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HubStar, the missing "star" feature for GitHub repositories

Hacker News

HubStar, the missing "star" feature for GitHub repositories

After the recent HN discussion[1] about what's wrong with GitHub's "Watch" feature, I was spurred to finish a pet project I put about 8 hours into a month ago. HubStar[2] is the missing "star" feature for GitHub repositories. The idea is you log into the site using GitHub's OAuth, download a browser extension (currently only Chrome is available), and you can now star repositories directly from their GitHub pages. The links are stored remotely on the HubStar site for later searching. Prior to this I was using the GitMarks Chrome plugin, but my main problem with it was that it only showed me the name of the projects I bookmarked. If I later needed to find one of them, but only remembered what it did and not what it was called, I'd have to open up a ton of old links trying to find it. HubStar fixes this by showing you the description for the repository so you can search on that as well. Further search filters, such as programming language, may be available in the future. There's also a social aspect to the site. You can see the number of other people who have starred a repository, and I have future plans to let people view all of the users who starred a given repository, and all of the repositories starred by a given user. The source for both the site itself and the Chrome plugin is available on GitHub[3]. I hope people find it useful and I'd love to have some contributions from other people. [1] http://news.ycombinator.com/item?id=3640856 [2] http://hubstar.herokuapp.com/ [3] https://github.com/tsigo/hubstar

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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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