The signal-to-noise ratio on PRs has definitely tanked since everyone started hooking up basic LLM scripts to their repos. Discarding the low-effort ones is a good first step, but the long-term solution is evaluating PRs structurally against historical incidents and performance impact. At CloudThinker, we focused our AI code review engine purely on this deep, incident-aware context—catching vulnerabilities and regressions automatically so human reviewers only spend time on architecture.