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An RSS aggregator with productivity and retention in mind

Hacker News

An RSS aggregator with productivity and retention in mind

Hello HN ! I'm the founder of https://symfoz.com which is basically a RSS aggregator that allows people to partition the content they want to see in dedicated spaces called "Playlists", thus reducing the time needed to find the information they need. Why Symfoz instead of other RSS aggregators ? - Create playlists of content, avoid mixing too many feeds in the same place. You know where everything is. - Follow people or playlists, people can create some playlists with AWESOME content, you may benefit from it. - We handle classic RSS feeds, but also Youtube channels, Twitter feeds, Spotify Podcasts, sub-reddits. - An easy UI interface (This is still WIP, UI is very hard haha) - Promote retention of information by allowing people to take notes (not highlighting but really take notes), instead of just consuming content and forget it. You'll be able to find your notes easily. A bit more about the project (you can skip this!), this is a side project that I've been working on for almost a year. I'm a back-end developer and the stack runs in Python. If you'd like more details, please feel free to ask, I'll be glad to answer ! Thanks for taking the time to read this, feel free to give any feedback, I would be really grateful :) !

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Actual performance

3points
2comments
Did not reach leaderboard

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: notes · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
32%32% 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
15%15% 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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