From 4f7ad6d2e6b29cf07c1b6f2cfa7b5320ea599017 Mon Sep 17 00:00:00 2001 From: ruv Date: Sun, 1 Mar 2026 11:25:35 -0500 Subject: [PATCH] docs: fix model size inconsistency and add AI Backbone cross-reference in ADR-024 section Co-Authored-By: claude-flow --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 453c226..b6c3187 100644 --- a/README.md +++ b/README.md @@ -162,7 +162,7 @@ Every WiFi signal that passes through a room creates a unique fingerprint of tha - Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room - Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed - Recognizes rooms, detects intruders, identifies people, and classifies activities using only WiFi -- Runs on an $8 ESP32 chip (the entire model fits in 60 KB of memory) +- Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory) - Produces both body pose tracking AND environment fingerprints in a single computation **Key Capabilities** @@ -227,6 +227,8 @@ cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index en | Per-room MicroLoRA adapter | ~1,800 | 2 KB | | **Total** | **~55,000** | **55 KB** (of 520 KB available) | +The self-learning system builds on the [AI Backbone (RuVector)](#ai-backbone-ruvector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top. + See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.