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PaperclipPerfector  3d ago (text post)   13134 thread views

Culture War Roundup for the week of June 9, 2025

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Oh for heaven's sake, dude. When did I ever say I consider myself better than anyone else, that I would deserve such a litany of sarcasm directed at me? I don't think that and certainly haven't said it. I am just an ordinary programmer - I doubt very much that I'm better at programming than anyone here except the non-programmers, and I'm sure I'm worse than more than a few. Not only did I say "hey I'm not trying to litigate this right now" and that got ignored, now I get people dogpiling me saying I'm a troll or think I'm better than everyone else or whatever.

But fine, since you and @SnapDragon are insistent on pressing me on the topic (and since I apparently didn't say to him what my experience was, my bad on that, but I know I have posted this in a previous thread before), I will reiterate the things that I personally have seen LLMs fall flat on their face with. This is of course in addition to the various embarrassments that are public, like Microsoft's ill-conceived attempt to let Copilot loose on PRs.

  • Tried to get ChatGPT to help me generate a fluentd config file that would process logs in a way I needed to do for work. It gave me a config file that not only didn't do the thing, it didn't conform to the schema and caused the software to crash
  • Tried to get it to help me order CloudFormation resource delete-and-recreate in a different way than the default order. It first gave me options that didn't even exist, then it gave me options that did exist but didn't do what I asked for. I had a similar issue with the AWS-trained model they provide, which also suggested options that don't do what I asked for (and are documented as such).
  • A coworker used ChatGPT (a custom one trained on our API docs) to generate a script to run against our API. Again it hallucinated methods that simply do not exist.

These were all within the last year, though I couldn't tell you exactly when or what model or anything. And I've been honest that sometimes it has done good work for me, namely in generating short snippets of code in a language (or using an API) that I know well enough to recognize as correct when I see it, but not well enough to produce without laborious reading of docs. I've never claimed that LLMs work 0% of the time (if people have taken that away, I've done a poor job communicating), but the failure rate is much too high for them to be considered viable tools in my book. Most frustratingly, the things that I actually need help on, the ones where I don't know really anything about the topic and a workable AI assistant would actually save me a ton of time, are precisely the cases where it fails hard (as in my examples where stuff doesn't even work at all).

So those are again my experiences with LLMs that have caused me to conclude that they are hype without substance. Disagree if you like, I don't mind if you find it useful and like I have tried to say I'm not actually trying to convince people of my views on this topic any more. Like I tried to say earlier, the only reason I posted in this thread was to push back on the idea that one simply must be ignorant if they don't think LLMs are good at coding (and other things). That idea is neither true, necessary, or kind (as the rules allude to) and I felt that it deserved some sort of rebuttal. Though heaven knows I wish I had just left it alone and had peace and quiet rather than multiple people jumping down my throat.

Apologies if I came on too hard, it's just you've been expressing this opinion for a while and had gone down several reply chains without bringing the thing to the object level. It's emblematic of the whole question, AI is "spikey", as in it's very good at some things and inexplicably bad at some other things. I don't think a lot of people would take so much offense if you just said it still seems bad at some tasks, that's broadly a consensus. But when you just say it "sucks at code" it's perplexing to the people watching it effortlessly do wide swaths of what used to be core programming work.

I could definitely see it struggle with highly context dependent config files but something seems strange about it not producing at least a valid file, did you try different prompts and giving it different contexts? I find giving it an example of valid output helps but I'm not familiar with fluentd and it's possible giving it enough context is unreasonable.

I have not tried that, but it also seems like kind of a failure of the tool if I have to, you know? The whole point of a tool that can understand natural language is that you can just talk to it normally. If one has to figure out how to word the incantations just right to get a useful result... I'm not sure how that's better than just figuring out the code myself at that point.

Prompting is a skill like any other. Sending it off without context is like telling an underling to fix your config file without explaining or letting them look at the system they're writing it for. It's often a mistake to assume the prompt needs to be something a human would understand. You can and should just dump unformatted logs, barely related examples of working config files, anything you can imagine an underline with infinite time in a locked room might find useful in solving your problem.

FWIW, I appreciate this reply, and I'm sorry for persistently dogpiling you. We disagree (and I wrongly thought you weren't arguing in good faith), but I definitely could have done a better job of keeping it friendly. Thank you for your perspective.

Most frustratingly, the things that I actually need help on, the ones where I don't know really anything about the topic and a workable AI assistant would actually save me a ton of time, are precisely the cases where it fails hard (as in my examples where stuff doesn't even work at all).

That does sound like a real Catch-22. My queries are typically in C++/Rust/Python, which the models know backwards, forwards, and sideways. I can believe that there's still a real limit to how much an LLM can "learn" a new language/schema/API just by dumping docs into the prompt. (And I don't know anything about OpenAI's custom models, but I suspect they're just manipulating the prompt, not using RL.) And when an LLM doesn't know how to do something, there's a risk it will fake it (hallucinate). We're agreed there.

Maybe using the best models would help. Or maybe, given the speed things are improving, just try again next year. :)

Thanks. And for my part I'm sorry that I blew you off unjustly; I really thought I had explained myself in detail but I was wrong.

And yeah, the tech might improve. I imagine you can see why I'm skeptical of the strong predictions that it'll do so (given that I don't agree it's as good as people say it is today), but I try to keep an open mind. It is possible, so we'll see.

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