HackerNew for Chrome

Hacker News

I've been working on and off on a Chrome Extension for Hacker News, the extension focusses on improved usability rather than design. I can almost guarantee that after a day you won't want to go back to regular Hacker News. Give it a try, features include: - Improved readability - Quick inline replies from comments page - Quick profiles with social network info when hovering over usernames - Filtering of stories based on terms and phrases / domain or user - Endless scrolling - Collapsible comment threads - Social sharing for Twitter, Facebook, Google+, Buffer Forks, improvements and comments welcome: https://github.com/tommoor/HackerNew

Visit product View on Hacker NewsMay 7, 2012

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Actual performance

150points

89comments

Made the leaderboard

Launch Intel predictions

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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.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

54%54% 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 · Strong signals: google · Missing: mobile apps, ios, personal

26%26% 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

13%13% 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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