Showing a trained policy to someone (recruiters, teammates, a booth) meant re-entering the full inference config (checkpoint, robot, cameras) from scratch every time, with no simple live camera preview separate from the full recording UI.

## What changed

- New `/demo` page: one click loads the **last successfully started inference config** (policy, robot, cameras) as a preset from `localStorage` — no re-configuration needed right before a demo.
- New `lelab/rollout_frames.py`: a thin wrapper around `lerobot.scripts.lerobot_rollout` that runs inference exactly as before, but also tees each camera frame to a JPEG on disk (~10fps). Served through a new `/demo-camera/{cam_id}` endpoint for a live, low-latency preview on the demo page.

```mermaid
flowchart LR
    A["InferenceModal:\nstart inference"] -->|saves preset| B["localStorage\npolicy + robot + cameras"]
    B --> C["/demo page\none-click preset"]
    C --> D["lerobot_rollout subprocess\nvia rollout_frames.py wrapper"]
    D -->|tees JPEG ~10fps| E["%TEMP%/lelab_demo_frames"]
    E --> F["GET /demo-camera/cam_id"]
    F --> G["Live preview\non /demo page"]
```

## Dead end tried and reverted

Real-Time Chunking (`--inference.type=rtc`) was tried to remove a periodic ~200ms stall from ACT's chunk recompute. This lerobot version's RTC engine calls `predict_action_chunk(inference_delay=...)`, which `ACTPolicy` doesn't accept — RTC targets flow-matching policies (pi0, smolvla), not ACT. Reverted cleanly to the standard sync engine; left a comment in the code for whoever revisits ACT-specific prefetching.

## Testing

Run against a checkpoint trained on ~250 episodes: verified the live preview updates (279 frame updates observed in one run) and that the preset correctly restores policy/robot/cameras without manual re-entry.