, how are you proposing they become the dominant mode of transport. These services are competing with taxis, what do they offer over taxis that people suddenly switch on mass to self driving taxis? They need to become cost competitive (and convenience competitive) with driving your own car, which would significantly drive down revenue. Secondly if robotaxi companies take over transport, why would the public continue to build their infrastructure and not demand that these robotaxi companies start to finance the infrastructure they exclusively use?
    • Earw0rm 1y ago
      I got to six figures by assuming that a human taxi driver makes maybe $30-40k at a guess, and an autonomous car can work 24/7. 6 figures is $100k minimum. So yeah, right now they'd have to be at ten cities x 300 cars each to hit 300M revenue, but there's still plenty of room for growth. Or should be, assuming the Waymo model isn't maxed out supporting the current handful of cities. But I'm not convinced they have to hit cost parity with personal cars, because the huge advantage is you can work and drive (or be driven). If NYC and LA rush-hour congestion time becomes productive time, there's your billions. I drive but prefer to take transit for this reason - some of my colleagues are able to join work calls effectively while driving, but for whatever reason my brain doesn't allow that. Just paying attention to calls is enough, you want me to pay attention to the road AND the call?
• ur-whale 1y ago
  I agree that most natural languages are a very poor tool to write code specification in. Specifically, natural language is: - ambiguous (LLMs solve this to a certain extent) - extremely verbose - doesn't lend itself well to refactoring - the same thing can be expressed in way too many different ways, which leads to instability in specs -> code -> specs -> code -> specs loops (and these are essential to do incremental work) Having something at our disposal that you can write code specs in, that is as easy as natural language yet, more concise, easy to learn and most of all not so anal/rigid as typical code languages are would be fantastic. Maybe LLMs can be sued to design such a thing ?
  • Agraillo 1y ago
    > Maybe LLMs can be sued to design such a thing nice misspelling (or a joke?), related to all the lawsuits around LLMs. Joking aside, it’s already there in a sense. Several times I started with a brief outline of what the prototype should do (an HTML/CSS/JS app), and sure enough, refinements and corrections followed. When the final version worked more or less as expected, I asked the LLM to create a specification (a reproducing prompt) of everything we made together. Even if the vibe-coded prototype is dropped, the time wasn’t wasted, I probably would never have come to the same bullet list specification without having an actual working app at my disposal to test and evaluate. So paradoxically this specification even might be used by a human later
• saejox 1y ago
  > AI makes you feel 20% more productive but in reality makes you 19% slower. How many more billions are we going to waste on this? True in the long run. Like a car with a high acceleration but low top speed. AI makes you start fast, but regret later because you don't have the top speed.
  • net01 1y ago
    This is shown in figure 5 of the paper. https://arxiv.org/pdf/2507.09089 https://arxiv.org/pdf/2507.09089
  • isaacremuant 1y ago
    People repeating articles or papers. I know myself. I know from my own experiences what the good and bad of practice A or database B is. I don't need to read a conclusion by some Muppet. Chill. Interesting times. Learn stuff, like always. Iterate. Be mindful and intentional and don't just chase mirrors but be practical. The rest is fluff. You know yourself.
    • roxolotl 1y ago
      The takeaway from the paper is you don’t know yourself. It’s one paper, and a small sample size, but attempting to refute its conclusion by stating it’s false doesn’t really get us anywhere.
      • isaacremuant 1y ago
        Lol. Maybe you need enough experience to understand if you think "it's written in a paper" is proof of anything, specially over the experience of people who do actual work and have been profilic over long periods.
• demirbey05 1y ago
  I started fully coding with Claude Code. It's not just vibe coding, but rather AI-assisted coding. I've noticed there's a considerable decrease in my understanding of the whole codebase, even though I'm the only one who has been coding this codebase for 2 years. I'm struggling to answer my colleagues' questions. I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.
  • numbers_guy 1y ago
    This is the chief reason I don't use integrations. I just use chat, because I want to physically understand and insert code myself. Else you end up with the code overtaking your understanding of it.
    • pmg101 1y ago
      Yes. I'm happy to have a sometimes-wrong expert to hand. Sometimes it provides just what I need, sometimes like with a human (who are also fallible), it helps to spur my own thinking along, clarify, converge on a solution, think laterally, or other productivity boosting effects.
  • apercu 1y ago
    I wrote a couple python scripts this week to help me with a midi integration project (3 devices with different cable types) and for quick debugging if something fails (yes, I know there are tools out there that do this but I like learning). I’m could have used an LLM to assist but then I wouldn’t have learned much. But I did use an LLM to make a management wrapper to present a menu of options (cli right now) and call the scripts. That probably saved me an hour, easily. That’s my comfort level for anything even remotely “complicated”.
  • ionwake 1y ago
    I keep wanting to go back to using claudecode but I get worried about this issue. How best to use it to complement you, without it rewriting everything behidn the scenes? whats the best protocol? constnat commit requests and reviews?
• ur-whale 1y ago
  I do agree with many points in the article, but not about the last part, namely that coding with AI assist makes you slower. Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster. I confess to not read the code produced at each iteration other than skimming through it for obvious architectural code smell, but I do read the final version line by line and make a few changes until I'm happy. Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
  • skydhash 1y ago
    > Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster. > Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation. One of these is not true. With libraries, it's either you HAVE to use it, so you spend time being acquainted with it (usually a couple hours to make sense of its design, the rest will come on a needed basis) or you are evaluating multiple ones (and that task is much quicker).
    • ur-whale 1y ago
      > you are evaluating multiple ones (and that task is much quicker). Of course the latter. And of course I ask the AI to help me select a libray/module/project/whatever that provides what I need. And I ask the AI to classify them by popularity/robustness. And then I apply whatever little/much I know about the space to refine the choice. may go as far as looking at examples that use the API. And maybe rummage through the code being the API to see if I like what I see. The whole thing is altogether still way faster than having to pick what I need by hand with my rather limited data ingestion capabilities. And then, once I've picked one, connecting to the API's is a no-brainer with an LLM, goes super fast. Altogether major time saved.
