March 2026 — RuView (ruvnet/RuView) advanced sensing, model integration, and deployment readiness through a focused set of dense-pose capabilities, training pipelines, and documentation enhancements. Core features delivered and stabilized, with a clear path to production and experimentation: - Rust sensing server with a DensePose-compatible API enabling real-time perception workflows. - ADR-021 vital sign detection and RVF container format for standardized data packaging (closes #45). - ADR-023 full DensePose training pipeline implemented across Phases 1-8 to accelerate end-to-end model development. - Docker images, RVF export capability, and README updates to streamline deployment and onboarding. - Training mode, ADR docs, and vitals/wifiscan crates to expand experimentation capabilities and maintainable integrations.

February 2026 performance snapshot focusing on business value and technical achievements across Ruflo, RuVector, and RuView. Major milestones include a stable product release, WASM integration across RVF components, IoT/CSI pipeline progress, and packaging/reliability enhancements that lay groundwork for scale and future capabilities.

January 2026 was marked by multi-repo improvements that unlock faster deployment, better performance, and stronger code quality across RuView, Claude-Flow, and RuvVector. We delivered developer-facing documentation and packaging, performance-oriented memory-tool upgrades, and cloud-ready AI/chat capabilities, all while hardening stability and formatting across the codebase. The month set the foundation for scalable AI deployment, smoother onboarding, and more reliable service delivery for end-users.

June 2025 monthly summary for RuView (ruvnet/RuView). This month focused on delivering end-to-end WiFi-DensePose capabilities, stabilizing core data processing, strengthening UI and documentation, and validating the system for production readiness. Key outcomes include API and system integration for WiFi-DensePose, core implementation with performance results, CSI processing with unit tests and batch support, UI styling with dark mode, and comprehensive docs plus system validation. Bug fixes addressed README badges and async engine poolclass configuration to improve reliability and onboarding.