Personalized Recommendations on Simkl for Movies, TV Shows and Anime

Hacker News

Personalized Recommendations: Tailored Just for You! Ever felt lost in the sea of TV shows, anime, and movies ? We’ve all been there. But guess what? Those days are over. Say hello to Personalized Recommendations. This isn’t just any update; it’s your ticket to a curated watchlist that feels like it was handpicked by your best friend who knows your taste inside out. Uses two separate recommendation engines — each serving up not just tens but hundreds, and sometimes even thousands, of personalized picks to which you can apply your preferred sorting and filters to narrow down exactly what you want to watch at different times. PS: Currently the requires access to Simkl Beta V2 while it's under testing for PRO and VIP plan. The PRO plan is free if you use Simkl for 20 days in a month.

Visit product View on Hacker NewsMay 27, 2024

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Actual performance

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

75%75% 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.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% 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

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

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

12%12% 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

1%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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