Where should OpenStream use LFM2.5-VL beyond face grounding?

[system1970]

OpenStream currently uses LFM2.5-VL for face grounding: sample a frame, ask the model for a normalized face box, and use that box to avoid covering the creator with captions or overlays.

That is useful for the MVP, but I think it is probably a weak flagship use case for LFM2.5-VL by itself. Face/person boxes are a solved, latency-sensitive problem where dedicated detectors will usually be faster, cheaper, and more stable. The more interesting role for LFM2.5-VL is not just "draw a box around a face"; it is using visual understanding to make creator tooling more context-aware.

Potential stronger use cases:

• Scene-aware caption layout: understand where important objects, faces, screens, hands, products, or whiteboards are and place captions/graphics where they do the least damage.
• Automatic stream chapters: sample frames over time and suggest segments like intro, coding, demo, debugging, Q&A, or closing.
• Thumbnail and highlight discovery: identify expressive frames, clear product shots, UI moments, or visually distinct scene changes from a recording.
• Visual clip search: ask questions like "find the part where I showed the dashboard" or "find the section with the terminal error".
• Contextual overlays: trigger overlays based on what is visible, not only on spoken words. Example: show a repo badge when a GitHub page is on screen.
• Privacy and safety checks: flag visible emails, API keys, private chats, personal documents, faces in the background, or accidental screen leaks before publishing clips.
• Product/logo/object awareness: detect when a brand, sponsor, app, device, or physical object is visible and apply the right caption/graphic treatment.
• Accessibility metadata: generate alt text, scene descriptions, and visual summaries for exported clips.
• Visual Q&A over recordings: let creators ask questions about their own footage after a session.
• Editing assist: summarize shot changes, repeated dead-air sections, visual glitches, camera obstruction, or moments where the speaker leaves frame.

For OpenStream, I think the right direction is to keep face grounding as a simple baseline, but design the LFM layer as a general "visual context engine". The model should be able to answer structured questions about frames, return normalized regions when useful, and feed that information into layout, overlays, recording review, and editing workflows.

Questions for contributors:

1. What VLM-powered workflows would make this worth running locally during a stream?
2. Which tasks need realtime behavior, and which can run asynchronously after recording?
3. Should face/person detection eventually move to a dedicated fast detector while LFM handles higher-level visual reasoning?
4. What structured outputs should OpenStream standardize beyond bounding boxes?