Smart Reader - native RSS reading app that suggests interesting news

Hacker News

We all use different kinds of RSS feed readers to fetch news, but as the number of feeds we subscribe to grows, we may wake up to 400+ news items every morning, but very few RSS feed readers can suggest to us what to read first. Smart Reader is a Windows RSS reading app that has a built-in machine learning algorithm for suggesting interesting news. It learns by user's labeling of what news are interesting to them, and as the learning progresses, it will start suggesting among the user's subscribed feeds what it thinks the user might find interesting to read first. Smart Reader also tries to provide a consistant, clean view for reading news articles across different domains. Here on the team we have seen some very nice results using Smart Reader. After just one day of training, Smart Reader was able to pick out many interesting articles for us. After about a week of training, the interesting news suggestion has gotten so good for us, we default to reading suggestions before searching all the feeds for interesting news. We would like to share with HN to see what fellow hackers think of this app, and to gather bug reports and insightful feedback. Here are some screenshots: http://dl.dropbox.com/u/69176916/sr1.png http://dl.dropbox.com/u/69176916/sr2.png Here is the readme, with instructions on how to run and train Smart Reader: http://dl.dropbox.com/u/69176916/SmartReaderBeta.pdf Here is the binary for the beta, no installation required, just unzip and run "Smart Reader.exe": http://dl.dropbox.com/u/69176916/SmartReaderBeta.zip The beta app will run till 5/31/12, thanks in advance for all of your time and feedback! You can import your Google Reader subscribed feeds into Smart Reader by importing the OPML file exported from Google Reader.

Visit product View on Hacker NewsApr 26, 2012

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

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

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.

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

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BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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