Audience
Multimodal model teams, agent teams, safety reviewers
Chris Royse field notes
The Meeting Bot Is A Multimodal Alignment Problem
A real-time avatar has to preserve voice, face, expression, timing, conversation state, and meeting latency all at once.
Media / alldata.md / 7:49
Core idea
The hard part is not making an avatar move. The hard part is keeping every modality in distribution while it responds live.
Phoenix + VoiceAgent
Watch on YouTube· 7:49
The Meeting Bot Is A Multimodal Alignment Problem
This is why the constellation framing matters beyond a demo: a live agent needs runtime checks, not just a trained prior.
Watch videoOpen the full video on YouTubeWhat to take from it
The videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.
Identity drift can occur in face, voice, affect, or timing.
Latency pressure should not remove verification.
A meeting bot needs explicit consent and scope controls.
Continue this thread.
Related notes stay inside the same problem area first, then move to the next useful context.
Watch + read / 14:09
ClipCannon Turns Video Into Inspectable Data
ClipCannon breaks video into transcripts, frames, scenes, emotion, speaker, prosody, highlights, storyboards, and provenance.
Watch + read / 13:09
An AI-Native Editor Starts With Analysis
The editor only works because the system already knows scenes, transcript timing, narrative flow, captions, crops, and render constraints.
Watch + read / 7:57
0.961 SECS Is A Pipeline Result
The measured voice result comes from reference selection, full ICL, best-of-N scoring, centroid enrollment, and quality gates.
Make it concrete.
Send the audience, data type, target task, proof bar, and sharing limits.