Quick Start & Installation
Relevant source files
The following files were used as context for generating this wiki page:
RuView (WiFi-DensePose) provides camera-free human sensing by analyzing WiFi Channel State Information (CSI). The project offers multiple entry points ranging from zero-hardware Docker demonstrations to live deployments using ESP32-S3 or ESP32-C6 hardware.
Sources: plugins/ruview/agents/ruview-onboarding-guide.md:12-25, AGENTS.md:10-15
Installation Tiers
RuView categorizes setup into three distinct tiers based on available hardware and technical requirements.
Tier 0: Docker (No Hardware)
This tier uses simulated CSI data to demonstrate the dashboard and API functionality without requiring physical sensors.
- Pull the latest image:
docker pull ruvnet/wifi-densepose:latest - Run the container:
docker run -p 3000:3000 ruvnet/wifi-densepose:latest - Access the UI at
http://localhost:3000
Sources: plugins/ruview/skills/ruview-quickstart/SKILL.md:11-17
Tier 1: Repository Build
Build the project from source to run the Rust workspace or verify the Python reference pipeline.
- Rust Workspace:
cd v2 cargo test --workspace --no-default-features - Python Proof:
python archive/v1/data/proof/verify.py
Sources: plugins/ruview/skills/ruview-quickstart/SKILL.md:21-29
Tier 2: Live Hardware Sensing
This tier involves flashing firmware to ESP32 nodes to capture real RF perturbations.
- Flash ESP32-S3 or C6 hardware using the
ruview-hardware-setupskill. - Start the sensing server:
cargo run -p wifi-densepose-sensing-server - (Optional) Run SNN learning helpers:
node scripts/snn-csi-processor.js --port 5006
Sources: plugins/ruview/skills/ruview-quickstart/SKILL.md:37-46
Environment Setup & Prerequisites
Successful installation requires specific software versions and hardware compatibility.
| Requirement | Supported / Version | Notes |
|---|---|---|
| Microcontrollers | ESP32-S3, ESP32-C6 | ESP32-C3 and original ESP32 are NOT supported. |
| Rust | 1.85+ | Required for v2/ workspace. |
| Node.js | 18+ (Mobile), 20+ (Desktop) | Required for harnesses and UI. |
| Python | 3.x | Required for reference pipeline and provisioning scripts. |
| Operating System | Windows, macOS, Linux | ESP-IDF builds on Windows use Python-subprocess patterns. |
Sources: plugins/ruview/skills/ruview-quickstart/SKILL.md:48-52, v2/crates/wifi-densepose-desktop/README.md:101-105, ui/mobile/README.md:73-78
System Component Architecture
The following diagram illustrates the relationship between the sensing server, hardware nodes, and various user interfaces.
The sensing server ingests UDP packets from hardware nodes and distributes processed data to frontend clients.
Sources: harness/ruview/src/tools.js:46-52, ui/mobile/README.md:144-168, v2/crates/wifi-densepose-desktop/README.md:27-44
Tooling & Verification
RuView provides a contributor harness (@ruvnet/ruview) to manage the repository and verify installations.
Contributor Harness Usage
The harness includes a doctor command to diagnose the local environment.
npx @ruvnet/ruview@0.5.0 doctor
npx @ruvnet/ruview@0.5.0 guidance --topic architecture
Sources: AGENTS.md:39-47
Implementation Verification
To ensure a successful installation, run the deterministic witness verification. This tool asserts that the pipeline produces expected results from a controlled input.
# Via the harness tool
ruview_verify --repo .
The verify script checks for a VERDICT: PASS message, which indicates the cryptographic hashes match the project reference.
Sources: harness/ruview/src/tools.js:145-160, plugins/ruview/skills/ruview-verify/SKILL.md:13-17
Mobile & Desktop Client Setup
Mobile App (iOS/Android/Web)
The mobile client is built with React Native and Expo.
- Navigate to the directory:
cd ui/mobile - Install dependencies:
npm install - Start the web preview:
npx expo start --web
Sources: ui/mobile/README.md:18-22
Desktop App (Tauri)
The desktop application requires the Tauri v2 CLI and Rust.
- Navigate to the UI folder:
cd v2/crates/wifi-densepose-desktop/ui - Install dependencies:
npm install - Run in development mode:
cargo tauri dev
Sources: v2/crates/wifi-densepose-desktop/README.md:109-114
Summary
RuView installation is structured to allow quick evaluation via Docker before moving to full source builds and hardware deployments. All sensing is performed at the edge without requiring cloud connectivity. Once installed, users typically proceed to hardware provisioning or model training to refine spatial resolution and accuracy.
Sources: plugins/ruview/agents/ruview-onboarding-guide.md:20-25, plugins/ruview/skills/ruview-quickstart/SKILL.md:54-65