5 changed files with 196 additions and 275 deletions
--- a/.github/workflows/clone-tracking.yml
+++ b/.github/workflows/clone-tracking.yml
@ -1,149 +0,0 @@
 name: GitHub Clone Tracking → data/clone-data.rvf
 # Persists rolling 14-day clone-traffic snapshots to data/clone-data.rvf in
 # the ruvector JSONL RVF format. GitHub's /traffic/clones endpoint only
 # retains the last 14 days server-side, so without this scheduled scrape
 # the data is gone forever the moment it falls outside the window.
 #
 # Format: JSONL RVF
 #   - line 1 is a `metadata` segment that initializes the file
 #   - each subsequent run appends one `clone_snapshot` segment carrying the
 #     14-day rollup PLUS per-day breakdown
 #   - file is idempotent: per-day entries are keyed by `timestamp` so a
 #     downstream reader can dedupe across overlapping snapshot windows
 #
 # Schedule: every 14 days (1st + 15th of each month, ~14-day cadence in
 # practice). Workflow can also be dispatched manually for backfill or test.
 on:
  schedule:
    # 01:23 UTC on the 1st and 15th of every month — close to 14-day cadence
    # without cron's "every 14 days" monthly-reset weirdness. Picking :23
    # avoids the cron herd on :00.
    - cron: '23 1 1,15 * *'
  workflow_dispatch:
 permissions:
  contents: write
 concurrency:
  group: clone-tracking
  cancel-in-progress: false
 jobs:
  snapshot:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Fetch /traffic/clones + /traffic/views from GitHub
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          mkdir -p data
          gh api repos/${{ github.repository }}/traffic/clones > /tmp/clones.json
          gh api repos/${{ github.repository }}/traffic/views > /tmp/views.json
          echo "--- clones rollup ---"
          jq '{count, uniques, days: (.clones | length)}' /tmp/clones.json
          echo "--- views rollup ---"
          jq '{count, uniques, days: (.views | length)}' /tmp/views.json
      - name: Append snapshot to data/clone-data.rvf
        env:
          REPO: ${{ github.repository }}
        run: |
          set -e
          RVF="data/clone-data.rvf"
          FETCHED_AT=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
          # Initialize the file with a metadata segment on first run.
          if [ ! -f "$RVF" ]; then
            echo "Initializing $RVF with metadata segment"
            jq -n --arg repo "$REPO" --arg ts "$FETCHED_AT" '{
              type: "metadata",
              name: "ruview-clone-traffic-history",
              version: "1.0.0",
              schema: "ruvector.rvf.jsonl/v1",
              format: "github-traffic-snapshots",
              repo: $repo,
              source: "GitHub Traffic API /repos/{repo}/traffic/{clones,views}",
              policy: "GitHub retains only 14 days server-side; this file is the long-term record.",
              segments: ["metadata", "clone_snapshot", "view_snapshot"],
              created_at: $ts,
              custom: {
                cadence: "twice monthly (1st and 15th, ~14-day intervals)",
                idempotency_key: "timestamp (per-day records de-duplicate across overlapping snapshot windows)"
              }
            }' >> "$RVF"
          fi
          # Append the clone snapshot.
          jq --arg ts "$FETCHED_AT" '{
            type: "clone_snapshot",
            fetched_at: $ts,
            window_count: .count,
            window_uniques: .uniques,
            per_day: .clones
          }' /tmp/clones.json >> "$RVF"
          # Append the views snapshot (free with the same auth).
          jq --arg ts "$FETCHED_AT" '{
            type: "view_snapshot",
            fetched_at: $ts,
            window_count: .count,
            window_uniques: .uniques,
            per_day: .views
          }' /tmp/views.json >> "$RVF"
          echo "--- RVF tail (last 4 lines) ---"
          tail -4 "$RVF" | jq -c '{type, fetched_at, window_count, window_uniques}' || true
          echo "--- file size ---"
          wc -l "$RVF"