• abdibrokhim 1y ago
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• bdcravens 1y ago
  I'm almost 50, and have been writing code professionally since the late 90s. I can pretty much see projects in my head, and know exactly what to build. I also get paid pretty well for what I do. You'd think I'd be the prototype for anti-AI. I'm not. I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts. I liken AI development to a developer somewhere between junior and mid-level, someone I can given a paragraph or two of thought out instructions and have them bang out an hour of work. (The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern, but a separate problem to solve)
  • osigurdson 1y ago
    >> developer somewhere between junior and mid-level, Analogies to humans don't work that well. AI is super-human in some respects while also lacking the ability to continually work toward a goal over long periods of time. AI can do very little on its own - just short / scoped / supervised tasks. However, sometimes the situation is reversed, AI is the teacher who provides some examples on how to do things or provides hints on how to explore a new area and knows how others have approached similar things. Then, sometimes, AI is an astute code reviewer, typically providing valuable feedback. Anyway, I've stopped trying anthropomorphize AI and simply try to reason about it based on working with it. That means combinations of direct ChatGPT usage with copy / paste / amend type workflows, async style / full PR style usage, one-shot "hail Mary" type throw away PRs just to establish an initial direction as well as PR reviews of my own code. I'm using AI all the time, but never anything like how I would work with another human.
  • haute_cuisine 1y ago
    Would love to see a project you built with the help of AI, can you share any links?
    • bdcravens 1y ago
      Most of my work is for my employer, but the bigger point is that you wouldn't be able to tell my "AI work" from my other work because I primarily use it for the boring stuff that is labor-intensive, while I work on the actual business cases. (Most of my work doesn't fall under the category of "web application", but rather, backend and background-processing intensive work that just happens to have an HTML front-end)
• raincole 1y ago
  > It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. Obviously... in what way? I feel the anti-ai pattern is clear. Self-driving cars don't work in my city so the whole concept is a hoax. LLMs don't code my proprietary language so it's a bubble. > From this study (https://arxiv.org/abs/2507.09089 https://arxiv.org/abs/2507.09089) I can tell this is going to be the most misquoted study in blogs and pop-sci books after the 10,000-hour mastery study. And it's just a preprint!
• vmg12 1y ago
  I think this gets to a fundamental problem with the way the AI labs have been selling and hyping AI. People keep on saying that the AI is actually thinking and it's not just pattern matching. Well, as someone that uses AI tools and develops AI tools, my tools are much more useful when I treat the AI as a pattern matching next-token predictor than an actual intelligence. If I accidentally slip too many details into the context, all of a sudden the AI fails to generalize. That sounds like pattern matching and next token prediction to me. > This isn’t to say “AI” technology won’t lead to some extremely good tools. But I argue this comes from increased amounts of search and optimization and patterns to crib from, not from any magic “the AI is doing the coding” * I can tell claude code to crank out some basic crud api and it will crank it out in a minute saving me an hour or so. * I need an implementation of an algorithm that has been coded a million times on github, I ask the AI to do it and it cranks out a correct working implementation. If I only use the AI in its wheelhouse it works very well, otherwise it sucks.
  • KoolKat23 1y ago
    I think this comes down to levels of intelligence. Not knowledge, I mean intelligence. We often underestimate the amount of thinking/reasoning that goes into a certain task. Sometimes the AI can surprise you and do something very thoughtful, this often feels like magic.
  • athrowaway3z 1y ago
    Both CRUD and boilerplate are arguably a tooling issue. But there are also a bunch of things only AI will let you do. My tests with full trace level logging enabled can get very verbose. It takes serious time for a human to parse where in the 100 lines of text the relevant part is. Just telling an AI: "Run the tests and identify the root cause" works well enough, that nowadays it is always my first step.
    • insane_dreamer 1y ago
      Yesterday I used Opus to diagnose an error and it sent me down a bunch of wrong paths, suggested fixes with bad unintended consequences. If I hadn’t carefully guided it and corrected it, not only would the bug not have been fixed but it would have made things worse. My point being that if you don’t have the domain knowledge and put on your thinking cap then you will likely be screwed. But this is not how AI is sold.
• pityJuke 1y ago
  > It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t. Did geohot not found one of these?
  • eviluncle 1y ago
    Yes. He mentions that in passing that saying people will accuse him of hating on it because he didn't profit from it. I think his point of view is that his company's attempt was smaller scale and not part of the $10B+ waste? In any case I don't fully understand what he's trying to say other than negating the hype (which i generally agree with), but not offering any alternative thoughts of his own other than- we have bad tools and programming language. (why? how are they bad? what needs to change for them to be good?)
    • vessenes 1y ago
      Well, he’s currently running a startup aimed at making better tooling for the space. So, he’s putting his time where mouth is.
  • runlevel1 1y ago
    Before he deleted his Twitter account, he claimed AI was already being better than humans and that singularity was only a few years away. He's confidently wrong a lot. (Even if I happen to agree with his new, more sober take on AI coding here.)
• sMarsIntruder 1y ago
  I stopped reading at this point: > It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t. This reeks of bias-dismissing massive investments as ‘obvious’ nonsense while hyping its own tinygrad as the ‘truth’ in AI coding. Author is allowed to claim ‘most people do not care to find the truth’ but it’s hypocritical when the post ignores counterpoints, like PyTorch’s dominance in efficient coding benchmarks. Author doesn’t seem to care about finding the full truth either, just the version that pumps its bag.
