问HN：你们在进行AI辅助代码的人为审查时使用了哪些工具？
我们团队中的许多人现在都在以惊人的速度生成代理辅助的代码，其中一些代码质量确实不错，但很多则不尽如人意。我个人发现，我们项目的真正质量门槛在于生成的代码经过人类审查的彻底程度，以确保它不仅是正确的，而且在架构上也是合理的。
像 coderabbit 和 copilot 这样的 AI 代码审查工具，甚至将 Claude 代码指向一个 PR，通常在发现错误和风格问题方面表现良好，但在发现重复代码、模块间耦合、关注点分离不当等方面则不太理想，即使被提示去做。
我发现 GitHub 的 PR 界面对我来说并不够好，尽管在审查较小的代码时已经显得有些笨拙，但现在随着审查规模的扩大，变得越来越难以管理。再加上混入代理审查的额外噪音，以及人们在复制粘贴代理输出时的“肉体代理”，使得整个过程变得非常嘈杂且难以导航。
你们发现有什么有效的方法来简化 AI 辅助代码的人类审查吗？欢迎分享工具和流程建议。
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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.<p>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.<p>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.<p>What have you all found that works well for streamlining human review of AI assisted code? Tools and process suggestions are welcome.