You didn’t do it enough. They stop finding bugs eventually. Also, different models can find different bugs (though they do find the same ones, too, which is good and expected). For best results you want to run multi model reviews in loops.

If you had multiple people look at your PRs multiple times on different days results would be very similar.

> There is never a “Nothing found, good to go!”. I have to keep reminding myself that the model will always give me what I ask for, regardless of the reality/truth.

Tell it something like:

  Before doing any commits or producing a summary for the user, you must run a verification sub-agent.
  Its goal is to adversarially and critically check your supposed findings to look out for false positives and hallucinations.
  Doing so with a separate sub-agent with relatively clean context (but with all the relevant details of the problem space that appear to be facts) should improve our confidence in the findings.

    Try to classify each found issue as either SERIOUS, CRITICAL or NITPICK, discard nitpicks, we only care about impactful issues.

I've largely found the same in regards to generating code - the initial pass will often have bugs that the model itself can find but only when run as a separate sub-agent without the confidence poisoning in its own previous output.

You need to create review skill and there define what "issue" or "good" are for you to limit sensitiviness. Otherwise you depend on model's random threshold or non of such then you get perfection chasing.

Anyway it will never match your judgemend completely unless you upload your brain dump into model.

You get the same result if you pay humans a good sum of money to find issues.

> There is never a “Nothing found, good to go!”

Not entirely true IME. Eventually the bug hunt will end with general design advices that may not be suitable to your use case and that you can skip.

> There is never a “Nothing found, good to go!”.

Like when you do recursive programming, have you tried providing more/better stop conditions? If you literally just say "Continue until there are no more issues" then it'll do just that, but if you scope it better, like "Only mention issues related to X, Y or that leads to Z" and so on, you'll get less noise and more focus on issues that actually matter (to you).

If I keep running the same prompt, it will keep finding issues.

I've had the same experience, but whenever I've reviewed what it finds it's basically right. It's pedantic, and a lot of the problems aren't things I really care about, but they definitely are real problems.

I'm not sure you can blame the AI for always finding problems if a) you asked it to, and b) there are problems to find.

There is a point of diminishing returns though; the issues suggested will get speculative, or point out comment unclarity, or "defense in depth". But I agree it’s somewhat annoying to rarely get clear pushback in terms of "no, this looks good enough to me, release it"

I use Claude Code and one of the steps in my workflow is do a review loop until no issues are found and it never loops. So my experience is entirely different. Even if I say: fix all issues. So not only the critical issues.

You could ask the model to say "nothing found" if the improvement was stylistic, or other constraints.

You need to run them in review loops, this is the only way to reduce or eliminate these issues.

I think this was true with older models, but at least with GPT 5.5 it can genuinely tell you "no issues found" after a few passes of finding real issues.

That's just plain wrong. The new models do not hallucinate as much as they used to (in my personal experience)

What do you mean? Are they valid flaws or not?

Would you like it to stop when there's still flaws in the code?

It's not eagerness to please (that's anthropomorphising), rather it's a desire to bill you more money/use more tokens

(The fixed prices are just temporary discounts)

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