docs(readme): link ecosystem badges + move Beta callout to bottom
Three operator-feedback corrections to the README:
1. Every ecosystem badge in the top row now links to a real
destination — Home Assistant -> integrations/home-assistant.md,
Matter -> ADR-122, Apple Home -> user-guide-apple-homepod.md,
Google Home + Alexa -> the HA integration doc (both ecosystems
reach RuView through HA's bridge today). Added an Alexa badge
alongside the existing four so all four major ecosystems are
represented. Dropped the now-redundant separate "HomePod
Integration" badge — the Apple Home badge linking to the same
guide is enough.
2. Beta callout moved from line 14 (under the hero image) to a
dedicated `## Beta software` section immediately before the
License. The callout's content is unchanged; it just no longer
gates the elevator pitch. Readers see the value proposition
first, the caveats at the bottom alongside license + support.
3. The intro paragraph ("Turn ordinary WiFi into ...") now ends
with a one-line summary of native ecosystem support naming all
four — Home Assistant, Apple Home & HomePod, Google Home, Alexa —
plus the Matter endpoint, each linked. The previous mention of
ecosystems was buried further down the page; this surfaces it
in the intro where the user reads first.
Co-Authored-By: claude-flow <ruv@ruv.net>
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@ -11,18 +11,13 @@
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</a>
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</p>
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> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
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> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
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> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
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> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.
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>
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> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
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## **See through walls with WiFi** ##
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**Turn ordinary WiFi into a spatial intelligence / sensing system.** Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.
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   [](docs/user-guide-apple-homepod.md) 
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Works natively with the four major smart-home ecosystems: **[Home Assistant](docs/integrations/home-assistant.md)** via the HA-DISCO MQTT publisher, **[Apple Home & HomePod](docs/user-guide-apple-homepod.md)** as a discoverable HAP-1.1 bridge, **[Google Home](docs/integrations/home-assistant.md)** + **[Amazon Alexa](docs/integrations/home-assistant.md)** via the same HA bridge or a [Matter](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.
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[](docs/integrations/home-assistant.md) [](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) [](docs/user-guide-apple-homepod.md) [](docs/integrations/home-assistant.md) [](docs/integrations/home-assistant.md)
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> Drop into any **Home Assistant** install with one `--mqtt` flag. Or pair into **Apple Home / Google Home / Alexa / SmartThings** as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See [`docs/integrations/home-assistant.md`](docs/integrations/home-assistant.md) · [ADR-115](docs/adr/ADR-115-home-assistant-integration.md).
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@ -607,6 +602,15 @@ Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full detail
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---
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## 🚧 Beta software
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> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
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> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
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> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
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> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.
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>
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> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
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## 📄 License
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MIT License — see [LICENSE](LICENSE) for details.
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