6 ms·

> Generative AI, as we know it, has only existed ~5-6 years, and it has improved substantially, and is likely to keep improving. Every 2/3 months we're hearing

• BeetleB 10mo ago
  > We're talking "copy basic examples and don't hallucinate APIs" here, not deep complicated system design topics. If your metric is an LLM that can copy/paste without alterations, and never hallucinate APIs, then yeah, you'll always be disappointed with them. The rest of us learn how to be productive with them despite these problems.
  • drewbug01 10mo ago
    > If your metric is an LLM that can copy/paste without alterations, and never hallucinate APIs, then yeah, you'll always be disappointed with them. I struggle to take comments like this seriously - yes, it is very reasonable to expect these magical tools to copy and paste something without alterations. How on earth is that an unreasonable ask? The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high. What, precisely, are they good for?
    • tombert 10mo ago
      I think what they're best at right now is the initial scaffolding work of projects. A lot of the annoying bootstrap shit that I hate doing is actually generally handled really well by Codex. I agree that there's definitely some overhype to them right now. At least for the stuff I've done they have gotten considerably better though, to a point where the code it generates is often usable, if sub-optimal. For example, about three years ago, I was trying to get ChatGPT to write me a C program to do a fairly basic ZeroMQ program. It generated something that looked correct, but it would crash pretty much immediately, because it kept trying to use a pointer after free. I tried the same thing again with Codex about a week ago, and it worked out of the box, and I was even able to get it to do more stuff.
      • smithkl42 10mo ago
        I think it USED to be true that you couldn't really use an LLM on a large, existing codebase. Our codebase is about 2 million LOC, and a year ago you couldn't use an LLM on it for anything but occasional small tasks. Now, probably 90% of the code I commit each week was written by Claude (and reviewed by me and other humans - and also by Copilot and ZeroPath).
    • blibble 10mo ago
      > What, precisely, are they good for? scamming people
      • viking123 10mo ago
        Also good for manufacturing consent in Reddit and other places. Intelligence services busy with certain country now, bots using LLMs to pump out insane amounts of content to mold the information atmosphere.
    • ubercow13 10mo ago
      It seems like just such a weird and rigid way to evaluate it? I am a somewhat reasonable human coder, but I can't copy and paste a bunch of code without alterations from memory either. Can someone still find a use for me?
    • falloutx 10mo ago
      Its strong enough to replace humans at their jobs and weak enough that it cant do basic things. Its a paradox. Just learn to be productive with them. Pay $200/month and work around with its little quirks. /s
    • BeetleB 10mo ago
      For a long time, I've wanted to write a blog post on why programmers don't understand the utility of LLMs[1], whereas non-programmers easily see it. But I struggle to articulate it well. The gist is this: Programmers view computers as deterministic. They can't tolerate a tool that behaves differently from run to run. They have a very binary view of the world: If it can't satisfy this "basic" requirement, it's crap. Programmers have made their career (and possibly life) being experts at solving problems that greatly benefit from determinism. A problem that doesn't - well either that needs to be solved by sophisticated machine learning, or by a human. They're trained on essentially ignoring those problems - it's not their expertise. And so they get really thrown off when people use computers in a nondeterministic way to solve a deterministic problem. For everyone else, the world, and its solutions, are mostly non-deterministic. When they solve a problem, or when they pay people to solve a problem, the guarantees are much lower. They don't expect perfection every time. When a normal human asks a programmer to make a change, they understand that communication is lossy, and even if it isn't, programmers make mistakes. Using a tool like an LLM is like any other tool. Or like asking any other human to do something. For programmers, it's a cardinal sin if the tool is unpredictable. So they dismiss it. For everyone else, it's just another tool. They embrace it. [1] This, of course, is changing as they become better at coding.
      • maccard 10mo ago
        I’m perfectly happy for my tooling to not be deterministic. I’m not happy for it to make up solutions that don’t exist, and get stuck in loops because of that. I use LLMs, I code with a mix of antigravity and Claude code depending on the task, but I feel like I’m living in a different reality when the code I get out of these tools _regularly just doesn’t work, at all_. And to the parents point, I’m doing something wrong for noticing that?
