GitRanks – GitHub Profile Analytics and Rankings

Hacker News

Hi HN, I built GitRanks because I’ve always wanted a clear, fun way to see how my open-source work compares - both globally and right here at home. The app pulls in GitHub data to give every developer an at-a-glance “scorecard”, then rolls those into live leaderboards so you can: - Track rankings by stars, contributions and followers - Filter worldwide vs. your own country - Refresh daily - no waiting weeks to see your progress - Discover rising devs you’ve never heard of and follow their work Tech Stack: - Back: MongoDB | NestJS | BullMQ | GraphQL - Front: Next.js | Tailwind | Shadcn Data: - 6.1M profiles - 0.7M organizations - 12.5M repositories Try it https://gitranks.com/ GitHub repo https://github.com/gitranks/gitranks-ui This is just v1, and I’d love your thoughts! What metrics or filters would make the leaderboards even more useful? Anything unclear or missing? I’ll be here answering questions and would really appreciate critical feedback. Thanks for checking it out!

Visit product View on Hacker NewsJul 20, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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: organizations · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% 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 · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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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