RuView: Major Streaming Engine and World Model Integration

Transcript

Good morning. This is RuView for May 30th, 2026.

Three major pull requests merged yesterday, bringing substantial new capabilities to the platform.

Ruvnet merged the RuView Streaming Engine implementation, adding over 14,000 lines across 67 files. This massive update implements architectural decision records 135 through 146, establishing data contracts, trust and privacy machinery, and algorithms that convert WiFi channel state information into auditable semantic beliefs. The system now provides full traceability from signal evidence to sensor agreement and calibration provenance.

The second merge introduced empty-room baseline calibration per ADR-135. This feature records 30 seconds of stationary CSI data and provides per-subcarrier baselines for amplitude and phase measurements. The calibration system integrates with motion detection and provides deviation scoring to filter downstream processing stages.

The third major merge added world model integration through ADR-147, introducing a new crate called wifi-densepose-worldmodel version 0.3.0. This creates an async Rust client that communicates with an OccWorld Python inference server running a 72.4 million parameter TransVQVAE model. The integration includes Kalman filter enhancements that extend prediction horizons from 5 to 15 frames.

Seven additional commits followed, including GCP GPU provisioning scripts for 8-GPU training clusters, a complete retraining pipeline for domain adaptation, and Candle Rust ports of the world model components. Performance benchmarks show 208 millisecond median inference times with 3.98 gigabytes of VRAM usage.

What's next: The foundation is now in place for real-time environmental intelligence processing, and GPU infrastructure is ready for large-scale model training.

That's RuView for today. I'm your host, back tomorrow with more developer updates.