      - name: Compute aggregates for the commit summary
        id: agg
        run: |
          # Count distinct per-day entries across all snapshots so we can
          # show "cumulative observed clones" in the commit message.
          python3 - <<'PY'
          import json, os
          path = "data/clone-data.rvf"
          per_day_clones = {}
          per_day_views = {}
          with open(path, encoding="utf-8") as f:
              for line in f:
                  if not line.strip():
                      continue
                  d = json.loads(line)
                  if d.get("type") == "clone_snapshot":
                      for entry in d.get("per_day", []):
                          per_day_clones[entry["timestamp"]] = entry
                  elif d.get("type") == "view_snapshot":
                      for entry in d.get("per_day", []):
                          per_day_views[entry["timestamp"]] = entry
          tot_clones = sum(e.get("count", 0) for e in per_day_clones.values())
          tot_uniq_clones = sum(e.get("uniques", 0) for e in per_day_clones.values())
          tot_views = sum(e.get("count", 0) for e in per_day_views.values())
          tot_uniq_views = sum(e.get("uniques", 0) for e in per_day_views.values())
          print(f"clone days observed: {len(per_day_clones)}  total clones: {tot_clones:,}  total unique cloners: {tot_uniq_clones:,}")
          print(f"view  days observed: {len(per_day_views)}  total views:  {tot_views:,}  total unique viewers:  {tot_uniq_views:,}")
          with open(os.environ["GITHUB_OUTPUT"], "a") as out:
              out.write(f"clones={tot_clones}\n")
              out.write(f"clone_days={len(per_day_clones)}\n")
              out.write(f"views={tot_views}\n")
              out.write(f"view_days={len(per_day_views)}\n")
          PY
      - name: Commit + push if changed
        run: |
          git config user.name "github-actions[bot]"
          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
          if git diff --quiet data/clone-data.rvf; then
            echo "no changes to commit"
            exit 0
          fi
          git add data/clone-data.rvf
          git commit -m "chore(traffic): clone snapshot — ${{ steps.agg.outputs.clone_days }} days observed → ${{ steps.agg.outputs.clones }} clones, ${{ steps.agg.outputs.view_days }} view-days → ${{ steps.agg.outputs.views }} views"
          git push
--- 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 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.
+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.
 ### Built for low-power edge applications
@ -45,27 +45,26 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
 [![Vital Signs](https://img.shields.io/badge/vital%20signs-breathing%20%2B%20heartbeat-red.svg)](#vital-sign-detection)
 [![ESP32 Ready](https://img.shields.io/badge/ESP32--S3-CSI%20streaming-purple.svg)](#esp32-s3-hardware-pipeline)
 [![crates.io](https://img.shields.io/crates/v/wifi-densepose-ruvector.svg)](https://crates.io/crates/wifi-densepose-ruvector)
 [![Downloads](https://img.shields.io/badge/downloads-10M%2B-brightgreen.svg)](#-edge-module-catalog)
-> | What | How | Speed / scale |
+> | What | Status | How | Speed |
-> |------|-----|---------------|
+> |------|--------|-----|-------|
-> | 🫁 **Breathing rate** | Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6–30 BPM, real-time |
+> | 🫁 **Breathing rate** | ✅ Works today | Bandpass 0.1-0.5 Hz → zero-crossing BPM, circular variance on wrapped phase ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6-30 BPM |
-> | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |
+> | 💓 **Heart rate** | ✅ Works today | Bandpass 0.8-2.0 Hz → zero-crossing BPM | 40-120 BPM (needs good SNR) |
-> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained), 100% validation accuracy) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
+> | 👤 **Presence detection** | ✅ Heuristic in server · 🤗 Trained head on HF (loader wiring pending) | Live server uses phase-variance vs adaptive threshold (60 s ambient calibration). A trained `presence-head.json` reporting 100% validation accuracy is published in [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) but the sensing-server's `--model` loader only accepts binary RVF today — JSONL adapter pending. | <1 ms heuristic |
-> | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |
+> | 🧬 **CSI embeddings** | 🤗 Trained encoder on HF | 128-dim contrastive encoder, **164,183 emb/s** on M4 Pro. Usable today from Python / training via `model.safetensors`; sensing-server consumption pending the same JSONL loader gap as above. | 8 KB q4 fits ESP32 SRAM |
-> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)) | 8.4 ms cold-start on a Pi 5 |
+> | 🚶 **Motion / activity** | ✅ Works today | Motion-band power + phase acceleration | Real-time |
-> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
+> | 🤸 **Fall detection** | ✅ Works today | Phase acceleration > threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
-> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
+> | 🧮 **Multi-person slot count** | ⚠️ Heuristic, not learned | Subcarrier diversity divided by 2 (capped). **Not** a learned counter — see [firmware README](firmware/esp32-csi-node/README.md#tier-2--full-pipeline-stable) "Tier 2 caveats". Adaptive normalisation in [#491](https://github.com/ruvnet/RuView/pull/491). | Real-time |
-> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
+> | 🦴 **17-keypoint pose estimation** | 🔬 Pipeline only, no shipped weights | Training infrastructure complete (WiFlow + AETHER + MERIDIAN heads); the published HF model is presence + embeddings, not keypoints. Tracked in [#509](https://github.com/ruvnet/RuView/issues/509). | Pending data collection |
-> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |
+> | 🧱 **Through-wall sensing** | ✅ Works today | Fresnel zone geometry + multipath modeling | Up to ~5m signal-dependent |
-> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |
+> | 🧠 **Edge intelligence** | ✅ Works today | Optional Cognitum Seed for persistent vector store + kNN + witness chain | $140 total BOM |
-> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |
+> | 🎯 **Camera-free pre-training** | ✅ Shipped weights on HF | Self-supervised contrastive encoder, 12.2M training steps on 60K frames. See [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained). | 84 s/epoch retrain on M4 Pro |
-> | 📷 **Camera-supervised fine-tune** | MediaPipe + ESP32 CSI paired training, end-to-end Candle pipeline on RTX 5080 ([ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md)) | 2.1 s for 400 epochs (~5 ms/epoch) |
+> | 📷 **Camera-supervised fine-tune** | 🔬 Pipeline only | MediaPipe + ESP32 CSI paired training, [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md). Target **35%+ PCK@20**. P7–P9 (data + train + eval) `Pending`. | ~19 min/epoch on laptop |
-> | 📡 **Multi-frequency mesh** | Channel hopping across 6 bands, TDM slot scheduling ([ADR-029](docs/adr/ADR-029-multifrequency-mesh.md)) | 3× sensing bandwidth |
+> | 📡 **Multi-frequency mesh** | ✅ Works today | Channel hopping across 6 bands, TDM slot scheduling (ADR-029) | 3x sensing bandwidth |
-> | 🌐 **3D point cloud fusion** | Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model | 22 ms pipeline · 19K+ points/frame |
+> | 🌐 **3D point cloud fusion** | 🔬 Reference impl | Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model. Requires camera. | 22 ms pipeline · 19K+ points/frame |
 >
-> Browse the full 105-module catalog (with practical descriptions, sizes, and difficulty) below in [🧩 Edge Module Catalog](#-edge-module-catalog), or visit [seed.cognitum.one/store](https://seed.cognitum.one/store).
+> Legend: ✅ shipped + tested on hardware (some have learned weights on [HF](https://huggingface.co/ruvnet/wifi-densepose-pretrained), others are deterministic DSP) · ⚠️ ships and runs, but is a heuristic/threshold (not a learned classifier) — accuracy depends on calibration · 🔬 implementation + tests in repo, weights/data/eval pending
 >
 > 🤗 **Pretrained weights**: download from [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — see [Loading the pretrained model](#loading-the-pretrained-model) below for one-command setup.
