AI PM Evaluation Framework (Open Source)

Hacker News

Why I Built This [This conversation]( https://www.youtube.com/watch?v=rcz39Y0qFxw ) between [Aakash Gupta]( https://x.com/aakashg0 ) and [Jaclyn Konzelmann]( https://x.com/jacalulu ) (Google's Director of AI Product, on his [podcast]( https://www.news.aakashg.com/s/product-growth-podcast )) truly inspired me. Jaclyn's AI PM evaluation framework was so clear and rigorous that I immediately thought: "I need to measure myself against this." "In disaster relief, you don't map safe routes and hide them. You share them." I started outlining her criteria as a self-assessment. But then I realized: this shouldn't be a private checklist. So this became a framework with a specific purpose: give everyone, especially people from non-traditional backgrounds, an actionable guide on what to build, where to focus, and how to position themselves for AI PM roles. If you have edits or ideas on how to improve please submit them on [GitHub]( https://github.com/abe238/aipm-resume-analyzer ).

Visit product View on Hacker NewsOct 29, 2025

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Actual performance

2points

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, open · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, ide · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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