• YJfcboaDaJRDw 1y ago
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• iammjm 1y ago
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• joefourier 1y ago
  Vibe coding large projects isn’t feasible yet, but as a developer here’s how I use AI to great effect, to the point where losing the tool greatly decreases my productivity: - Autocomplete in Cursor. People think of AI agents first when they talk about AI coding but LLM-powered autocomplete is a huge productivity boost. It merges seamlessly with your existing workflow, prompting is just writings comments, it can edit multiple lines at once or redirect you to the appropriate part of the codebase, and if the output isn’t what you need you don’t waste much time because you can just choose to ignore it and write code as you usually do. - Generating coding examples from documentation. Hallucination is basically a non-problem with Gemini Pro 2.5 especially if you give it the right context. This gets me up to speed on a new library or framework very quickly. Basically a stack overflow replacement. - Debugging. Not always guaranteed to work, but when I’m stuck at a problem for too long, it can provide a solution, or give me a fresh new perspective. - Self contained scripts. It’s ideal for this, like making package installers, cmake configurations, data processing, serverless micro services, etc. - Understanding and brainstorming new solutions. - Vibe coding parts of the codebase that don’t need deep integration. E.g. create a web component with X and Y feature, a C++ function that does a well defined purpose, or a simple file browser. I do wonder if a functional programming paradigm would be better when working with LLMs since by avoiding side effects you can work around their weaknesses when it comes to large codebases.
• giveita 1y ago
  I have a boring opinion. A cold take? served straight from the freezer. He is right, however AI is still darn useful. He hints at why: patterns. Writing a test suite for a new class when an existing one is in place is a breeze. It even can come up with tests you wouldnt have thought of or would have been too time pressed to check. It also applies to non-test code too. If you have the structure it can knock a new one out. You could have some lisp contraption that DRYs all the WETs so there is zero boilerplate. But in reality we are not crafting these perfect cosebases, we make readable, low-magic and boilerplatey code on tbe whole in our jobs.
  • skydhash 1y ago
    But what about the tests usefulness? Tests enforce contracts, contracts are about the domain, not the implementation. The number of tests don't actually matter as much as what is being actually verified. If you look at the code to know what to tests, you are doing it wrong.
    • giveita 1y ago
      The usefulness is in saving time boilerplating, plus figuring out tests I may not have thought of. But I do closely review the code! It turns the usual drudge of writing tests into more of a code review. Last time I did it it had some mistakes I needed to fix for sure.
      • skydhash 1y ago
        There shouldn't be boilerplate in test code. It should be refactored into harnesses, utils, and fixtures instead.
        • asjir 1y ago
          I'm not disagreeing per se, but in my experience my tests are so disposable they're never worth investing that much engineering effort in past getting them to pass
        • 1y ago
• piker 1y ago
  Pretty much nailed it. Once you’re at about 40k LOC you can just turn off the autocomplete features and use Claude or GPT to evaluate specific high-level issues. My sense is 40k LOC is the point at which the suggestions are offset by the rabbit holes they sometimes send you down, but, more importantly by obscuring from you the complexity of the thing you’re building—temporarily.
  • ako 1y ago
    I expect much of this can be solved with better architecture: smaller, more independent components. Break large code bases up into independent libraries, and LLMs can work again because they need much less code in their context.
    • piker 1y ago
      Suggesting I restructure my code base to work better with a non-deterministic black box feels like an instance of Conway’s law[1] that should be avoided. https://en.m.wikipedia.org/wiki/Conway%27s_law https://en.m.wikipedia.org/wiki/Conway%27s_law
      • ako 1y ago
        You’re free to avoid it but maybe your competition will do it and may outpace you because of it. Conways law just states that architecture follows organizational structure so it’s only to be expected that ai generated software has an ai influenced architecture.
        • piker 1y ago
          Yes, I think that’s the other position. But my bet is that significant software projects built today on today’s LLMs will be outpaced by teams in the future who avoided delegating that complexity to them. It’s a bet, but perhaps it’s wrong.
• huevosabio 1y ago
  There is some amount of truth on the AI coding claims. But, what's with the self driving hate? I take Waymos on a regular basis, and he is basing his credibility on the claim that they are not a thing. Makes him sound bitter more than insightful.
  • apercu 1y ago
    I think some of the “hate” is the hype. We’re all tired of companies announcing ground breaking tech that isn’t readily available a decade later. I don’t live in California (like most of the population of the planet) - Toronto for 18 years and now the American side of the Great Lakes. Ice storms, snow, sleet, cold weather 5-6 months out of the year. Batteries suck in the cold, sensors fail or under-perform. Hell, door handles and windows struggle in this weather. Waymo is not a thing in NY or Chicago or Minneapolis or Philadelphia (I could go on).
    • abraxas 1y ago
      And that's just in North America where cities were built for cars and with cars in mind. Autonomous driving in Europe or Asia is likely a magnitude harder due to windy roads, non trivial intersections, complex interactions of humans, bikes, trams and cars etc.
  • vessenes 1y ago
    George famously started a self driving hardware-as-an-addon company. He started after both claiming self driving was easy, and reportedly taking up an Elon bounty/bet in the seven figure range to make self driving AI for Tesla.
• faangguyindia 1y ago
  AI coding is working really good for us. My teammate shared 3 phase workflow we are using on our team to deliver project at rapid phase. It's shared on ClaudeCode subreddit https://www.reddit.com/r/ClaudeCode/s/iy058fH4sZ https://www.reddit.com/r/ClaudeCode/s/iy058fH4sZ I've been using it for months with great success
  • h4ch1 1y ago
    This is just basic spec based development, don't see what's so interesting about this. People have been using this method for quite a while, even Kiro now uses this for its entire process.
    • faangguyindia 1y ago
      >People have been using this method for quite a while, even Kiro now uses this for its entire process. Kiro is terrible at this (I assume it's your product), maybe stop shilling it every now and then
  • roxolotl 1y ago
    These workflows always surprise me. Isn’t this what you’ve been doing with humans all along? Write up a basic project plan likely following some structure the product team likes. Share it with the eng team. One to a few members of the eng team writes up a more specific plan. Everyone gets together again and goes through the specific plan to iron out the kinks. You the implement the plan. I’ve seen at my workplace people are more willing to help Claude with a plan than their fellow humans. I pointed that out and one engineer replied with “well I know Claude will read the documentation”. It’s a depressing observation but I don’t know if it’s wrong.
    • faangguyindia 1y ago
      I can implement 20 such features on my own each day with no input from others though. That's why i am using this method in the first place.
