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A rich daily digest feed for Hacker News
Hacker NewsA rich daily digest feed for Hacker News
I made a feed that contains rich information from every article (title, description, image), and also from the Hacker News discussion. The feed is very convenient for reviewing Hacker News content weekly and picking what to read out of the myriad of articles. The backend is Ruby on AWS Lambda, with the brilliant [metainspector gem]( https://github.com/metainspector/metainspector ) to extract rich content.
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released60%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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%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, crypto14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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