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GitStars - A better way to organize your GitHub starred repositories

Hacker News

GitStars - A better way to organize your GitHub starred repositories

Just released a simple web tool, GitStars (http://www.gitstars.com) to help make the act of starring repositories on GitHub infinitely more useful. When you log into GitStars, your starred GitHub repositories are fetched and automatically tagged. You can freely edit the tags and help make the system's auto tagging better for everyone. In this early version, we were focused on helping you re-discover the repositories that you may have starred and lost track of. You can browse or search the repositories by the tags that were automatically generated. I would love to hear your comment on whether you find GitStars useful.

Visit productView on Hacker NewsAug 30, 2012
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
11%11% predicted probability of success on Indie Hackers, 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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