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.
中文简介正在准备中。当前显示英语简介。
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".
安装前可以查看交给 AI 代理的指令内容。
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)还没有评价。欢迎分享你的使用感受。
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
中文简介正在准备中。当前显示英语简介。
Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
中文简介正在准备中。当前显示英语简介。
Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
中文简介正在准备中。当前显示英语简介。
Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
中文简介正在准备中。当前显示英语简介。
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
中文简介正在准备中。当前显示英语简介。
Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
中文简介正在准备中。当前显示英语简介。