HN Buffer – A read-it-later site for your HN favorites

Hacker News

Hello! I’ve been reading Hacker News for years and have a bad habit of favoriting articles but never actually going back to read them. I finally built hnbuffer to scratch my own itch and help me work through that backlog. I also mainly built this as an excuse to learn/use some of the languages/frameworks/tools since I have another backlog of things I wanted to try too. It syncs your favorites into a queue with a reader mode and swipe gestures. The streak tracking is something I'm still experimenting with. Note: It requires an HN login to scrape the favorites list (credentials are client-side only, not stored). Let me know what you think!

Visit product View on Hacker NewsNov 22, 2025

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% 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: new · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

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

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

Incorrect prediction on native model

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