Hi everyone! 👋
Last idea from me before the gate comes down — and it comes from the graveyard of AI-vision deployments I keep visiting as a self-taught AI builder.
By day I work in retail and F&B operations. AI is my passion: my evenings and weekends go into self-hosted AI monitoring systems running on my own hardware. And when you run cameras for real, you learn the hard truth — the systems almost never die from missing features. They die from the dark beanie that looks like a hard hat. The shadow that looks like a person. Fifty silent false positives a day, and by month one nobody believes the alerts anymore and the system is off.
Every vision project — mine included — shares the same architecture: camera, ONE model, alert. One judge. No internal check on its own confidence.
QUORUM replaces the judge with a council.
Same frame, three experts running in parallel on the Metis AIPU — all built with Voyager Wingman:
1. A detector: "hard hat present, 0.92"
2. A segmentation model: "that shape has no visor — I read beanie, 0.71"
3. A pose/context model: "worker is inside the marked operating zone"
A lightweight moderator on the host compares the verdicts. Agreement passes. Disagreement opens a cross-examination round — conflicting experts challenge each other's evidence — and the final output is a written, motivated verdict:
"Non-compliant: detector read a helmet, but segmentation shows no visor and the worker stands under an active load. Alarm upheld."
A single model can be wrong in silence. A council can't.
Why this matters, concretely:
- Fewer false alarms on the same base models → a system that's still switched on in month one
- A written rationale for every alarm — the thing compliance buyers ask for and never get from a bounding box
- A demo that's actually fun: the deliberation happens live on screen. You watch the experts disagree, argue, and converge.
Why Metis: running 3-4 pipelines in parallel on one AIPU at a few watts is literally the multi-stream job this hardware is designed for. The experts are all standard model-zoo work — detection, segmentation, pose. Wingman builds them in minutes, so the whole month goes into the real novelty: the deliberation layer.
The month: week 1 — experts running side by side. Week 2 — disagreement engine and moderator. Week 3 — the deliberation UI. Week 4 — live demo in a deliberately messy environment, video, and full open-source release with every Wingman prompt.
Why me: I know the pain firsthand from retail operations — alarm fatigue is real, and a written rationale per alarm is what actual buyers ask for and never get. And AI is my passion: I've spent months of evenings and weekends building multi-agent deliberation systems — AI agents debating in structured rounds until they converge on a documented decision. QUORUM is that machinery pointed at vision. The orchestration isn't the risk here; I already run it for fun.
One model can be wrong in silence. A council can't. That's the demo I want to put in front of this community.
Good luck to everyone — some seriously great ideas in this group already!