• blinkingled 1y ago
  I have been working on finding out ways to make use of AI a net-positive in my professional life as opposed to yet another thing I have to work around and have cognitive load of. Some notes so far in getting great benefits out of it on couple projects - * Getting good results from AI forced me to think through and think clearly - up front and even harder. * AI almost forces me to structure and break down my thoughts into smaller more manageable chunks - which is a good thing. (You can't just throw a giant project at it - it gets really far off from what you want if you do that.) * I have to make it a habit of reading what code it has added - so I understand it and point to it some improvements or rarely fixes (Claude) * Everyone has what they think are uninteresting parts of a project that they have to put effort into to see the bigger project succeed - AI really helps with those mundane, cog in the wheel things - it not only speeds things up, personally it gives me more momentum/energy to work on the parts that I think are important. * It's really bad at reusability - most humans will automatically know oh I have a function I wrote to do this thing in this project which I can use in that project. At some point they will turn that into a library. With AI that amount of context is a problem. I found that filling in for AI for this is just as much work and I best do that myself upfront before feeding it to AI - then I have a hope of getting it to understand the dependency structure and what does what. * Domain specific knowledge - I deal with Google Cloud a lot and use Gemini for understanding what features exist in some GCP product and how I can use it to solve a problem - works amazingly well to save me time. At the least optioning the solution is a big part of work it makes easier. * Your Git habits have to be top notch so you can untangle any mess AI creates - you reach a point where you have iterated over a feature addition using AI and it's a mess and you know it went off the rails after some point. If you just made one or two commits now you have to unwind everything and hope the good parts return or try to get AI to deal with it which can be risky.
• zkmon 1y ago
  Ofcourse, there is some truth in what you say. But business is desperate for new tech where they can redefine the order (who is big and who is small). There are floating billions which chase short term returns. Fund managers will be fired if they are not jumping on the new fad in the town. CIO's and CEO's will be fired if they are not jumping on AI. It's just nuclear arms race. It's good for none. but the other guy is on it, so you need to be too. Think about this. Before there were cars on roads, people were just as much happy. Cars came, cities were redesigned for cars with buildings miles apart, and commuting miles became the new norm. You can no longer say cars are useless because the context around them has changed to make the cars a basic need. AI does same thing. It changes the context in which we work. Everyone expects you use AI (and cars). It becomes a basic need, though a forced one. To go further, hardly anything produced by science or technology is a basic need for humans. The context got twisted, making them basic needs. Tech solutions create the problems which they claim to solve. The problem did not exist before the solution came around. That's core driving force of business.
• CompoundEyes 1y ago
  It takes time. There are cycles of “Oh wow!” “Oh wait...” “What if?” and “Aha!” Each of those has made me more effective and resulted in reliable benefits with less zig zagging back and forth.
• lukaslalinsky 1y ago
  AI coding is the one thing that got my back to programming. I got to the point in life, when my ability to focus is reducing, and I prefer to send the remaining energy elsewhere. I kind of gave up on programming, just doing architecture and occasionally doing very small programming tasks. It all changed when I discovered Claude Code and saw that the way it works, is kind of how I work. I also use a lot of grep to find my way through a new codebase, I also debug stuff by adding logs to see the context, I also rely on automated tests to tell me something is broken. I'm still very good at reading code, I'm good at architecture, and with these tools, I feel I can safely delegate the boring bits of writing code and debugging trivial things to AI. Yes, it's slower than if I focused on the task myself, but the point is that I'd not be able to focus on the task myself.
  • stevex 1y ago
    I feel the same way. After 30+ years coding, I know what I want to build, but finding the time to work on it and the focus is harder than it used to be.
• manx 1y ago
  This pre-AI article makes a very similar argument: https://mortoray.com/programming-wont-be-automated-or-it-already-has-been/ https://mortoray.com/programming-wont-be-automated-or-it-alr... Once we realize that what we actually want is turning specifications into software, I think that English will become the base for a new, high level specification language.
  • skydhash 1y ago
    We are turning specifications into software precisely because English (and any natural languages) lacks the formality that makes it not reliable (necessary quality for a tool), but great for imagination (the source of invention). We always start from natural language. RFC, docs, tickets,... are in natural language. But gaining formality (losing ambiguity) is what programming is (software engineering is doing programming well). People that struggled with programming struggle in fact with formality.
