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Ask HN: What tools are you using for human code review of AI-assisted code?

8•dafelst•13h ago
A good proportion of us and our colleagues are now churning out agent-assisted code at an incredible rate, with some of it that is actually good, and a lot that is not so good. I'm personally finding that the real quality gate for our projects is now how thoroughly the generated code was human reviewed to ensure that it is not just correct, but architecturally sensible.

AI code review tools like coderabbit and copilot, or even pointing claude code at a PR are all generally pretty good at finding bugs and style nits, but less good at finding duplicate code, module cross coupling, bad separation of concerns, and so on, even if prompted to do so.

I'm finding that github's PR interface is not really cutting it for me, it was janky even when the reviews were small, but now at the size they're at, it is becoming unmanageable. Add to that the extra noise of mixing in agent reviews, and people "meat-proxying" in copy-pasted agent output, and it's getting pretty noisy and difficult to navigate.

What have you all found that works well for streamlining human review of AI assisted code? Tools and process suggestions are welcome.

Comments

mandarinclips•1h ago
I use Gemini most of the time when using different AI tools because it's free.I occasionally use grok build.And I rarely do code reviews anymore.
taurath•36m ago
I’m considering a set of skills that I can apply common lenses to PRs (metrics, error handling, testability + tests, architecture, security), and fetch the code and pr details via the GitHub CLI or an MCP. This seems like it might get absurdly expensive, but maybe w the right tuning it’ll be possible to run most of them on smaller models. Each would let me zoom in on its area and help me through the process of reviewing.

The problem is with full on misses that I’d be able to catch by going line by line. The more static analysis and style enforcement the better - I’m still not sure whether it will come out as a benefit, esp when you consider cost of tokens. AI often creates its own extra work alongside the benefits, and it’s a bit like nicotine in that it works for a minute but then you’ll need to keep applying it to even return to baseline.