News Pulse – Real-time global news feed, 475 sources, no algorithm

Hacker News

Former investigative reporter turned developer. I built a simple breaking-news monitor because tracking events across platforms is a mess now that Twitter’s unreliable. Bluesky is the backbone (a fraction of twitter, but still lots of journalists and OSINT folks), plus RSS, Telegram, Reddit, YouTube and Mastodon. Everything is one chronological feed with no algorithm, clear source labels, and lightweight activity detection when a region spikes above baseline (frequency math, not LLMs). But we do have a (hopefully non-obtrusive) AI-generated recent/post summary. Been building for a while, figured I'd post in light of today's events. Can't promise it will survive or is good, any feedback appreciated. Built with Next.js 15 + TypeScript + Tailwind on Vercel. Real coded + supervised vibe coded. Free, no login, signups, ect https://pulse-osint.vercel.app/

Visit product View on Hacker NewsFeb 28, 2026

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

75%75% 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, activity, code · Missing: mac, agents, macos

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

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% 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 · Strong signals: plus, platform · Missing: intuitive, reviews, host

32%32% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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