• mikewarot 1y ago
  LLMs are a tool to help match human thought to what computers can do. People would like them to have exact reproducible results, but they're on the other end of the spectrum, more like people than tools. George correctly points out there is a vast space to explore closer to the compute hardware that might profitably be explored. Thanks to the same LLMs, that's about to get a whole lot easier. If you marveled at the instant response of Turbo Pascal and IDEs, you're in for a whole lot more. --- (that was the tl;dr, here's how I got there) --- As a mapper[3], I tend to bounce all the things I know against each new bit of knowledge I acquire. Here's what happens when that coincides with GeoHot's observation about LLMs vs Compilers. I'm sorry this is so long, right now it's mostly just stream of thought with some editing. It's an exploration of bouncing the idea of impedance matching against the things that have helped advance programming and computer science. -- I've got a cognitive hammer that I tend to over-use, and that is seeing the world through the lens of a Ham Radio operator, and impedance matching[2]. In a normal circuit, maximum power flows when the source of power and the load being driven have the same effective resistance. In radio frequency circuits (and actually any AC circuit) there's another aspect, reactance. It's a time shifted form of current. This is trickier there are now 2 dimensions to consider instead of one, but most of the time, a single value, VSWR is sufficient to tell how well things are matched. VSWR is adequate to know if a transmitter is going to work, or power bouncing back from the antenna might destroy equipment, but making sure it will work across a wide range of frequencies, yields at least a 3rd dimension. As time progresses, if you actually work with those additional dimensions, it slowly sinks in what works, and how, and what had previously seemed like magic, becomes engineering. For example, vacuum tube based transmitters have higher resistances that almost any antenna, transformers and coupling through elements that shift power back and forth between the two dimensions allow optimum transfer without losses at the cost of complexity. On the other hand, semiconductor based transmitters tend to have the opposite problem, their impedances are lower, so different patters work for them, but most people still just see it as "antenna matching", and focus on the single number, ignoring the complexity. {{Wow... this is a book, not an answer on HN... it'll get shorter after a few edits, I hope, NOPE... it's getting longer...}} Recently, a tool that used to cost thousands of dollars, the Vector Network Analyzer, has become available at less than $100. It allows for measuring resistance, reactance, and gain simultaneously across frequency. It's like compilers, making things manageable in scope that otherwise seemed too complex. It only took a few times playing with a NanoVNA to understand things that previously would have been some intense EE classwork with Smith Charts. Similarly, tools like Software Defined Radios for $30, and GNU Radio (for $0.00) allowed understanding digital signal processing in ways that would have been equally difficult without professional instruction. With these tools, you can build a signal flow graph in an interactive window, and in moments have a working radio for FM, AM, Sideband, or any other thing you can imagine. It's magic! -- back to computing and HN -- In the Beginning was ENIAC, a computer that took days to set up and get working on a given problem by a team with some experience. Then John Von Neumann came along, and added the idea of stored programs, which involved sacrificing the inherently parallel nature of the machine, losing 70% of its performance, but making it possible to set it up for a task simply by loading a "program" onto it's back of instruction switches. Then came cards and paper tape storage, further increasing the speed at which data and programs could be handled. It seems to me that compilers were like one of the above tools, they made it possible for humans to do things that only Alan Turing or others similarly skilled could do in the beginning of programming. Interactive programming increased the availability of compute, and make machines that were much faster that programmers, more easily distributed among teams of programmers. IDEs were another. Turbo Pascal allowed compile, linking, and execution to happen almost instantly. It widely opened the space for experimentation by reducing the time required to get feedback from minutes to almost zero. I've done programming on and off through 4 decades of work. Most of my contemplation is as an enthusiast, instead of professional. As far as compilers and the broader areas of Computer Science I haven't formally studied, it seems to me that LLMS, especially the latest "agentic" versions, will allow me to explore things far easier than I might have otherwise done. LLMs have helped me to match my own thoughts across a much wider cognitive impedance landscape. (There's that analogy/hammer in use...) Compilers are an impedance matching mechanism. Allowing a higher level of abstraction gives flexibility. One of the ideas I've had in the past for helping with better interaction between people and compilers is to allow compilers that also work backwards.[1] I'm beginning to suspect that with LLMs, I might actually be able to attempt to build this system, it's always seemed out of reach because of the levels of complexity involved. I have several other ideas that might warrant a new attempt, now that I'm out of the job market, and have the required free time and attention. {{Sorry this turned out to be an essay... I'm not sure how to condense it back down right now}} [1] https://wiki.c2.com/?BidirectionalCompiler https://wiki.c2.com/?BidirectionalCompiler [2] https://en.wikipedia.org/wiki/Impedance_matching https://en.wikipedia.org/wiki/Impedance_matching [3] https://garden.joehallenbeck.com/container/mappers-and-packers https://garden.joehallenbeck.com/container/mappers-and-packe...
• matt3D 1y ago
  This is a more extreme example of the general hacker news group think about AI. Geohot is easily a 99.999 percentile developer, and yet he can’t seem to reconcile that the other 99.999 percent are doing something much more basic than he can ever comprehend. It’s some kind of expert paradox, if everyone was as smart and capable as the experts, then they wouldn’t be experts. I have come across many developers that behave like the AI. Can’t explain codebases they’ve built, can’t maintain consistency. It’s like a aerospace engineer not believing that the person that designs the toys in an Kinder egg doesn’t know how fluid sims work.
  • mihaic 1y ago
    [flagged]
    • Sammi 1y ago
      You've seen plenty of people who hacked the ps3 and iphone as teenagers and created a low level system analysis tool for doing such system hacks? You've seen plenty of people writing self driving car software a decade ago? Why did you write this when you know nothing?
      • mihaic 1y ago
        I actually have seen plenty of people that could have done something like this, but did not because they simply never tried. Being daring by itself is a skill, but we're talking raw technical ability here. I've actually seen another developer that was probably in the same category write his own self-driving software. It kind of worked, but couldn't have ever been production ready, so it was just an exercise in flexing without any practical application. So, what product that George built do you actually use?
        • dehugger 1y ago
          So, if I understand correctly, you've seen plenty of people that didn't do what he did? This was not a compelling argument.
• Havoc 1y ago
  >It’s not precise in specifying things. That's the point - it's a higher level of abstraction. >highly non-deterministic ...not unlike say a boss telling a junior to change something? The bet here isn't that AI can be as precise as something hand coded but rather that you can move up a step in the abstraction layer. To use his compiler example...I don't care what the resulting assembly instructions look like, just whether it works. It's the same thing here just one level higher
  • skydhash 1y ago
    > That's the point - it's a higher level of abstraction. That's not what abstraction is. When I type `echo Hello, World`, I don't have to deal with graphic drivers and test rendering to have the text on the screen. And I don't have to worry that "Goodbye" will appear instead. > Not unlike say a boss telling a junior to change something? Junior don't stay junior for long. And bosses usually give juniors less time to grow than people are allocating AI tools to actually prove themselves. Github copilot was more than 3 years ago. Today a new hire is expected to be productive on day one.
• anabis 1y ago
  I also had a compiler related description come to me after using Copilot. It allows you to partially generate imperative code declaratively, by writing a comment like //now I will get rows X, Y, Z from ContentsProvider then tab tab complete. You can then even tweak the generated code, very useful!
  • runlevel1 1y ago
    There's the rub: AI coding is like super tab complete. It's a useful tool. Full stop.
• runningmike 1y ago
  Great short read. But this “ It’s why the world wasted $10B+ on self driving car companies that obviously made no sense.” Not everything should make sense. Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why.
  • skydhash 1y ago
    > Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why. It would be, if there weren't actual important works that needs funding.
  • stevex 1y ago
    "that obviously made no sense" is bizarre. Waymo's driving people around with an injuries-per-mile rate that's lower than having humans do it. I don't see how that reconciles with "obviously made no sense".
