I build a free feed reader that reimagines what is possible with RSS

Hacker News

I've been obsessed with feed readers for 20 years (RSS, Atom, JSONFeed, OPML, etc), and I've spent the last 5+ years trying and failing to convince my friends & family to escape the algorithm. But I couldn't find the right setup that met them where they were: scrolling a timeline that looks & feels like social media. So I built that. HyperTexting is your news feed minus the ads, algorithms, and AI-generated slop. Today's release is all about the subscriber experience. But the bigger picture vision for HyperTexting is to become a tool for publishing AND subscribing to text-, image-, audio-, and video- feeds. I should have more to say about that in a few weeks. In the interim, I hope y'all will give HyperTexting a try!

Visit product View on Hacker NewsMar 25, 2026

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

83%83% 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: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

64%64% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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 · Strong signals: audio · 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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