/ruview-quickstart

name: ruview-quickstart
description: Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine".
allowed-tools: Bash Read Write Edit Glob Grep

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)

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

# 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:

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:

# 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`)