ReadHn - Reading list for top HN posts

Hacker News

A reading list for top Hacker News posts grouped monthly. Shows top posts by month with read/unread tracking. Set a monthly goal (15, 25, 50), mark posts as you go, see your progress. Features: - Monthly view sorted by points (surfaces the best, not just the newest) - Mark as read/unread, or hide permanently - Syncs across browsers via your own private GitHub Gist - No server, no account, no tracking – just open the HTML file - Works offline, state persists in localStorage Try it out here: [ https://www.readhn.top/ ]

Visit product View on Hacker NewsDec 25, 2025

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Actual performance

12points

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.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide · 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: month, monthly · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

27%27% 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

16%16% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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