Adds two new npm packages that expose RuView's WiFi-DensePose
sensing capabilities outside the Cognitum appliance ecosystem:
- tools/ruview-mcp/ (@ruv/ruview-mcp) — MCP server with 6 tools:
ruview_csi_latest, ruview_pose_infer, ruview_count_infer,
ruview_registry_list, ruview_train_count, ruview_job_status.
Uses @modelcontextprotocol/sdk with stdio transport.
6/6 smoke tests pass. TypeScript strict mode, Node 20.
- tools/ruview-cli/ (@ruv/ruview-cli) — Yargs CLI with matching
subcommands: csi tail, pose infer, count infer, cogs list,
train count, job status. Same fail-open pattern as the cog
binaries (WARN to stderr, exit 0 on unavailable sensing-server).
- docs/adr/ADR-104-ruview-mcp-cli-distribution.md — design rationale,
6-row threat table, packaging plan, acceptance gates, failure modes.
- docs/research/sota-2026-05-22/HORIZON.md — 12-hour horizon plan
with 7 milestones tracked (M1 complete in this commit).
Both packages are private:true pending the user's publish decision.
Inference is via subprocess to the signed cog binaries (ADR-100/101/103)
— no JS/WASM ML engine bundled.
This folder contains 44 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
Context — What problem or gap prompted this decision
Decision — What we chose to do and how
Consequences — What improved, what got harder, and what risks remain
References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
