π RuView

See through walls with WiFi

Turn ordinary WiFi into a spatial intelligence / sensing system. Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.

Works natively with the four major smart-home ecosystems: Home Assistant via the HA-DISCO MQTT publisher, Apple Home & HomePod as a discoverable HAP-1.1 bridge, Google Home + Amazon Alexa via the same HA bridge or a Matter endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.

Drop into any Home Assistant install with one --mqtt flag. Or pair into Apple Home / Google Home / Alexa / SmartThings as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See docs/integrations/home-assistant.md · ADR-115.

π RuView is a WiFi sensing platform that turns radio signals into spatial intelligence.

Every WiFi router already fills your space with radio waves. When people move, breathe, or even sit still, they disturb those waves in measurable ways. RuView captures these disturbances using Channel State Information (CSI) from low-cost ESP32 sensors and turns them into actionable data: who's there, what they're doing, and whether they're okay.

What it senses:

• Presence and occupancy — detect people through walls, count them, track entries and exits
• Vital signs — breathing rate and heart rate, contactless, while sleeping or sitting
• Activity recognition — walking, sitting, gestures, falls — from temporal CSI patterns
• Environment mapping — RF fingerprinting identifies rooms, detects moved furniture, spots new objects
• Sleep quality — overnight monitoring with sleep stage classification and apnea screening

Also included:

• Camera-free pose — estimate 17 body keypoints from WiFi CSI
• Built-in model workflow — record CSI, train models, load RVF files, and switch LoRA profiles
• Local automation — HOMECORE provides state, history, automations, signed Wasm plugins, voice hooks, and HomeKit support
• Unified RF world model — combine WiFi CSI, radar, UWB, and cellular sensing in one privacy-bounded scene model; accuracy is still synthetic until real-data validation
• Governed evidence — attach privacy policy, uncertainty, provenance, and witness records to sensing events
• RuView MetaHarness — use an AI operator to onboard, calibrate, train, verify, and check sensing claims

# Check the local setup and get source-cited guidance
npx @ruvnet/ruview@0.9.1 doctor
npx @ruvnet/ruview@0.9.1 guidance --topic sensing --query "model loading"

# Hardware: what is plugged in, live CSI from your nodes, a 60 GHz radar kit
npx @ruvnet/ruview@0.9.1 devices
npx @ruvnet/ruview@0.9.1 esp32 --watch                       # ESP32 + Realtek RAC1 nodes, live view
npx @ruvnet/ruview@0.9.1 esp32 --seconds 45 --analyze        # live CSI through the vitals kernel
npx @ruvnet/ruview@0.9.1 mmwave --source esphome --host <kit-ip>

# Agents: MCP over stdio, or over HTTP for ChatGPT (token-protected, read tools only)
npx @ruvnet/ruview@0.9.1 mcp start
RUVIEW_MCP_GRANTS=device-access npx @ruvnet/ruview@0.9.1 mcp start --http

# Claude Code: a live sensing pane (/ruview), shipped in the package
npx @ruvnet/ruview@0.9.1 mod

# Run a read-only RuView agent through Codex
npx @ruvnet/ruview@0.9.1 agent run --host codex --repo . \
  --prompt "Find the nearest tests and cite the source files"

# Check claims, replay the deterministic proof, search the reviewed brain
npx @ruvnet/ruview@0.9.1 claim-check --file REPORT.md
npx @ruvnet/ruview@0.9.1 verify
npx @ruvnet/ruview@0.9.1 brain search --query "calibration"

Built on RuVector and Cognitum Seed, RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.

The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.

RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at ruvnet/wifi-densepose-pretrained — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized) and runs in microseconds on a Raspberry Pi. (The [v2 encoder](https://huggingface.co/ruvnet/wifi-