Hacker News Search updated

Hacker News

Based on your feedback (https://news.ycombinator.com/item?id=8874801) we've released a new version of the Hacker News Search. It's accessible through the search box in the footer of Hacker News. We hope you'll enjoy the new minimal design and the additional features. Feel free to give us your dream list of improvements :) ChangeLog: 2015-01-19 - Custom date range picker - Responsive design - New "comments[<>=]X" syntax to filter by number of comments - If the story doesn't have any URL, the link goes to the HN discussion - Here and there cosmetics improvements (less padding, removed useless "upvote" arrow, ...) 2015-01-12 - Full reimplementation based on Angular.js - Performance improvements - Minimal design to improve readability - Customizable sort order (by date/popularity, by date range) - New sharing features (Twitter & Facebook compliant) - Cloudfront-based assets delivering - Experimental UI - Inlined comments - Advanced filtering/refinements capabilities - Thumbnails

Visit product View on Hacker NewsJan 19, 2015

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Actual performance

12points

11comments

Made the leaderboard

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

74%74% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

Correct prediction on native model

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