Get RuView running with a Docker demo using simulated WiFi signal data, source checks, or live sensing with supported hardware. Choose a starting path based on available equipment and open a working sensing dashboard.
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".
Review the instructions the agent will receive before you install.
Get a newcomer from zero to a running RuView sensing dashboard. Three tiers, pick the one that matches the hardware on hand.
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.
# 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
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
ruview-model-training skill, ADR-079).| 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 |
README.md — feature matrix, hardware table, install optionsdocs/user-guide.md, docs/wifi-mat-user-guide.md, docs/build-guide.md, docs/TROUBLESHOOTING.mddocs/tutorials/, examples/ — runnable examples (environment, medical, sleep, stress, ruview_live.py)No reviews yet. Tell others what you thought.
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