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HN-Brief – Catch up on the top stories in 5 minutes

Hacker News

HN-Brief – Catch up on the top stories in 5 minutes

Hi HN, I’ve built HN-Brief, a self-updating static digest of Hacker News. It is a static daily digest that fetches the top 20 stories summarizing both the article content and the community discussion (digest) into a cohesive 5-minute read. How it works: A Cloudflare Worker runs on a cron schedule, fetches stories via the Algolia API, generates markdown summaries, and commits them directly to GitHub. Cloudflare Pages then serves the static site. While excellent tools like hndigest.com exist for email subscriptions, HN-Brief is a web-first alternative that is fully open source. Source: https://github.com/jnd0/hn-brief I’d appreciate feedback on the summary quality, reading experience and the UI!

Visit productView on Hacker NewsDec 29, 2025
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, open · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, 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: exist, open source, hacker news · Missing: https docs, excited, just released
62%62% 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
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
19%19% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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