A community going deep on how video AI and VLMs actually work in practice, with project ideas if you want to build

I work on VideoDB (data infrastructure for video) and we run a community focused on the practical side of video AI and VLMs: not just headlines, but what actually breaks, how to evaluate these models properly, and what the configuration decisions actually matter for.

We share field notes and research: how to build eval sets from real failures (not just benchmarks), why configuration often beats model choice, how to structure video data so it is searchable and agent-usable. There is also a running list of project ideas for people who want to build but are not sure where to start.

Good fit if you are learning how video AI actually works under the hood, or want to make something practical with it.

Link to the community is in the first comment. Also dropping a writeup there on how we evaluate VLMs on video tasks if you want a sample of what we share.