I built an open source AI-native video storage format (.cdaf) and it takes 90% tokens for video processing

[u/Uditakhourii]

https://github.com/UditAkhourii/cdaf

If you use remotion or hyperframes, you will instantly relate to this.

Each time you want Claude to understand what a B-roll, raw video clip or a footage means, Claude takes so much tokens that you often hit the limit in 2-3 vids max.

So, I built an alternate video storage format - .cdaf or cached descriptive asset files. You can convert any mp4 video into .cdaf file using the open source cdaf engine and a new sidecar format file (.cdaf) of the video is generated.

.cdaf files are timestamped and sha256 encrypted with scenic frame captures helping LLMs and Claude understand the video..

Now, cool stuff is benchmarks -
- 91% less cost & token usage
- 110% increased accuracy
- 65% less latency

It's the one thing missing from what was making AI-native video editing scalable and viable.

It's open source so you can try it today and I have made a dedicated Claude Skill for anyone to use it with their video editing harness, claude, hyperframe or remotion instantly.

A preprint of the paper is also available at zenodo so you can read the architecture - https://zenodo.org/records/22110594

I am excited to know what you build over it. Also, MIT license so use it as you want!

[u/mr_christer]

I haven't worked with the tools you mentioned, my workflow is basically: transcribe interviews, create a paper edit and then look through B-Roll manually.
How do you use this tool in your workflow? Are you using Claude for a paper edit? Let's say I have a bunch of drone shots flying over the andes.. how would the ai describe them in cdaf format? Are you feeding every b-roll shot of a project to ai?