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GitTrends – Find out when you're trending on GitHub

Hacker News

GitTrends – Find out when you're trending on GitHub

Hey HN! We're a team of two Pakistani brothers who like to build things. Every year we have a tradition of hacking together a fun project on the side, to learn and solve a niche problem. The idea of GitTrends came from the open-source work that we do in our daily jobs. We noticed how difficult it was to see when our repositories or developers were trending. For an event that can cause such an impact on open-source work, we thought there should be a dedicated solution. So we built it! GitTrends allows you to search a database of GitHub Trending data that we collect every 5th minute. With a small fee, you can also subscribe for email alerts for a specific repository or Github username that you are interested in observing. Knowing that your watched repository or username is trending can significantly give you a burst of popularity on your repository or developer profile, which you can then take advantage of. For an open-source developer looking for work, or a small project that needs more eyes, getting on trending can be a game-changer. As a note, we started collecting data in August 2022. We are looking for volunteers who would help us load historical data (i.e. from the BigQuery GitHub Dataset) to our database. We've launched on ProductHunt as well, so an upvote there would be massively helpful -> https://www.producthunt.com/posts/gittrends . Thank you!

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Actual performance

5points
5comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, open · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
66%66% 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 · Missing: mobile apps, ios, personal
48%48% 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
23%23% 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
16%16% 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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