Zebra – Social Feed Reader

Hacker News

I build zebra - a cross-over of rss feed reader and micro publishing service. The idea is to make following rss feeds a much more seamless experience and and deeply integrate with a set of social media features. I would call it a community around everyone's media diet and want to create a place for exchange around daily updates about all kinds of topics people are interested in. Making users micro-columnists in their personal newsroom. Essentially you can like and comment on everything you see. zebra tries to enrich rss contents as much as possible and has different representations for the types of sources (forums, articles, blog posts, etc.). It's completely written in flutter and has it's own nodejs backend to collect and serve both feeds and community contents. I'd love to hear all kinds of questions, feedback and will soon release zebra officially on Android Play store after leaving open testing. Publishing an iOS version would be easy, it's just, that i currently lack the necessary hardware.

Visit product View on Hacker NewsOct 6, 2022

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

81%81% 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: ios · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

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

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

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

nativeThis product was originally launched on this platform.

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

22%22% 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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