Qodo just launched the AI Code Review Academy.

It's a free set of chapters from @QodoAI on code review in the era of AI-generated code.

No sign-up required.

I went through the benchmarks and tool comparison chapter. I would recommend it to anyone evaluating review tools right now.

It gives you a way to judge whether a benchmark means anything before you act on it.

• The dataset has to be real production PRs. • Every tool has to run at default settings; otherwise you are measuring tuning effort. • The methodology has to be open enough to rerun yourself. • And precision and recall have to be reported together, so you know how much a tool catches and how much of what it flags is real.

The number that stuck with me came from a 2025 study cited in the chapter. Same model, two evaluations. It scored 84-89% on an isolated benchmark and 25-34% inside a real codebase with dependencies and conventions.

The chapter's best advice is to run every tool you are considering against the same 10-20 pull requests from your own codebase.

Academy: https://www.qodo.ai/academy/?utm_source=x&utm_medium=partner&utm_campaign=academy-launch-2026&utm_content=omarsar0

Thanks, Qodo, for partnering on this post.