ruview-quickstart

# RuView Quickstart Get a newcomer from zero to a running RuView sensing dashboard. Three tiers, pick the one that matches the hardware on hand. ## Tier 0 — Docker, no hardware (2 minutes) ```bash docker pull ruvnet/wifi-densepose:latest docker run -p 3000:3000 ruvnet/wifi-densepose:latest # open http://localhost:3000 — simulated CSI, full UI ``` Use this to demo the dashboard, explore the API, or develop UI without a sensor. ## Tier 1 — Build the repo from source ```bash # Rust workspace (1,400+ tests, ~2 min) cd v2 cargo test --workspace --no-default-features # Single-crate sanity check (no GPU) cargo check -p wifi-densepose-train --no-default-features # Python proof (deterministic SHA-256 pipeline check) cd .. python archive/v1/data/proof/verify.py # must print VERDICT: PASS ``` If `verify.py` fails on a hash mismatch after a numpy/scipy bump: ```bash python archive/v1/data/proof/verify.py --generate-hash python archive/v1/data/proof/verify.py ``` ## Tier 2 — Live sensing with an ESP32-S3 ($9) This is the real thing. Hand off to the `ruview-hardware-setup` skill for the flash/provision/monitor loop, then: ```bash # Lightweight sensing server (consumes the ESP32 UDP CSI stream) cd v2 cargo run -p wifi-densepose-sensing-server # Live RF room scan / SNN learning helpers: node ../scripts/rf-scan.js --port 5006 node ../scripts/snn-csi-processor.js --port 5006 ``` ## What to know before you start - **ESP32-C3 and the original ESP32 are NOT supported** — single-core, can't run the CSI DSP pipeline. Use ESP32-S3 (8MB or 4MB) or ESP32-C6. - A **single ESP32** has limited spatial resolution — 2+ nodes (or add a Cognitum Seed) for good results. - Camera-free pose accuracy is limited (~84s to train, modest PCK). For 92.9% PCK@20 use camera-supervised training (see `ruview-model-training` skill, ADR-079). - No cloud, no internet, no cameras required — everything runs on edge hardware. ## Next steps to suggest | Goal | Skill / command | |------|-----------------| | Flash & provision an ESP32 node | `ruview-hardware-setup` · `/ruview-flash` · `/ruview-provision` | | Tune channels / MAC filter / edge modules | `ruview-configure` | | Run a sensing application (presence, vitals, pose, sleep, MAT) | `ruview-applications` · `/ruview-app` | | Train a pose / sensing model | `ruview-model-training` · `/ruview-train` | | Multistatic mesh, tomography, cross-viewpoint fusion | `ruview-advanced-sensing` · `/ruview-advanced` | | Verify the build + generate a witness bundle | `ruview-verify` · `/ruview-verify` | ## Reference - `README.md` — feature matrix, hardware table, install options - `docs/user-guide.md`, `docs/wifi-mat-user-guide.md`, `docs/build-guide.md`, `docs/TROUBLESHOOTING.md` - `docs/tutorials/`, `examples/` — runnable examples (environment, medical, sleep, stress, `ruview_live.py`)