AI
AI Reddit Curator – A better Reddit homepage
Hacker NewsAI Reddit Curator – A better Reddit homepage
Why did my wife and I make this site? We want to make browsing Reddit more educational, hilarious, inspiring, and extremely cute. We use Claude 3 Haiku to rate content from Reddit and you can change the rating category to "educational," "hilarious," "cute," or "political." That way you can see the best of the best while avoiding the worst (ragebait or doomscroll'y). The website is available in web and Android!
Share cardActual performance
6points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude · Missing: mac, agents, macos75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way, education · Missing: mobile apps, ios, personal50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue24%24% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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