We

We built an Anime Recommendation and streaming Website

Hacker News

We built an Anime Recommendation and streaming Website

Me and my friend built an unique content based Recommendation System, where user can just select Anime or write synopsis and our system will find the most similar anime available. We used Qdrant Vector Database for the Recommendations. Other Features includes, Streaming, Custom watchlist creation and sharing of watchlists. We update our Database regularly and plan to introduce new features in future.

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Actual performance

8points
1comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
69%69% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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