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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.
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