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Comment by merlindru | Hacker News Reader

I like biasing it towards the fact that there is a bug, so it can't just say "no bugs! all good!" without looking into it very hard.

Usually I ask something like this:

"This code has a bug. Can you find it?"

Sometimes I also tell it that "the bug is non-obvious"

Which I've anecdotally found to have a higher rate of success than just asking for a spot check

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Do you not run into too many false positives around "ah, this thing you used here is known to be tricky, the issue is..."

I've seen that when prompting it to look for concurrency issues vs saying something more like "please inspect this rigorously to look for potential issues..."

What's more useful is to have it attempt to not only find such bugs but prove them with a regression test. In Rust, for concurrency tests write e.g. Shuttle or Loom tests, etc.

It would be generally good if most code made setting up such tests as easy as possible, but in most corporate codebases this second step is gonna require a huge amount of refactoring or boilerplate crap to get the things interacting in the test env in an accurate, well-controlled way. You can quickly end up fighting to understand "is the bug not actually there, or is the attempt to repro it not working correctly?"

(Which isn't to say don't do it: I think this is a huge benefit you can gain from being able to refactor more quickly. Just to say that you're gonna short-term give yourself a lot more homework to make sure you don't fix things that aren't bugs, or break other things in your quest to make them more provable/testable.)

That is an unfortunate case you described, but also, git gud and write tests in the first place so you don't need to refactor things down the road.

yes but i can identify those easily. i know that if it flags something that is obviously a non issue, i can discard it.

...because false positives are good errors. false negatives is what i'm worried about.

i feel massively more sure that something has no big oversights if multiple runs (or even multiple different models) cannot find anything but false positives

Just in case you didn't read the full article, this is how they describe finding the bugs in the Linux kernel as well.

Since it's a large codebase, they go even more specific and hint that the bug is in file A, then try again with a hint that the bug is in file B, and so on.

very interesting. i think "verbal biasing" and "knowing how to speak" in general is a really important thing with LLMs. it seems to massively affect output. (interestingly, somewhat less with Opus than with GPT-5.4 and Composer 2. Opus seems to intuit a little better. but still important.)

it's like the idea behind the book _The Mom Test_ suddenly got very important for programming

As a meta activity, I like to run different codebases through the same bug-hunt prompt and compare the number found as a barometer of quality.

I was very impressed when the top three AIs all failed to find anything other than minor stylistic nitpicks in a huge blob of what to me looked like “spaghetti code” in LLVM.

Meanwhile at $dayjob the AI reviews all start with “This looks like someone’s failed attempt at…”

> so it can't just say "no bugs! all good!"

If anyone, or anything, ever answers a question like that, you should stop asking it questions.

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