Re
Recommend HN comments based on your comment history
Hacker NewsRecommend HN comments based on your comment history
I love the HN community. Too many third-party sites republish links to popular HN stories, but readers of those links miss the best part: the discussion on HN. http://hnrecommender.qontex.com/ attempts to find comments and stories you may find interesting based on your comment history. Feedback & ideas more than welcome!
Share cardActual performance
4points
3comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting38%38% 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, 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, revenue20%20% 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, crypto9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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