Start using RuView by choosing a Docker demo, a source build, or a live ESP32 setup. The skill guides the next concrete step, distinguishing sample data playback from sensing an actual room with WiFi signals.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Zero-to-sensing path picker for RuView (WiFi-DensePose) — pick docker-demo, repo-build, or live-esp32 and run the next concrete step.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Get a newcomer from nothing to a working RuView setup. First fact to set: WiFi
sensing infers coarse pose/presence/breathing from Channel State Information — it
is not a camera, and any accuracy number must be MEASURED against a baseline
(use the verify skill / ruview_claim_check tool). Never present WiFi output as
camera-grade.
Run ruview_onboard {path} or decide from:
docker run -p 8000:8000 ruvnet/wifi-densepose → open http://localhost:8000.
Use to see what it looks like.cd v2 && cargo test --workspace --no-default-features
(1,031+ tests pass), then cargo run -p wifi-densepose-cli -- --help.provision-node skill), point it at
the sensing-server, then calibrate-room. This is the only path that senses a real room.ruview_claim_check on any
report before you quote a number.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
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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.
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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.
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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.
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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.
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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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.