    • CamperBob2 1y ago
      "He hates 'em because he ain't 'em" is essentially the whole theme of this rather-disappointing post. I think he's having a bad day. He's smarter than this.
• sunir 1y ago
  Code has a lot of bits of information the compiler users to construct the program. But not all because software needs iteration to get right both in bugs and in solving the intended problem. The llm prompt has even fewer bits of information specifying the system than code. The model has a lot more bits but still finite. A perfect llm cannot build a perfect app in one shot. However AIs can research, inquire, and iterate to gain more bits than when you started. So the comparison to a compiler is not apt because the compiler can’t fix bugs or ask the user for more information about what the program should be. Most devs are using ai at the autocomplete level which is like this compiler analogy which makes sense in 2025 but that isn’t where we will be in 2030. What we don’t know is how good the technology will be in the future and how cheap and how fast. But it’s already very different than a compiler.
• mindwok 1y ago
  These articles are beyond the point of exhausting. Guys, just go use the tools and see if you like them and feel more capable with them. If you do, great, if you don’t, then stop.
  • energy123 1y ago
    The truth will emerge naturally through labor market competition in the long-run. You do you. I will be using these tools extensively. Good luck out there in the arena.
• deleted 1y ago
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• hereme888 1y ago
  I'm a 100% vibe-coder. AI/CS is not my field. I've made plenty of neat apps that are useful to me. Don't ask me how they work; they just do. Sure the engineering may be abysmal, but it's good enough to work. It only takes basic english to produce these results, plus complaining to the AI agent that "The GUI is ugly and overcrowded. Make it look better, and dark mode." Want specs? "include a specs.md" This isn't a 20% more productive feeling. It's productivity beyond what I will ever do on my own, given this is not my field. This is all possible because AI was trained on the outstanding work of CS engineers like ya'll. But the article is highly opinionated. It's like saying only phD's can be called scientists, or only programmers can be computer hackers. But in reality every human is a scientist and a hacker in the real world. The guy in a street corner in India came up with novel ways to make and sell his product, but never wrote a research paper on it. The guy on his fourth marriage noted a statistical correlation in the outcome when meeting women at a bar vs. at a church. The plant that grew in the crevice of a rock noted sunlight absorption was optimal at an angle of 78.3 degrees and grew in that direction.
  • croes 1y ago
    The crucial part is security. If the apps runs locally it doesn’t matter, if it‘s connected to the net it could be the seed for the next Mirai bot network.
    • hereme888 1y ago
      Oh I'd never argue that. Cloud stuff is truly beyond the complexity I'd get involved with at the moment.
    • rhizome31 1y ago
      Apps running locally can also be subject to security issues. What you're trying to say is probably "apps not using untrusted input". If an app takes no input all, I guess we could say that security isn't an issue, but there could still be safety issues.
    • chatmasta 1y ago
      It’s a pretty good solution for creating live mockups. A designer on my team came back eight hours after a meeting with a fully vibe coded, multi-page interface. I was honestly blown away. I had no idea this was the state of what’s possible with these tools. Was it a real website? No, but it’s a live mockup way better than any Figma mock or rigid demo-ware.
• softwaredoug 1y ago
  Instead of everyone telling their personal anecdote, we should look at the actual research. Asking ChatGPT to summarize the state of AI coding research that’s not done by one of the providers, and I feel like I got the sanest summary of the actual state of things: > If you weight independent work more heavily: current best evidence says don’t expect speedups for senior devs in complex, well-known codebases without process changes; expect security pitfalls unless you add guardrails. Gains look more reliable for novices, unfamiliar code, and templated tasks Seems the most sane PoV on the actual benefits I’ve seen. Actual link if you want to pick apart GPT-5s summary https://chatgpt.com/share/68c56214-1038-8004-b1ed-359393eb152b https://chatgpt.com/share/68c56214-1038-8004-b1ed-359393eb15...
• saltserv 1y ago
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• 8cvor6j844qw_d6 1y ago
  Just started using Claude Code recently. It seems to speed up feature development but requires one to have a good understanding of the codebase to guide it and be aware of edge cases it missed. Also, it doesn't seem to be able to take advantage of latest information or new SDK features unless deliberately informed. Not sure if I'm doing it right, but I resorted to feeding it documentation when it can't seem to something right. The only thing I'm still unsure is the context management with /compact and /clear
  • h4ch1 1y ago
    Dunno how it'll work with Claude Code, but I use the context7 MCP to feed up-to-date docs in copilot. Sometimes that also doesn't work so I make a guiding document using whatever llms.txt the framework/language/platform offers or just wget the entire documentation, condense it into a single text file and use that while prompting. Seems to work ok, especially for Svelte 5 where most LLMs except Gemini don't give me runes from the get-go.
    • faangguyindia 1y ago
      >Dunno how it'll work with Claude Code, but I use the context7 MCP to feed up-to-date docs in copilot. No wonder you've no idea about current capability of LLM based coding, copilot is far behind competition.
• dsiegel2275 1y ago
  So I have all kinds of problems with this post. First, the assertion that the best model of "AI coding" is that it is a compiler. Compilers deterministically map a formal language to another under a spec. LLM coding tools are search-based program synthesizers that retrieve, generate, and iteratively edit code under constraints (tests/types/linters/CI). That’s why they can fix issues end-to-end on real repos (e.g., SWE-bench Verified), something a compiler doesn’t do. Benchmarks now show top agents/models resolving large fractions of real GitHub issues, which is evidence of synthesis + tool use, not compilation. Second, that the "programming language is English". Serious workflows aren’t "just English." They use repo context, unit tests, typed APIs, JSON/function-calling schemas, diffs, and editor tools. The "prompt" is often code + tests + spec, with English as glue. The author attacks the weakest interface, not how people actually ship with these tools. Third, non-determinism isn't disqualifying. Plenty of effective engineering tools are stochastic (fuzzers, search/optimization, SAT/SMT with heuristics). Determinism comes from external specs: unit/integration tests, type systems, property-based tests, CI gates. False dichotomy: "LLMs are popular only because languages/libraries are bad." Languages are improving (e.g. Rust, Typescript), yet LLMs still help because the real bottlenecks are API lookup, cross-repo reading, boilerplate, migrations, test writing, and refactors, the areas where retrieval and synthesis shine. These are complementary forces, not substitutes. Finally, no constructive alternatives are offered. "Build better compilers/languages" is fine but modern teams already get value by pairing those with AI: spec-first prompts, test-gated edits, typed SDK scaffolds, auto-generated tests, CI-verified refactors, and repo-aware agents. A much better way to think about AI coding and LLMs is that they aren’t compilers. They’re probabilistic code synthesizers guided by your constraints (types, tests, CI). Treat them like a junior pair-programmer wired into your repo, search, and toolchain. But not like a magical English compiler.
