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The GitHub/Facebook star beggers.

Hacker News

The GitHub/Facebook star beggers.

It seems we're living in a really fucked up world. As if it weren't enough the ugly people on facebook trying to get stars to inflate their low ego. Now we experiencise it even in the world of software development. I came accross to a really pathetic situation when checking someone's project. https://imgur.com/a/b55pQu0 Screenshot taken from: https://vibora.io/ Amateur developers nowdays add their projects on github in the hope of getting stars like the ugly girls on sexy clothes do usually on facebook. It is really sad that software engineering has turned into such a nasty circus with the help of github.

Visit product View on Hacker NewsJun 21, 2018

Actual performance

1points

2comments

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% 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.

Hacker NewsStrong engagement from HN community · Strong signals: 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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