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by rfgplk 2 hours ago
Why is it unreadable? I actually find LLM bug reports/breakdowns to be far more detailed and concise that classical human written ones. If you read the linked repo it clearly goes it depth where the bug was found, how to reproduce it (and in depth). Most disclosures that are human written don't do this at all, they barely even tell you _how_ to reproduce the bug. Just look at the "3.3 The self-store tear", the LLM clearly describes exactly what went wrong, so you can verify it by hand.
2 comments

I'm reasonably confident that the author has a reproduction case on their hands. That's easy to directly verify. I'm way less confident in that long and rambling analysis. As fwlr observes, it's not clear that this has been reproduced off the original machine: https://news.ycombinator.com/item?id=49134357

If it is the case that the original machine has an intermittent hardware fault, that analysis is exactly what I'd expect of an current AI. Voluminous and confidently wrong. Also interesting that the AI itself doesn't note this... having been biased by the conversation to consider kernel bugs, in the "cross-machine data" [1] it blames the kernel and doesn't appear to consider alternate explanations.

It doesn't get mentioned as such around here but one major issue with AIs today is that they fall into tunnel vision very easily and often need some help getting out of it. There's a structural reason for tunnel vision built into their context limits, and there's also situations like this, where if you bias them in a certain direction they can get monomaniacally stuck on it. You can also see it where they do things like try to do something on your system like view a certain file, then if for some reason they fail due to permission issues, if you don't stop them they'll go absolutely insane trying different ways to access the file, rather than just stopping and saying "Hey, I need help". Even if you prompt them directly to do that, it only helps, it doesn't fix the issue. I suspect the RHLF training they get to be better coding agents reinforces this behavior because in the coding quality tests they're rewarded for one-shotting all the various benchmarks, where that "bull in a china shop" approach works better than giving up. But I'd actually like them to give up a bit more often. I've had the same problem with some particularly go-getting junior devs, too... I appreciate the ambition and I look forward to harnessing it in other situations but I'd rather you didn't spend five hours to create a terrible work around to something I could have gotten you in two minutes. For the junior dev, it's OK, they take the feedback and adjust... the AIs never adjust.

[1]: https://github.com/dfoxfranke/ripgrep-3494-analysis#6-cross-...

Because any writing needs a core intent they need to convey, which you can summarize down to according to the audience and why it should be important to them. Kinda like the same idea of elevator pitches, “explain like I’m five” and tactical reports when there’s a time constraints.

You got none of that here. It’s just realms of text.

Maybe the person writing the report isn't an expert in this domain or doesn't have the time to commit to it? From my point of view as long as the information is accurate and reproducible, it's valuable.
The other part is that almost no one would have the energy to spend hours instrumenting and rebuilding musl, reading the kernel mm code, and thinking hard about how they might interact.

Claude is probably not right about the root cause here and is probably bsing, I agree. But it's collected enough raw data to point some expert humans at the right interaction. I'd take this bs bug report and start asking Claude some questions that would guide it to a more plausible actual answer, but that requires a little more experience in kernel development.

> Maybe the person writing the report isn't an expert in this domain or doesn't have the time to commit to it?

Then maybe that person should not do it? At least until they find the time?

> From my point of view as long as the information is accurate

That's the trillion-dollar catch, isn't it. LLMs love to write 30 paragraphs about some plausibly-correct-sounding explanation that is just as likely to be completely fucking wrong as it is accurate. The bug might be real, but that doesn't mean this analysis is accurate, and trying to figure out where the LLM went off the rails can be a nightmare. If you can actually understand the bug, it doesn't take 30 paragraphs to explain it. I would throw this bug report into my junk bin if I were on the receiving end of it, and I say that as someone who will spend days troubleshooting any issue a user will help me diagnose even if it only happens on their machine.

Then where’s the lie in “soulless AI slop”?
I'd rather read walls of AI slop than soulful meatbag bickering. Just because you might have a soul and intent doesn't mean you do anything useful with it. All I see is the intentful invention of more reasons to fight over arbitrary crap.