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GeoGit – Rank the top GitHub users by location
Hacker NewsGeoGit – Rank the top GitHub users by location
A tool I built that ranks the top GitHub users by location. I'm a student and had never worked with React, so I built this as my first project. There is still lots of features I want to add, but for now it's done. Built with React, Vite and Tailwind. Deployed with Vercel. Also leave a star on the repo! :) https://github.com/sheeehy/Geo-Git-v2
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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos54%54% 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.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting49%49% 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: io · Missing: https docs, excited, just released36%36% 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: users · Missing: mobile apps, ios, personal25%25% 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, revenue18%18% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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