@ -97,7 +96,7 @@ node scripts/mincut-person-counter.js --port 5006  # Correct person counting
 >
 > | Option | Hardware | Cost | Full CSI | Capabilities |
 > |--------|----------|------|----------|-------------|
-> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |
+> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence indicator, motion, breathing rate, heart rate, fall detection, slot-count multi-person heuristic + persistent vector store, kNN search, witness chain, MCP proxy. (Pose pending weights — see [#509](https://github.com/ruvnet/RuView/issues/509).) |
 > | **ESP32 Mesh** | 3-6x ESP32-S3 + WiFi router | ~$54 | Yes | Same capabilities as above without the persistent-memory features |
 > | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |
 > | **Any WiFi** | Windows, macOS, or Linux laptop | $0 | No | RSSI-only: coarse presence and motion (see [tutorial #36](https://github.com/ruvnet/RuView/issues/36)) |
@ -438,6 +437,178 @@ These scenarios exploit WiFi's ability to penetrate solid materials — concrete
 </details>
 <details>
 <summary><strong>🧩 Edge Intelligence (<a href="docs/adr/ADR-041-wasm-module-collection.md">ADR-041</a>)</strong> — 60 WASM modules across 13 categories, all implemented (609 tests)</summary>
 Small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response. Each module is a tiny WASM file (5-30 KB) that you upload to the device over-the-air. It reads WiFi signal data and makes decisions locally in under 10 ms. [ADR-041](docs/adr/ADR-041-wasm-module-collection.md) defines 60 modules across 13 categories — all 60 are implemented with 609 tests passing.
 | | Category | Examples |
 |---|----------|---------|
 | 🏥 | [**Medical & Health**](docs/edge-modules/medical.md) | Sleep apnea detection, cardiac arrhythmia, gait analysis, seizure detection |
 | 🔐 | [**Security & Safety**](docs/edge-modules/security.md) | Intrusion detection, perimeter breach, loitering, panic motion |
 | 🏢 | [**Smart Building**](docs/edge-modules/building.md) | Zone occupancy, HVAC control, elevator counting, meeting room tracking |
 | 🛒 | [**Retail & Hospitality**](docs/edge-modules/retail.md) | Queue length, dwell heatmaps, customer flow, table turnover |
 | 🏭 | [**Industrial**](docs/edge-modules/industrial.md) | Forklift proximity, confined space monitoring, structural vibration |
 | 🔮 | [**Exotic & Research**](docs/edge-modules/exotic.md) | Sleep staging, emotion detection, sign language, breathing sync |
 | 📡 | [**Signal Intelligence**](docs/edge-modules/signal-intelligence.md) | Cleans and sharpens raw WiFi signals — focuses on important regions, filters noise, fills in missing data, and tracks which person is which |
 | 🧠 | [**Adaptive Learning**](docs/edge-modules/adaptive-learning.md) | The sensor learns new gestures and patterns on its own over time — no cloud needed, remembers what it learned even after updates |
 | 🗺️ | [**Spatial Reasoning**](docs/edge-modules/spatial-temporal.md) | Figures out where people are in a room, which zones matter most, and tracks movement across areas using graph-based spatial logic |
 | ⏱️ | [**Temporal Analysis**](docs/edge-modules/spatial-temporal.md) | Learns daily routines, detects when patterns break (someone didn't get up), and verifies safety rules are being followed over time |
 | 🛡️ | [**AI Security**](docs/edge-modules/ai-security.md) | Detects signal replay attacks, WiFi jamming, injection attempts, and flags abnormal behavior that could indicate tampering |
 | ⚛️ | [**Quantum-Inspired**](docs/edge-modules/autonomous.md) | Uses quantum-inspired math to map room-wide signal coherence and search for optimal sensor configurations |
 | 🤖 | [**Autonomous & Exotic**](docs/edge-modules/autonomous.md) | Self-managing sensor mesh — auto-heals dropped nodes, plans its own actions, and explores experimental signal representations |
 All implemented modules are `no_std` Rust, share a [common utility library](v2/crates/wifi-densepose-wasm-edge/src/vendor_common.rs), and talk to the host through a 12-function API. Full documentation: [**Edge Modules Guide**](docs/edge-modules/README.md). See the [complete implemented module list](#edge-module-list) below.