        • BeetleB 10mo ago
          If it were terrible, you wouldn't use them, right? Isn't the fact that you continue to use AI coding tools a sign that you find them a net positive? Or is it being imposed on you? > And to the parents point, I’m doing something wrong for noticing that? There's nothing wrong pointing out your experience. What the OP was implying was he expects them to be able to copy/paste reliably almost 100% of the time, and not hallucinate. I was merely pointing out that he'll never get that with LLMs, and that their inability to do so isn't a barrier to getting productive use out of them.
• tombert 10mo ago
  Sure, but think about what it's replacing. If you hired a human, it will cost you thousands a week. Humans will also fail at basic tasks, get stuck in useless loops, and you still have to pay them for all that time. For that matter, even if I'm not hiring anyone, I will still get stuck on projects and burn through the finite number of hours I have on this planet trying to figure stuff out and being wrong for a lot of it. It's not perfect yet, but these coding models, in my mind, have gotten pretty good if you're specific about the requirements, and even if it misfires fairly often, they can still be useful, even if they're not perfect. I've made this analogy before, but to me they're like really eager-to-please interns; not necessarily perfect, and there's even a fairly high risk you'll have to redo a lot of their work, but they can still be useful.
  • falloutx 10mo ago
    I am an AI-skeptic but I would agree this looks impressive from certain angles, especially if you're an early startup (maybe) or you are very high up the chain and just want to focus on cutting costs. On the other hand, if you are about to be unemployed, this is less impressive. Can it replace a human? I would say no its still long way to go, but a good salesman can convince executives that it does and thats all that matters.
    • tombert 10mo ago
      I just think Jevins paradox [1]/Gustafson's Law [2] kind of applies here. Maybe I shouldn't have used the word "replaced", as I don't really think it's actually going to "replace" people long term. I think it's likely to just lead to higher output as these get better and better . [1] https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox [2] https://en.wikipedia.org/wiki/Gustafson%27s_law https://en.wikipedia.org/wiki/Gustafson%27s_law
      • falloutx 10mo ago
        Not you, but the word replaced is the being used all the time. Even senior engineers are saying they are using it as a junior engineers while we can easily hire junior engineers (but Execs don't want to). Jevon's paradox wont work in Software because user's wallets and time is limited, and if software becomes too easy to build, it becomes harder to sell. Normal people can have 5 subscriptions, may be 10, but they wont be going to 50 or 100. I would say we would have already exhausted users already, with all the bad practices.
• elzbardico 10mo ago
  There’s a subtle point a moment when you HAVE to take the driver wheel from the AI. All issues I see are from people insisting to use far beyond the point it stops being useful. It is a helper, a partner, it is still not ready go the last mile
  • xp84 10mo ago
    It's funny how many people don't get that. It's like adding a pretty great senior or staff level engineer to sit on-call next to every developer and assist them, for basically free (I've never used any of the expensive stuff yet. Just things like Copilot, Grok Code in JetBrains, just asking Gemini to write bits of code for me). If you hired a staff engineer to sit next to me, and I just had him/her write 100% of the code and never tried to understand it, that would be an unwise decision on my part and I'd have little room to complain about the times he made mistakes.
