Folo – an RSS reader that summarizes timeline and sends daily AI digest

Hacker News

I’ve used RSS heavily for years (and maintain RSSHub https://news.ycombinator.com/item?id=24213682 ). I love the control RSS gives, but the “1,000+ unread” problem is real, and most readers don’t help with discovery or filtering noise. So I built Folo, a modern open-source RSS reader ( 36k stars on GitHub: https://github.com/RSSNext/Folo ) that adds optional AI features without taking control away: What it does: - Timeline summaries: Daily “TL;DR” of everything new. https://cdn.follow.is/onboarding/3.1.mp4 - AI search/discovery: Ask for feeds on any topic. https://cdn.follow.is/onboarding/2.1.mp4 - Digest routines: Auto-summaries emailed each morning. https://cdn.follow.is/onboarding/3.2.png - Article summaries & Q&A: Get key points or ask follow-ups. https://cdn.follow.is/onboarding/3.4.png - Podcast/Video transcription: Read long episodes in minutes. Even if you ignore the AI, it’s a great reader: - Native RSSHub support (Twitter, Telegram, Instagram, GitHub, Hacker News, etc.) - Specialized views for articles, social posts, images, and videos - Built-in newsletter inbox Available on desktop web: https://app.folo.is Would appreciate feedback.

Visit product View on Hacker NewsNov 24, 2025

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Launch Intel predictions

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

79%79% 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, inbox, email · 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, 000 · Missing: https docs, excited, just released

60%60% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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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