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wifi-densepose
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docs(readme): rewrite hero paragraph in plain language (#652) 

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.
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@ -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. 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 ### Built for low-power edge applications