 </details>
 <details id="edge-module-list">
 <summary><strong>🧩 Edge Intelligence — <a href="docs/edge-modules/README.md">All 65 Modules Implemented</a></strong> (ADR-041 complete)</summary>
 All 60 modules are implemented, tested (609 tests passing), and ready to deploy. They compile to `wasm32-unknown-unknown`, run on ESP32-S3 via WASM3, and share a [common utility library](v2/crates/wifi-densepose-wasm-edge/src/vendor_common.rs). Source: [`crates/wifi-densepose-wasm-edge/src/`](v2/crates/wifi-densepose-wasm-edge/src/)
 **Core modules** (ADR-040 flagship + early implementations):
 | Module | File | What It Does |
 |--------|------|-------------|
 | Gesture Classifier | [`gesture.rs`](v2/crates/wifi-densepose-wasm-edge/src/gesture.rs) | DTW template matching for hand gestures |
 | Coherence Filter | [`coherence.rs`](v2/crates/wifi-densepose-wasm-edge/src/coherence.rs) | Phase coherence gating for signal quality |
 | Adversarial Detector | [`adversarial.rs`](v2/crates/wifi-densepose-wasm-edge/src/adversarial.rs) | Detects physically impossible signal patterns |
 | Intrusion Detector | [`intrusion.rs`](v2/crates/wifi-densepose-wasm-edge/src/intrusion.rs) | Human vs non-human motion classification |
 | Occupancy Counter | [`occupancy.rs`](v2/crates/wifi-densepose-wasm-edge/src/occupancy.rs) | Zone-level person counting |
 | Vital Trend | [`vital_trend.rs`](v2/crates/wifi-densepose-wasm-edge/src/vital_trend.rs) | Long-term breathing and heart rate trending |
 | RVF Parser | [`rvf.rs`](v2/crates/wifi-densepose-wasm-edge/src/rvf.rs) | RVF container format parsing |
 **Vendor-integrated modules** (24 modules, ADR-041 Category 7):
 **📡 Signal Intelligence** — Real-time CSI analysis and feature extraction
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Flash Attention | [`sig_flash_attention.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_flash_attention.rs) | Tiled attention over 8 subcarrier groups — finds spatial focus regions and entropy | S (<5ms) |
 | Coherence Gate | [`sig_coherence_gate.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_coherence_gate.rs) | Z-score phasor gating with hysteresis: Accept / PredictOnly / Reject / Recalibrate | L (<2ms) |
 | Temporal Compress | [`sig_temporal_compress.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_temporal_compress.rs) | 3-tier adaptive quantization (8-bit hot / 5-bit warm / 3-bit cold) | L (<2ms) |
 | Sparse Recovery | [`sig_sparse_recovery.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_sparse_recovery.rs) | ISTA L1 reconstruction for dropped subcarriers | H (<10ms) |
 | Person Match | [`sig_mincut_person_match.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_mincut_person_match.rs) | Hungarian-lite bipartite assignment for multi-person tracking | S (<5ms) |
 | Optimal Transport | [`sig_optimal_transport.rs`](v2/crates/wifi-densepose-wasm-edge/src/sig_optimal_transport.rs) | Sliced Wasserstein-1 distance with 4 projections | L (<2ms) |
 **🧠 Adaptive Learning** — On-device learning without cloud connectivity
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | DTW Gesture Learn | [`lrn_dtw_gesture_learn.rs`](v2/crates/wifi-densepose-wasm-edge/src/lrn_dtw_gesture_learn.rs) | User-teachable gesture recognition — 3-rehearsal protocol, 16 templates | S (<5ms) |
 | Anomaly Attractor | [`lrn_anomaly_attractor.rs`](v2/crates/wifi-densepose-wasm-edge/src/lrn_anomaly_attractor.rs) | 4D dynamical system attractor classification with Lyapunov exponents | H (<10ms) |
