Meet Geisha

Hacker News

Meet Geisha

For the past couple of months, I’ve been working with a total stranger, @ahmetsulek. We’ve never met in real life, and our communication has been restricted to Skype. And that’s Skype call, not video. Whilst it’s advised that one should only team up with somebody they’ve known for a long time, I’ve found this not to be necessary. Instead, all you need is a dose of mutual respect and a clear partnership agreement. We’ve been working together for the past couple of months on a web app and Chrome Extension that aggregates sites like Hacker News, Designer News, Sidebar.io, Behance and Dribbble into one place. Why? To make an easier browsing experience. Meet http://geisha.io Let us know what you think. @williamchanner

Visit product View on Hacker NewsFeb 28, 2014

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Actual performance

33points

21comments

Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, 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: hacker news, ide, io · Missing: https docs, excited, just released

72%72% 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · 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

24%24% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real life · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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