Refeed – Open Source RSS Reader

Hacker News

Hey HN! I've been working on an Open Source RSS Reader called Refeed. RSS Readers have been around for quite a while now, but honestly, they haven't changed much in the last 10 years. With Refeed I hope to change that and iterate on what makes RSS readers so great. I've been working on Refeed for the last year with the intention of making it the best RSS reader out there, I don't think I'm quite there yet but I think Refeed is at a point where some people might like it more than what's already out there. Over the next 6-12 months I intend to get Refeed to the point of being the best RSS reader out there and after that, I have a ton of ideas on ways I can iterate on the modern RSS Reader. Would be great if you guys could try it and give feedback on it!

Visit product View on Hacker NewsFeb 29, 2024

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Did not reach leaderboard

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

86%86% 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: open source, ide, io · Missing: https docs, excited, just released

76%76% 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: open · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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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