Show HN: AI PM Evaluation Framework (Open Source) (aipmframework.com)

An engineer has open‑sourced an AI Product Manager (PM) evaluation framework inspired by a podcast conversation between Aakash Gupta and Jaclyn Konzelmann (Google’s Director of AI Product). What began as a personal self‑assessment of Jaclyn’s clear, rigorous criteria became a public, criteria‑based toolkit aimed at helping people—especially those from non‑traditional backgrounds—measure and develop the skills needed to be an AI PM. The repo frames concrete questions around what to build, where to focus, and how to position yourself for AI PM roles, and invites community edits and contributions on GitHub under an explicit “share the safe routes” philosophy. For the AI/ML community this matters because it standardizes an actionable competency model for a role that sits between product, data, and engineering—reducing ambiguity in hiring, onboarding, and career growth. Technically, the framework functions as an assessment checklist and roadmap rather than a research paper: it operationalizes product judgment, domain knowledge, and execution priorities so candidates and teams can identify gaps and signal readiness. By open‑sourcing the criteria, the project lowers entry barriers, encourages inclusive feedback loops, and can evolve into a de‑facto industry reference for evaluating AI product expertise.