Joyspace AI Clips – Automatically Create Short Clips from Long Videos

Hacker News

Hi HN, Sagar here from Joyspace AI. We've built a video understanding model that works alongside transcripts to deeply comprehend people, objects, themes, personas, and the core message of a video. Using this model as the foundation, we’ve launched Joyspace AI Clips. Joyspace AI Clips automatically creates short, shareable clips from longer videos like webinars, podcasts, talks, sales calls, Zoom recordings, etc. Our initial testing shows higher accuracy and capturing all key moments from long videos. Right now, our model performs best when there’s a human presence in at least part of the video. It doesn't need to be every frame, but having a person in the video boosts accuracy significantly. We will update our model for other categories such as gaming videos or faceless videos. To be clear, they still work today but accuracy may not be higher. Joyspace AI Clips works quite well with presentation-style content. From what we’ve seen, horizontal videos tend to perform better on social when there's a presentation involved, while vertical videos work better for people-first, face-to-camera formats. All the AI features are currently free while we figure out pricing. We want to keep video transformation as accessible and affordable as possible. We’d love for you to try it out and share any feedback – technical, product, or even just impressions. It would really help us improve. Try it here: https://app.joyspace.ai Website: https://www.joyspace.ai Please try it out and give us feedback. You can submit feedback and feature requests at https://www.joyspace.ai/feedback Looking forward to hearing your thoughts.

Visit product View on Hacker NewsJun 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, recordings, using · Missing: mac, agents, macos

95%95% 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 · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, 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

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 · 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.

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

19%19% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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