Roadmap

Now: the alpha

The alpha exists to find out what breaks on machines that are not the developer's. It has already been worth it: the first release could not finish a clip on any source, and nobody could have known without installing it somewhere clean.

• Shipped. One Windows installer carrying the app, the engine, FFmpeg, the AI runtime and the tracking and transcription models. Releases with notes, a changelog and a known-issues list. Bug reports and feature requests, including an in-app button that needs no GitHub account. Discussions. Security scanning. A container image for working on the engine. This website.
• Being worked on. Code signing, so Windows stops warning that the installer is unsigned. Screenshots and a demo video. Progress reporting for the scoring stage and for update downloads, so working cannot be mistaken for hung. Automated Windows builds.

Next: community

Making it worth someone's time to contribute: good first issues with enough context to pick up cold, contributor recognition, example workflows, community showcases, performance benchmarks, and voting on feature requests.

Contributions are wanted in features, bug fixes, documentation, translations, AI improvements and performance work.

Later

None of this delays the alpha.

• Gaming and split-screen, done properly. The framing has to reliably find where the action is, and tracking has to tell a streamer apart from a character inside the game. Shipping it half-working would produce clips centred on the wrong person.
• Reaction videos. These need the app to understand the video being reacted to, not just the words spoken over it.
• An Android companion app for Twitch, Kick and local files, within Google Play's policies.
• Remote rendering, a plugin architecture, community extensions, creator analytics, and more models and platforms where they earn their place.

The long-term idea

Clips Studio is local-first, and that word carries the whole design: your footage, your workflows and your data stay on your machine. The app gets better as your hardware does, rather than as someone's subscription tier does.

Consumer AI hardware is improving quickly. The architecture is kept modular so newer models and faster machines can be adopted without a redesign. As hardware like the NVIDIA RTX Spark and future AI-accelerated GPUs arrives, the same application should run larger local models, infer faster, analyse video better and automate more.

The goal is an open-source creator platform that becomes more capable over time because local AI hardware does, not one that becomes more expensive.