 | Meta Adapt | [`lrn_meta_adapt.rs`](v2/crates/wifi-densepose-wasm-edge/src/lrn_meta_adapt.rs) | Hill-climbing self-optimization with safety rollback | L (<2ms) |
 | EWC Lifelong | [`lrn_ewc_lifelong.rs`](v2/crates/wifi-densepose-wasm-edge/src/lrn_ewc_lifelong.rs) | Elastic Weight Consolidation — remembers past tasks while learning new ones | S (<5ms) |
 **🗺️ Spatial Reasoning** — Location, proximity, and influence mapping
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | PageRank Influence | [`spt_pagerank_influence.rs`](v2/crates/wifi-densepose-wasm-edge/src/spt_pagerank_influence.rs) | 4x4 cross-correlation graph with power iteration PageRank | L (<2ms) |
 | Micro HNSW | [`spt_micro_hnsw.rs`](v2/crates/wifi-densepose-wasm-edge/src/spt_micro_hnsw.rs) | 64-vector navigable small-world graph for nearest-neighbor search | S (<5ms) |
 | Spiking Tracker | [`spt_spiking_tracker.rs`](v2/crates/wifi-densepose-wasm-edge/src/spt_spiking_tracker.rs) | 32 LIF neurons + 4 output zone neurons with STDP learning | S (<5ms) |
 **⏱️ Temporal Analysis** — Activity patterns, logic verification, autonomous planning
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Pattern Sequence | [`tmp_pattern_sequence.rs`](v2/crates/wifi-densepose-wasm-edge/src/tmp_pattern_sequence.rs) | Activity routine detection and deviation alerts | S (<5ms) |
 | Temporal Logic Guard | [`tmp_temporal_logic_guard.rs`](v2/crates/wifi-densepose-wasm-edge/src/tmp_temporal_logic_guard.rs) | LTL formula verification on CSI event streams | S (<5ms) |
 | GOAP Autonomy | [`tmp_goap_autonomy.rs`](v2/crates/wifi-densepose-wasm-edge/src/tmp_goap_autonomy.rs) | Goal-Oriented Action Planning for autonomous module management | S (<5ms) |
 **🛡️ AI Security** — Tamper detection and behavioral anomaly profiling
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Prompt Shield | [`ais_prompt_shield.rs`](v2/crates/wifi-densepose-wasm-edge/src/ais_prompt_shield.rs) | FNV-1a replay detection, injection detection (10x amplitude), jamming (SNR) | L (<2ms) |
 | Behavioral Profiler | [`ais_behavioral_profiler.rs`](v2/crates/wifi-densepose-wasm-edge/src/ais_behavioral_profiler.rs) | 6D behavioral profile with Mahalanobis anomaly scoring | S (<5ms) |
 **⚛️ Quantum-Inspired** — Quantum computing metaphors applied to CSI analysis
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Quantum Coherence | [`qnt_quantum_coherence.rs`](v2/crates/wifi-densepose-wasm-edge/src/qnt_quantum_coherence.rs) | Bloch sphere mapping, Von Neumann entropy, decoherence detection | S (<5ms) |
 | Interference Search | [`qnt_interference_search.rs`](v2/crates/wifi-densepose-wasm-edge/src/qnt_interference_search.rs) | 16 room-state hypotheses with Grover-inspired oracle + diffusion | S (<5ms) |
 **🤖 Autonomous Systems** — Self-governing and self-healing behaviors
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Psycho-Symbolic | [`aut_psycho_symbolic.rs`](v2/crates/wifi-densepose-wasm-edge/src/aut_psycho_symbolic.rs) | 16-rule forward-chaining knowledge base with contradiction detection | S (<5ms) |
 | Self-Healing Mesh | [`aut_self_healing_mesh.rs`](v2/crates/wifi-densepose-wasm-edge/src/aut_self_healing_mesh.rs) | 8-node mesh with health tracking, degradation/recovery, coverage healing | S (<5ms) |
