π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.
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See through walls with WiFi📖 Documentation🚧 Beta software📞 SupportQuick startAdvanced brokersReferences6. References6. Alternatives considered10. Referencesv0.0.1 shipping status — 2026-05-198. ReferencesLinksEvidence and LinksRelated7. ReferencesTraining a ModelHardware SetupPre-Trained Models (No Training Required)Further Reading2. Python Pipeline (v1/)3. Rust Pipeline (v2)1. Context13. ReferencesLatest Additions📦 Installation📡 Signal Processing & Sensing📄 Changelog
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me what the models do resources from RuView"
Installation instructions →What's inside
Related
Resources
10. References
v0.0.1 shipping status — 2026-05-19
Pre-Trained Models (No Training Required)
7. References
- A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi SensingWiFi Sensing and CSI Embeddings
- Barlow Twins: Self-Supervised Learning via Redundancy ReductionContrastive Learning Foundations
- BYOL: Bootstrap Your Own LatentContrastive Learning Foundations
- CLIP: Learning Transferable Visual Models From Natural Language SupervisionContrastive Learning Foundations
- Diffusion Model-based Contrastive Learning for Human Activity RecognitionSelf-Supervised Learning for Time Series
- DINO: Emerging Properties in Self-Supervised Vision TransformersContrastive Learning Foundations
Further Reading
- CMU DensePose From WiFi
The foundational research paper
🚧 Beta software
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