Gitcuss – See Reviews of GitHub Repositories

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I just released first version of my side project, Gitcuss. http://www.gitcuss.com --What is this? Gitcuss is a platform for discussing Github repositories. You can learn more about a project, library, gem or anything available on Github. Or you can leave a comment about it. --Why did I created it? After reading readme files of repos, I always look for last commit date and watch, star, fork counts to evaluate libraries(gems for ruby) then I decide to use it or not. But sometimes thats not enough. I want to learn what people think about it. Thats why I created Gitcuss. It was something I was looking for most of the time. A Chrome plugin will be available soon for better experience. Any feedback would be appreciated. Leave feedback on Gitcuss. http://www.gitcuss.com/r/beydogan/gitcuss

Visit product View on Hacker NewsJan 2, 2015

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Actual performance

6points

8comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: just released, lua, ide · Missing: https docs, excited, exist

56%56% 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 · Strong signals: platform, reviews, soon · Missing: plus, intuitive, host

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

27%27% 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, revenue

11%11% 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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