Semantic search for video

Hacker News

Hello HN Over the New Year's break, I created semanticvideosearch.com. This can search any video based on meaning and context. I would love to get your feedback on it. What should I change and what can be improved? The preprocessed videos can be search very quickly, while the youtube video links take some time (yt videos also have a upper duration limit due to compute issues). I intend to add search based on the frames of the video soon. I would love to know your thoughts on the demo and any suggestions for improvements. Thanks! PS: the inspiration to create this was to get the 2 mins of content from youtube videos with 18 other mins of fluff.

Visit product View on Hacker NewsJan 4, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context · Missing: mac, agents, macos

84%84% 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 · Strong signals: soon · Missing: plus, platform, intuitive

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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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