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If you are good at code review, you will be good at using AI agents

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192 points imasl42 | 1 comments | 20 Sep 25 04:59 UTC | HN request time: 0.306s | source
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rsynnott ◴[20 Sep 25 10:06 UTC] No.45311963[source]▶
This idea that you can get good results from a bad process as long as you have good quality control seems… dubious, to say the least. “Sure, it’ll produce endless broken nonsense, but as long as someone is checking, it’s fine.” This, generally, doesn’t really work. You see people _try_ it in industry a bit; have a process which produces a high rate of failures, catch them in QA, rework (the US car industry used to be notorious for this). I don’t know of any case where it has really worked out.

Imagine that your boss came to you, the tech lead of a small team, and said “okay, instead of having five competent people, your team will now have 25 complete idiots. We expect that their random flailing will sometimes produce stuff that kinda works, and it will be your job to review it all.” Now, you would, of course, think that your boss had gone crazy. No-one would expect this to produce good results. But somehow, stick ‘AI’ on this scenario, and a lot of people start to think “hey, maybe that could work.”

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Manfred ◴[20 Sep 25 11:04 UTC] No.45312253[source]▶
Reviewing code from less experienced or unmotivated people is also very taxing, both in a cognitive and emotional sense. It will never approach a really good level of quality because you just give up after 4 rounds of reviews on the same feature.
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1. hakfoo ◴[21 Sep 25 22:11 UTC] No.45327142[source]▶
With human co-workers, you can generally assume things you can't with AI.

My human co-workers generally have good faith. Even the developer who was clearly on the verge of getting a role elsewhere without his heart in it-- he tried to solve the problems assigned to him, not some random delusion that the words happened to echo. I don't have that level of trust with AI.

If there's a misunderstanding the problem or the context, it's probably still the product of a recognizable logic flow that you can use to discuss what went wrong. I can ask Claude "Why are you converting this amount from Serbian Dinars to Poppyseed Bagels in line 476?" but will its answer be meaningful?

Human code review often involves a bit of a shared background. We've been working with the same codebases for several years, so we're going to use existing conventions. In this situation, the "AI knows all and sees all" becomes an anti-feature-- it may optimize for "this is how most people solve this task from a blank slate" rather than "it's less of a cognitive burden for the overall process if your single change is consistent with 500 other similar structures which have been in place since the Clinton administration."

There may be ways to try to force-feed AI this behaviour, but the more effort you devote to priming and pre-configuring the machine, the less you're actually saving over doing the actual work in the first place.