Open-source AI video editor

Hacker News

Hey HN community! I'm one of the lead devs of this project at fal.ai and we created an open source lightweight video editor powered by the latest media AI models. The main goal was to tackle some challenges when dealing with complex media handling and encoding on the browser. It all started as an internal experiment but as we tackled some of the issues it was clear there could be some value sharing it with the open source community. Some of the key points and tech stack details: - It uses IndexedDb, so all data is local (i.e. no auth, no cloud db) - Multiple AI models for video, image, music and voice-over. APIs are provided by fal.ai - Built with the typical React+Next.js, Shadcn front-end - Used remotion.dev for the realtime video preview (this is such a great project, without it the codebase would be twice as large) - File uploads so you can bring your own media by uploadthing.com - ffmpeg for encoding the final video and also some ui tricks, like the audio waveform We deployed a version of it and for now it's free to use. We do plan to add some rate limiting and a bring your own API Key next, but it's open source and I'm curious about what the community will build on top of it, or derive from it. Customize your own video app and if you do, please share. If you have any questions, hit me up!

Visit product View on Hacker NewsJan 23, 2025

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

96%96% 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 · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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: open source, ide, io · Missing: https docs, excited, just released

40%40% 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

19%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: audio · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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