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I made a newsletter that finds AI trends before they're trending
Hacker NewsI made a newsletter that finds AI trends before they're trending
Hey HN I made an AI scraper that scrapes alot of the internet: reddit, twitter, linkedin, tiktok, instagram, google analytics, semrush etc and finds trends before they start trending. So I made it into a newsletter that gives you 3 AI trends before they're trending every week, along with business ideas related to them to your inbox. Totally free - we've got a paid tier where you can get trends specific to a certain industry, keyword or company. Check it out :)
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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, inbox · Missing: mac, agents, macos70%70% 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.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released45%45% 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, podcasting45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive22%22% 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, revenue19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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