 **🔮 Exotic (Vendor)** — Novel mathematical models for CSI interpretation
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Time Crystal | [`exo_time_crystal.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_time_crystal.rs) | Autocorrelation subharmonic detection in 256-frame history | S (<5ms) |
 | Hyperbolic Space | [`exo_hyperbolic_space.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_hyperbolic_space.rs) | Poincare ball embedding with 32 reference locations, hyperbolic distance | S (<5ms) |
 **🏥 Medical & Health** (Category 1) — Contactless health monitoring
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Sleep Apnea | [`med_sleep_apnea.rs`](v2/crates/wifi-densepose-wasm-edge/src/med_sleep_apnea.rs) | Detects breathing pauses during sleep | S (<5ms) |
 | Cardiac Arrhythmia | [`med_cardiac_arrhythmia.rs`](v2/crates/wifi-densepose-wasm-edge/src/med_cardiac_arrhythmia.rs) | Monitors heart rate for irregular rhythms | S (<5ms) |
 | Respiratory Distress | [`med_respiratory_distress.rs`](v2/crates/wifi-densepose-wasm-edge/src/med_respiratory_distress.rs) | Alerts on abnormal breathing patterns | S (<5ms) |
 | Gait Analysis | [`med_gait_analysis.rs`](v2/crates/wifi-densepose-wasm-edge/src/med_gait_analysis.rs) | Tracks walking patterns and detects changes | S (<5ms) |
 | Seizure Detection | [`med_seizure_detect.rs`](v2/crates/wifi-densepose-wasm-edge/src/med_seizure_detect.rs) | 6-state machine for tonic-clonic seizure recognition | S (<5ms) |
 **🔐 Security & Safety** (Category 2) — Perimeter and threat detection
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Perimeter Breach | [`sec_perimeter_breach.rs`](v2/crates/wifi-densepose-wasm-edge/src/sec_perimeter_breach.rs) | Detects boundary crossings with approach/departure | S (<5ms) |
 | Weapon Detection | [`sec_weapon_detect.rs`](v2/crates/wifi-densepose-wasm-edge/src/sec_weapon_detect.rs) | Metal anomaly detection via CSI amplitude shifts | S (<5ms) |
 | Tailgating | [`sec_tailgating.rs`](v2/crates/wifi-densepose-wasm-edge/src/sec_tailgating.rs) | Detects unauthorized follow-through at access points | S (<5ms) |
 | Loitering | [`sec_loitering.rs`](v2/crates/wifi-densepose-wasm-edge/src/sec_loitering.rs) | Alerts when someone lingers too long in a zone | S (<5ms) |
 | Panic Motion | [`sec_panic_motion.rs`](v2/crates/wifi-densepose-wasm-edge/src/sec_panic_motion.rs) | Detects fleeing, struggling, or panic movement | S (<5ms) |
 **🏢 Smart Building** (Category 3) — Automation and energy efficiency
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | HVAC Presence | [`bld_hvac_presence.rs`](v2/crates/wifi-densepose-wasm-edge/src/bld_hvac_presence.rs) | Occupancy-driven HVAC control with departure countdown | S (<5ms) |
 | Lighting Zones | [`bld_lighting_zones.rs`](v2/crates/wifi-densepose-wasm-edge/src/bld_lighting_zones.rs) | Auto-dim/off lighting based on zone activity | S (<5ms) |
 | Elevator Count | [`bld_elevator_count.rs`](v2/crates/wifi-densepose-wasm-edge/src/bld_elevator_count.rs) | Counts people entering/leaving with overload warning | S (<5ms) |
 | Meeting Room | [`bld_meeting_room.rs`](v2/crates/wifi-densepose-wasm-edge/src/bld_meeting_room.rs) | Tracks meeting lifecycle: start, headcount, end, availability | S (<5ms) |
 | Energy Audit | [`bld_energy_audit.rs`](v2/crates/wifi-densepose-wasm-edge/src/bld_energy_audit.rs) | Tracks after-hours usage and room utilization rates | S (<5ms) |
