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Revideo – Create Videos with Code

Hacker News

Revideo – Create Videos with Code

Hey HN! We’re building Revideo ( https://github.com/redotvideo/revideo ), an open source framework for programmatic video editing. Revideo lets you create video templates in Typescript and render them with dynamic inputs through an API. It also comes with a <Player /> component that lets you preview your projects in the browser and integrate video editing functionality into web apps. The project is useful for anyone who wants to build apps that automate certain video editing tasks. A lot of companies in the space build their own custom stack for this, like Opus ( https://www.opus.pro/ ), which automatically creates highlight videos from podcasts, or Clueso ( https://www.clueso.io/ ), which lets you create stutter-free product walkthroughs with AI voiceovers. Revideo is based on the HTML Canvas API and is forked from Motion Canvas ( https://github.com/motion-canvas/motion-canvas ), a tool that lets you create canvas animations. While Motion Canvas is intended by its maintainer to exclusively be a standalone application [1], we have turned Revideo into a library that developers can integrate into their apps, while specifically focusing on video use cases. To support this, we have, among other things, added the ability to do headless rendering, made video rendering much faster and added support for syncing and exporting audio. We’re excited about programmatic video editing because of the possibility to automate content creation with AI. One of our users is building StoriesByAngris ( https://storiesbyangris.com/ ), which lets you create video-based RPG stories from language prompts. Other users are marketing-tech companies that help their customers generate and A/B test different versions of video ads. We started to work on video tooling because we ourselves explored a bunch of product ideas in the space of AI-based video creation earlier this year. For example, we built apps that automatically create educational short videos and tinkered with apps that let you create memes. While building these products, we were frustrated with the video editing frameworks we used: Moviepy ( https://github.com/Zulko/moviepy ), which we used initially, doesn’t work in the browser, so we’d often have to wait minutes for a video to render just to test our code changes. Remotion ( https://github.com/remotion-dev/remotion ), which we switched to later, is pretty good, but we didn’t want to rely on it as it is not FOSS (source-available only). We had already followed Motion Canvas for some time and really liked it, so we thought that extending it would get us to something useful much faster than building an animation library from scratch. We initially tried to build Revideo as a set of Motion Canvas plugins, but we soon realized that the changes we were making were too drastic and far too complex to fit into plugins. This is why we ultimately created a fork. We’re unsure if this is the right way to go in the long term, and would prefer to find a way to build Revideo without feeling like we’re dividing the community - if you have experience with this (keeping forks with complex changes in sync with upstream) or other suggestions on how to solve this, we’d love your input. Our current focus is improving the open source project. In the long term, we want to make money by building a rendering service for developers building apps with Revideo. We’d love to hear your feedback and suggestions on what we can improve! You can find our repo at https://github.com/redotvideo/revideo , and you can explore example projects at https://github.com/redotvideo/examples [1] “Motion Canvas is not a normal npm package. It's a standalone tool that happens to be distributed via npm.” - https://github.com/orgs/motion-canvas/discussions/1015

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, tasks · 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
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, video, users · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: exclusive, soon, users · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money, audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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