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egoFile RSS reader sorts, highlights, summarizes item by your interests

Hacker News

egoFile RSS reader sorts, highlights, summarizes item by your interests

Yeah, I know, another RSS reader, but I figured an automatically personalized RSS reader could do a lot more than just magic sort items. It also tries to group related items. I put this together last week and was curious if anyone else finds it worthwhile. The algorithms can always be improved but I'm finding even dumb methods to be a lot more useful than nothing. I'd like to know if anyone finds it useful, or has suggestions, so I can justify improving it. It's a webapp at http://www.egofile.com/ You can paste your OPML subscription list into the feed search box.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
13%13% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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