HN Favourite Users (chrome extension)

Hacker News

I've made a simple Chrome extension for Hacker News: https://chrome.google.com/webstore/detail/hn-favourite-users/hleiemhnepoghplnnhkhpnjeiaifnobo It allows you to add your favourite users to a list, and the extension will highlight them here on HN. Simple, but it prevents me from missing comments made by my friends :) The extension is also open source so feel free to fork it: https://github.com/janhancic/hn-favourite-users I'll appreciate any feedback you can give me. Cheers!

Visit product View on Hacker NewsNov 18, 2012

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Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

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

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

Correct prediction on native model

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