From 42492e14a506569d04237768da9bc748076659da Mon Sep 17 00:00:00 2001 From: ruv Date: Sun, 2 Aug 2026 10:56:08 -0400 Subject: [PATCH] docs: collapse secondary README details --- README.md | 46 ++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 40 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 95812489..c3ac6f0d 100644 --- a/README.md +++ b/README.md @@ -235,6 +235,11 @@ The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/ ### Results & proof +See the measured benchmarks, witness records, and one-command reproducibility check. + +
+View benchmark and proof details + | What | Where | Numbers | |------|-------|---------| | **MM-Fi pose model (SOTA)** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30% | @@ -253,8 +258,15 @@ python archive/v1/data/proof/verify.py Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) phases P7–P9 for the camera-supervised fine-tune path. +
+ ### Model weights: what's real, what's not +See which checkpoints are validated, experimental, or architecture-only. + +
+View model maturity details + "WiFi → pose" means three different things in this repo, at three different maturity levels. Read the label, not the headline ([ADR-187](docs/adr/ADR-187-archive-v1-deprecation-honest-labeling.md)): @@ -272,13 +284,17 @@ project can stand behind today is the **MM-Fi benchmark number**, not a live sin number. The path to a first *reproducible* on-device baseline (PCK@20 ≥ 35%) is tracked in [ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) / [#645](https://github.com/ruvnet/RuView/issues/645) — do not advertise the live single-ESP32 17-keypoint feature without the "first-cut, below-target, runtime-stub" caveat until that baseline is measured. +
+ ## 🧩 Edge Module Catalog -
-🧩 105 edge modules ready to install on a Cognitum appliance — live catalog from app-registry.json v2.1.0 (updated 2026-05-13). Browse + install at seed.cognitum.one/store or your local appliance http://<appliance>:9000/cogs. +Add signed modules for health, security, buildings, industry, research, AI, and more. -Each module is a small signed binary (~400 KB) that runs alongside the WiFi-DensePose sensing stack on a Cognitum-V0 appliance. The catalog updates over the air — your appliance fetches it via GET /api/v1/edge/registry ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) and verifies each binary against an Ed25519 signature ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)) before install. +
+Browse the full edge module catalog + +Browse and install modules at [seed.cognitum.one/store](https://seed.cognitum.one/store) or on your appliance at `http://:9000/cogs`. Each module is a small signed binary that runs beside the sensing stack. The appliance updates the catalog over the air and verifies every module before installation ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md), [ADR-102](docs/adr/ADR-102-edge-module-registry.md)). ### 🫀 Health — 14 modules @@ -561,8 +577,12 @@ These scenarios exploit WiFi's ability to penetrate solid materials — concrete --- +## 🧠 Self-Learning WiFi AI + +Learn compact room fingerprints from raw CSI and adapt the model to each environment. +
-🧠 Self-Learning WiFi AI (ADR-024) — Adaptive recognition, self-optimization, and intelligent anomaly detection +View self-learning architecture and commands Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment. @@ -645,7 +665,12 @@ See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-con ## 🧩 Claude Code & Codex Plugin -RuView ships a [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin (and Codex prompt mirror) that wraps the whole workflow — onboarding, ESP32 setup, configuration, sensing apps, model training, advanced multistatic sensing, CLI/API/WASM, mmWave radar, and witness verification — as 9 skills, 7 `/ruview-*` commands, and 3 agents. It lives in [`plugins/ruview/`](plugins/ruview/README.md); the marketplace manifest is [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json) at the repo root. +Use the in-repo plugin for guided setup, sensing, training, and verification in Claude Code or Codex. + +
+View plugin installation and commands + +RuView's [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin and Codex prompt mirror cover onboarding, ESP32 setup, sensing apps, model training, advanced sensing, CLI/API/WASM, mmWave radar, and witness verification. The source lives in [`plugins/ruview/`](plugins/ruview/README.md); the marketplace manifest is [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json). ```bash # In Claude Code — add this repo as a plugin marketplace, then install: @@ -669,12 +694,19 @@ claude --plugin-dir ./plugins/ruview Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full details: [`plugins/ruview/README.md`](plugins/ruview/README.md). -**Portable harness — `npx @ruvnet/ruview`:** a lighter, host-portable companion to the in-repo plugin, minted via [MetaHarness](https://www.npmjs.com/package/metaharness) and hardened per [ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md). It runs **without cloning this repo** and on more hosts (Claude Code, Codex, Copilot, opencode, …), exposing the RuView operator tools (`onboard`, `verify`, `node_monitor`, `calibrate`, `node_flash`) over an MCP server — plus the project's **MEASURED-vs-CLAIMED honesty guardrail enforced in code** (`ruview.claim_check` flags untagged or retracted-"100%" accuracy claims). v0.1: the onboarding/verify/claim-check paths are tested (17/17, `verify.py` → PASS); the hardware tools are fail-closed wrappers. Try `npx @ruvnet/ruview` to onboard, or `npx @ruvnet/ruview claim-check --text "…"`. Source: [`harness/ruview/`](harness/ruview/README.md). +For the portable RuView MetaHarness, use `npx @ruvnet/ruview@0.3.1`; the quick commands and fuller explanation are in the collapsed MetaHarness section near the top of this README and in [`harness/ruview/`](harness/ruview/README.md). + +
--- ## 📖 Documentation +Start with the user, build, and calibration guides; expand for the full reference map. + +
+Browse all documentation + | Document | Description | |----------|-------------| | [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training | @@ -696,6 +728,8 @@ Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full detail | [Medical Examples](examples/medical/README.md) | Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable | | [Extended Documentation](docs/readme-details.md) | Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog | +
+ --- ## 🚧 Beta software