Nibbl – I moved 30 newsletters out of my inbox into one swipeable feed

Hacker News

I had 30+ newsletters across gmail and outlook and was missing most of them. They'd get buried under promotions, spam, whatever. Tried filters, labels, separate folders. Always fell apart after a week. The key insight: the problem isn't the newsletters, its the inbox. Newsletters shouldn't live in your email at all. So I built Nibbl. You get a free @nibbll.app email address and subscribe to newsletters with that. They land in the app, not your inbox. You can add RSS feeds too so everything lives in one place. Articles show up as cards you swipe through. Right to save, left to skip. My partner started calling it "tinder for articles" and honestly thats exactly what it is. Gets through a morning's worth of reading in like 5 minutes instead of 30. Also added AI summaries (Gemini) for when you just want the key points without reading the full thing. One tap, done.

Visit product View on Hacker NewsFeb 27, 2026

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Launch Intel predictions

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

88%88% 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.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, para, gemini · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, 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

44%44% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

21%21% 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.

Correct prediction on native model

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