Developer Builds Dual-Pool AI Code Review System Using Real Engineer Personas

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A developer has created an adversarial AI code review system that simulates feedback from real-world engineers with documented philosophies, rather than relying on generic abstract roles. The system uses two pools: a fixed pool for consistent, depth-focused review and a randomly assembled pool that pulls fresh personas via web search each session to surface blind spots. Two AI managers, modeled on Netflix's Patty McCord and Pixar's Ed Catmull, curate teams of workers tailored to each task's specific needs. In a live test on a real pull request to an 18,700-star GitHub repository, the system surfaced 21 total findings across three rounds, with the random pool identifying issues that both fixed-pool rounds had missed. The project is publicly available on GitHub and was submitted as an installable skill via PR #866.

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