  • maccard 10mo ago
    As someone else said in this thread: > The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high. I’m perfectly happy to write code, to use these tools. I do use them, and sometimes they work (well). Other times they have catastrophic failures. But apparently it’s my failure for not understanding the tool or expecting too much of the tool, while others are screaming from the rooftops about how this new model changes everything (which happens every 3 months at this point)
    • elzbardico 10mo ago
      There's no silver bullet. I’m not a researcher, but I’ve done my best to understand how these systems work—through books, video courses, and even taking underpaid hourly work at a company that creates datasets for RLHF. I spent my days fixing bugs step-by-step, writing notes like, “Hmm… this version of the library doesn’t support protocol Y version 4423123423. We need to update it, then refactor the code so we instantiate ‘blah’ and pass it to ‘foo’ before we can connect.” That experience gave me a deep appreciation for how incredible LLMs are and the amazing software they can power—but it also completely demystified them. So by all means, let’s use them. But let’s also understand there are no miracles here. Go back to Shannon’s papers from the ’60s, and you'll understand that what seems to you like "emerging behaviors" are quite explainable from an information theory background. Learn how these models are built. Keep up with the latests research papers. If you do, you’ll recognize their limitations before those limitations catch you by surprise. There is no silver bullet. And if you think you’ve found one, you’re in for a world of pain. Worse still, you’ll never realize the full potential of these tools, because you won’t understand their constraints, their limits, or their pitfalls.
• Aurornis 10mo ago
  You’re doing exactly the thing that the parent commenter pointed out: Complaining that they’re not perfect yet as if that’s damning evidence of failure. We all know LLMs get stuck. We know they hallucinate. We know they get things wrong. We know they get stuck in loops. There are two types of people: The first group learns to work within these limits and adapt to using them where they’re helpful while writing the code when they’re not. The second group gets frustrated every time it doesn’t one-shot their prompt and declares it all a big farce. Meanwhile the rest of us are out here having fun with these tools, however limited they are.
  • maccard 10mo ago
    Someone else said this perfectly farther down: > The whole discourse around LLMs is so utterly exhausting. If I say I don't like them for almost any reason, I'm a luddite. If I complain about their shortcomings, I'm just using it wrong. If I try and use it the "right" way and it still gets extremely basic things wrong, then my expectations are too high. As I’ve said, I use LLMs, and I use tools that are assisted by LLMs. They help. But they don’t work anywhere near as reliably as people talk about them working. And that hasn’t changed in the 18 months since I first promoted v0 to make me a website.
    • vips7L 10mo ago
      Rather be a Luddite than contribute to these soul suckers like OpenAI and help them lay off workers.
      • Gud 10mo ago
        How are they “soul suckers”? Using LLMs has made it fun for me to make software again.
        • kakacik 10mo ago
          Shallow learning, overall laziness imprinted on the character over time. For kids and juniors starting the field they are much worse. None of the stuff I've learned over past 20 years was handed over to me in this easy fashion. Overconfident and over-positive shallow posts just hurt the overall discussion. Also some layer of arrogance - a typical 'if you struggle to get any significant value out of this new toy you must be doing something horribly wrong, look at us all being 100x productive!' which is never ever followed by some detailed explanation of their stack and other details. Clearly the tools have serious issues since most users struggle to get any sustained reliable added value, and everybody keeps hoping things will improve later due to it being able to write lengthy prose on various topics or fill our government documents.
• deleted 10mo ago
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• nonethewiser 10mo ago
  >Every 2/3 months we're hearing there's a new model that just blows the last one out of the water for coding I haven't heard that at all. I hear about models that come out and are a bit better. And other people saying they suck. >Meanwhile, here I am with Opus and Sonnet for $20/mo and it's regularly failing at basic tasks, antigravity getting stuck in loops and burning credits. Is it bringing you any value? I find it speeds things up a LOT.
• user34283 10mo ago
  I have a hard time believing that this v0, from 2023, achieved comparable results to Gemini 3 in Web design. Gemini now often produces output that looks significantly better than what I could produce manually, and I'm an expert for web, although my expertise is more in tooling and package management.
• golly_ned 10mo ago
  Frankly I think the 'latest' generation of models from a lot of providers, which switch between 'fast' and 'thinking' modes, are really just the 'latest' because they encourage users to use cheaper inference by default. In chatgpt I still trust o3 the most. It gives me fewer flat-out wrong or nonsensical responses. I'm suspecting that once these models hit 'good enough' for ~90% of users and use cases, the providers started optimizing for cost instead of quality, but still benchmark and advertise for quality.