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GitCruiter – AI tool that evaluates developers by GitHub activity

Hacker News

GitCruiter – AI tool that evaluates developers by GitHub activity

GitCruiter analyzes public GitHub data to estimate a developer’s technical level and habits. Each profile generates a detailed report with metrics like Algorithmic Thinking, Code Quality, Project Organization, Testing Practices, Documentation, and Problem Solving, plus an overall score (e.g., Senior 80/100). You can browse developers by region, language, or framework — for example, “Python devs in Canada” or “React engineers in Eastern Europe.” We built it to make developer evaluation more transparent — no résumés, no mystery algorithms, just interpretable metrics from real repos and commits. Would love feedback from developers on whether these evaluations feel accurate or how they could be improved.

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: activity, code · Missing: mac, agents, macos
74%74% 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 · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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.

Correct prediction on native model

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