  • inimino 1y ago
    People knock "English as a programming language", but in my opinion this is the whole value of AI programming: by the time you've expressed your design and constraints well enough that an LLM can understand it, then anyone can understand it, and you end up with a codebase that's way more maintainable than what we're used to. The problem of course is when people throw away the prompt and keep the code, like the code is somehow valuable. This would be like everyone checking in their binaries and throwing away their source code every time, while arguments rage on HN about whether compilers are useful. (Meanwhile, compiler vendors compete on their ability to disassemble and alter binaries in response to partial code snippets.) The right way to do AI programming is: English defines the program, generated code is exactly as valuable as compiler output is, i.e. it's the actual artifact that does the thing, so in one sense it's the whole point, but iterating on it or studying it in detail is a waste of time, except occasionally when debugging. It's going to take a while, but eventually this will be the only way anybody writes code. (Note: I may be biased, as I've built an AI programming tool.) If you can explain what needs to be done to a junior programmer in less time than it takes to do it yourself, you can benefit from AI. But, it does require totally rethinking the programming workflow and tooling.
• amirhirsch 1y ago
  That METR study gets a lot of traction for its headline; and I doubt many people read the whole thing—it was long—but the data showed a 50% speed up for the one dev with the most experience with Cursor/AI, suggesting a learning curve and also wild statistical variation on a small sample set. An errata later suggested another dev who did not have a speedup had not represented their experience correctly, but still strongly draws into question the significance of the findings. The specific time sucks measured in the study are addressable with improved technology like faster LLMs and improved methodology like running parallel agents—the study was done in March running Claude 3.7 and before Claude Code. We also should value the perception of having worked 20% less even if you actually spent more time. Time flies when you’re having fun!
  • zurfer 1y ago
    It was such a great study imo and rightfully deserves a lot of attention since it's a great alternative to twitter vibes. But to stop here and say AI coding doesn't work will just not hold up well. We have a sample size of 16 developers for 250 tasks. Here is my data point: adding AI to your engineering team really is like adding a new member. At first it slows everybody down: explain this, explain that, stupid mistake made that the experienced dev would have avoided. But over the months you learn how to get the most out of them: they speak fluently all programming languages, don't mind writing 20 tests or coming up with good industry ideas to your problems. Over time I have come to appreciate that the market is rather smart and even hype has its place. You often have to cross a line to know you crossed it, especially with something as fuzzy and quickly changing as technological progress hitting the messy real world. But then again, we need the heretics to stay smart and not follow an emperor without clothes.
    • amirhirsch 1y ago
      Just putting this up as a reference the next time this comes up on HN. The study data shows that the median task is 1.5hrs and the 15 minutes that developers think they saved was actually more than that many minutes less researching, testing, and writing code and the 15 minutes longer they actually spent was more idle (5mins) and waiting for AI (5 mins) and reviewing and prompting dominating their work over actually writing code. The person who showed a speed-up indicated over a week of prior experience with cursor while all others under a week.
• roflcopter69 1y ago
  > Or we could, you know, do the hard work and build better programming languages, compilers, and libraries. But that can’t be hyped up for billions. 100% this. I fear that AI will cause us to be stuck in a local optimum for the next decades where most of the code will be Python or JS because these are the languages best supported by LLMs. Don't get me wrong, Python and JS and mature and productive languages. That's fine. But we could have it so much better if there was more effort put into a next generation of tools that take all the harsh lessons learnt from the tools before and "just" do it better. I acknowledge that we get incremental improvements here and there but some things are just unfixable without breaking existing ecosystems.
  • CamperBob2 1y ago
    But we could have it so much better if there was more effort put into a next generation of tools that take all the harsh lessons learnt from the tools before and "just" do it better. We've had 50+ years to do that. Progress has been unimpressive, to be charitable. It's time to let some different people try something else.
• CuriouslyC 1y ago
  This post is such a cold take, and is going to age horribly. Self driving cars fail because of regulatory requirements for five nines reliability, and they're doing inference over a dynamic noisy domain. Autonomous engineering does not have these issues. Code doesn't need to be five nines correct, and the domain of inference is logical and basically static. If the AI agent/coding companies didn't have their heads up their collective asses we could have fully spec driven autonomous coding within ~3 years, 100%.
  • deleted 1y ago
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  • JimmyBuckets 1y ago
    This sounds like you haven't used one of these coding agents.
    • CuriouslyC 1y ago
      I'm piloting a swarm of coding agents 12+ hours a day, friend.
      • _se 1y ago
        Seems like you should know that you're spouting bullshit if that's the case.
        • CuriouslyC 1y ago
          Seems like you should get better at picking up on when your priors are miscalibrated.
• deleted 1y ago
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• richardfulop 1y ago
  I feel like many opinion pieces on AI coding are written from the perspective of highly experienced software engineers, often from a kind of ivory tower. (even that study they cite was based on “16 experienced open-source developers.”) But for people who aren’t seasoned coders, these tools are incredibly valuable. I have some coding experience myself, but it’s never been my profession (I’m a visual artist). Now, I can accomplish in an afternoon what would otherwise take me days. Two months ago I left my job to work solo on my video game, and even though my budget is limited, I still make sure to keep Claude and ChatGPT. Also, being able to write something on my phone at 1 a.m. when I can’t sleep, send it to Codex, and then test it first thing in the morning at my computer feels pretty magical. It also takes away the worry of “what if this isn’t the best way to do it?” since refactoring parts of my codebase is now so easy. It helps not just with writing code, but also with removing the fear that might otherwise keep me from sitting down and getting the work done. That said, I understand that much of the criticism is really aimed at the marketing hype around these tools and the broader “this will replace the engineers at your company” narrative.
