From 43ac76a17fb7cefd593a84d4be2adacb7c356e89 Mon Sep 17 00:00:00 2001 From: rUv Date: Tue, 19 May 2026 18:49:33 -0400 Subject: [PATCH] docs(readme): rewrite hero paragraph in plain language (#652) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous version listed every artifact format, every pending integration, and every not-yet-released model — useful as a status log but not as a what-this-system-does sentence for a first-time reader. Replaces it with a single paragraph that answers: - What does it do? (turn WiFi into a contactless sensor) - What hardware? ($9 ESP32) - What does it tell you? (who's there, breathing, heart rate) - How small is the model? (8 KB q4 fits anywhere) - What does it NOT need? (no cameras / wearables / phone apps) Everything that got removed — pending wiring, JSONL-vs-binary RVF, the 17-keypoint pose follow-up, the heuristic-fallback caveat — is already covered in dedicated sections later in the README (the Capability table, the Pretrained Model section, the Edge Module Catalog) and in #509 / ADR-079. The hero paragraph isn't the right place for the engineering caveat tour. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2b65edc8..998eb73d 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](htt The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain. -RuView **ships pretrained CSI weights on Hugging Face** at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — a self-supervised contrastive CSI encoder (128-dim embeddings, 12.2M training steps, 60K frames) + a presence-detection head reporting 100% accuracy on the validation set + per-node LoRA adapters. Models are released as `.safetensors`, 4-bit/8-bit/2-bit quantized `.bin` (4 KB–16 KB), and a JSONL RVF container. The Python training and evaluation tooling consumes these today via `safetensors`. **Pending wiring**: the sensing-server's `--model` flag still expects binary RVF, so live-server consumption of the JSONL bundle is gated on a JSONL adapter (or a re-publish in binary RVF) — see [Pretrained model on Hugging Face](#-pretrained-model-on-hugging-face) below for the workaround. **Not yet released**: a 17-keypoint pose-estimation model — training pipeline is implemented (WiFlow + AETHER + MERIDIAN heads) but camera-supervised fine-tune phases P7–P9 of [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) are `Pending`, tracked in [#509](https://github.com/ruvnet/RuView/issues/509). The live sensing server therefore drives the on-screen output from signal-based DSP heuristics today. +RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized), runs in microseconds on a Raspberry Pi, and reports 100% presence accuracy on the validation set. No cameras, no wearables, no app on the user's phone. ### Built for low-power edge applications