Discover books, movies, apps and songs from your Twitter Feed

Hacker News

Ankit Ranka and I built this for ourselves and it was kind of useful, so we decided to package it up and see if others find it useful too. Ankit is an avid reader, but never new what book to read on the weekend. I'm always trying to find a new movie on Friday night to unwind. I usually end up spending 30-45 minutes looking for a movie, and by the time I find one I don't feel like watching a movie anymore! We built ScoutFeed to monitor our Twitter feeds and help us find books and movies that people we follow tweet about. It turns out people recommend apps and songs on twitter too, so we decided to include those as well. Check it out if you have a second. We'd love to hear your thoughts on the project and look forward to your feedback after you get your first email.

Visit product View on Hacker NewsJun 8, 2012

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

85%85% 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: apps, new, email · Missing: mac, agents, macos

80%80% 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 · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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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