• redhale 1y ago
  > The only reason it works for many common programming workflows is because they are common. The minute you try to do new things, you need to be as verbose as the underlying language. You can stop here. This is enough to change the game. SO MUCH time and money is spent on mostly-boilerplate line of business apps. If you don't understand this, you are in a bubble of some kind. The corporate world's IT departments and consulting agencies are filled with developers who write nothing but what boil down to custom-tailored CRUD apps. AI is absolutely KILLER for this kind of work. If you're doing novel research, perhaps not. But that describes a relatively small portion of the worldwide developer community.
• xeckr 1y ago
  By the same token, a software developer could perhaps also be likened to a compiler, taking language and/or visual information as input and turning it into code.
• sltr 1y ago
  What an aggressive tone. By the author's implied definition of compiler, a human is also a compiler. (Coffee in, code out, so the saying goes.) But code is distinct from design, and unlike compilers, humans are synthesizers of design. LLMs let you spend more time as system architect instead of code monkey.
  • jasonjmcghee 1y ago
    I wouldn't over index on his tone here- that's the only tone I've seen him use.
    • dwaltrip 1y ago
      Doesn’t make it any better.
• ripped_britches 1y ago
  Who hurt you georgie?
• goshx 1y ago
  This kid is 35, by the way.
• jrm4 1y ago
  Again, can't help but wonder when we might improve this conversation by more strongly defining "what kind of programming." It's just like "Are robots GOOD or BAD at building things?" WHAT THINGS?
• huflungdung 1y ago
  [dead]
• andrewchambers 1y ago
  Kind of surprised by this take - I use openpilot often and also use claude code. I kind of consider them the same thing. Openpilot can drive really well on highways for hours on end when nothing interesting is happening. Claude code can do straight forward refactors, write boilerplate, do scaffolding, do automated git bisects with no input from me. Neither one is a substitute for the 'driver'. Claude code is like the level 2 self driving of programming.
• btown 1y ago
  > AI workflows are, in practice, highly non-deterministic. While different versions of a compiler might give different outputs, they all promise to obey the spec of the language, and if they don’t, there’s a bug in the compiler. English has no similar spec. AI coding shines when this is a good thing. For instance, say you have to adapt the results of one under-documented API into another. Coding agents like Claude Code can write a prototype, get the real-world results of that API, investigate the contents, write code that tries to adapt, test the results, rewrite that code, test again, rewrite again, test again, ad nauseam. There are absolutely problem domains where this kind of iterative adaptation is slower than bespoke coding, where you already have the abstractions such that every line you write is a business-level decision that builds on years of your experience. Arguably, Geohot's low-level work on GPU-adjacent acceleration is a "wild west" where his intuition outstrips the value of experimentation. His advice is likely sound for him. If he's looking for a compiler for the highly detailed specifications that pop into his head, AI may not help him. But for many, many use cases, the analogy is not a compiler; it is a talented junior developer who excels at perseverance, curiosity, commenting, and TDD. They will get stuck at times. They will create things that do not match the specification, and need to be code-reviewed like a hawk. But by and large, if the time-determining factor is not code review but tedious experimentation, they can provide tremendous leverage.
• pier25 1y ago
  > It’s why the world wasted $10B+ on self driving car companies that obviously made no sense Anyone knows what the author is referring to here? I'm guessing a lot more than $10B has been invested into self driving. So probably referring to failed companies. But which ones?
• within_will 1y ago
  Natural language becomes like an abstraction above the high-level languages then, even above things like Scratch and Basic. But you can't define how big of an abstraction it is exactly, like I can say "write an GPS app" or "write a function that does x" The former is more abstracted and the latter is less. It's this ambiguity and non-determinism that's tricky.
• dmh2000 1y ago
  I'm 72, a dev for 40 years. I've lost a step or two. It's harder to buckle down and focus, but using AI tools have enabled me to keep building stuff. I can spec a project, have an agent build it then make sure it works. I just code for fun anyway.
  • iKlsR 1y ago
    I love using AI to prototype that something is possible then go and build it myself while borrowing bits from that initial MVP. The other night I wanted to build a browser extension that could intercept requests from a tab, claude got me something working in about 10 minutes with a couple prompts and a local storage session and then I toyed with the UI a bit to see what was possible. Now after a weekend morning I have something much slimmer, predictable and sophisticated running... my extension shows a list of repeated responses and I can toggle which one to send to a localhost api that has a simple job queue to update a sqlite db with each new entry, extract the important parts and send it to my lm studio gpt oss 20b endpoint for some analysis and finally and send me a summary on telegram. I know what I want in my head but cutting down the experimenting or PoC step down to minutes vs hours is pretty useful and as a competent enough dev it's elevated what I can get done now so I can take on more work than I would have by myself previously.
• stevage 1y ago
  Anyone dismissing AI coding without having seriously tried it or looked at the outputs of people who have can't be taken seriously. Yes, it shouldn't worked in theory. But it's disturbingly good in practice, for someone who knows what they are doing.
  • th0ma5 1y ago
    So anyone that doesn't know what they're doing can dismiss it without seriously trying it? I mean, I agree, but I feel like you wouldn't with this implication of what you're saying...
• j3th9n 1y ago
  “Some people go as far as to believe that perception is reality and that truth is a construction.” To the author: ask them what they mean.
• scloudfox 1y ago
  I use LLMs like autocomplete on steroids - fast but need reviews
• drumdance 1y ago
  IMO the value of AI coding is less about what we're seeing now and more about what will happen once anyone can program their phone, watch or smart speaker just by feeding it a prompt.