 **🛒 Retail & Hospitality** (Category 4) — Customer insights without cameras
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Queue Length | [`ret_queue_length.rs`](v2/crates/wifi-densepose-wasm-edge/src/ret_queue_length.rs) | Estimates queue size and wait times | S (<5ms) |
 | Dwell Heatmap | [`ret_dwell_heatmap.rs`](v2/crates/wifi-densepose-wasm-edge/src/ret_dwell_heatmap.rs) | Shows where people spend time (hot/cold zones) | S (<5ms) |
 | Customer Flow | [`ret_customer_flow.rs`](v2/crates/wifi-densepose-wasm-edge/src/ret_customer_flow.rs) | Counts ins/outs and tracks net occupancy | S (<5ms) |
 | Table Turnover | [`ret_table_turnover.rs`](v2/crates/wifi-densepose-wasm-edge/src/ret_table_turnover.rs) | Restaurant table lifecycle: seated, dining, vacated | S (<5ms) |
 | Shelf Engagement | [`ret_shelf_engagement.rs`](v2/crates/wifi-densepose-wasm-edge/src/ret_shelf_engagement.rs) | Detects browsing, considering, and reaching for products | S (<5ms) |
 **🏭 Industrial & Specialized** (Category 5) — Safety and compliance
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Forklift Proximity | [`ind_forklift_proximity.rs`](v2/crates/wifi-densepose-wasm-edge/src/ind_forklift_proximity.rs) | Warns when people get too close to vehicles | S (<5ms) |
 | Confined Space | [`ind_confined_space.rs`](v2/crates/wifi-densepose-wasm-edge/src/ind_confined_space.rs) | OSHA-compliant worker monitoring with extraction alerts | S (<5ms) |
 | Clean Room | [`ind_clean_room.rs`](v2/crates/wifi-densepose-wasm-edge/src/ind_clean_room.rs) | Occupancy limits and turbulent motion detection | S (<5ms) |
 | Livestock Monitor | [`ind_livestock_monitor.rs`](v2/crates/wifi-densepose-wasm-edge/src/ind_livestock_monitor.rs) | Animal presence, stillness, and escape alerts | S (<5ms) |
 | Structural Vibration | [`ind_structural_vibration.rs`](v2/crates/wifi-densepose-wasm-edge/src/ind_structural_vibration.rs) | Seismic events, mechanical resonance, structural drift | S (<5ms) |
 **🔮 Exotic & Research** (Category 6) — Experimental sensing applications
 | Module | File | What It Does | Budget |
 |--------|------|-------------|--------|
 | Dream Stage | [`exo_dream_stage.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_dream_stage.rs) | Contactless sleep stage classification (wake/light/deep/REM) | S (<5ms) |
 | Emotion Detection | [`exo_emotion_detect.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_emotion_detect.rs) | Arousal, stress, and calm detection from micro-movements | S (<5ms) |
 | Gesture Language | [`exo_gesture_language.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_gesture_language.rs) | Sign language letter recognition via WiFi | S (<5ms) |
 | Music Conductor | [`exo_music_conductor.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_music_conductor.rs) | Tempo and dynamic tracking from conducting gestures | S (<5ms) |
 | Plant Growth | [`exo_plant_growth.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_plant_growth.rs) | Monitors plant growth, circadian rhythms, wilt detection | S (<5ms) |
 | Ghost Hunter | [`exo_ghost_hunter.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_ghost_hunter.rs) | Environmental anomaly classification (draft/insect/wind/unknown) | S (<5ms) |
 | Rain Detection | [`exo_rain_detect.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_rain_detect.rs) | Detects rain onset, intensity, and cessation via signal scatter | S (<5ms) |
 | Breathing Sync | [`exo_breathing_sync.rs`](v2/crates/wifi-densepose-wasm-edge/src/exo_breathing_sync.rs) | Detects synchronized breathing between multiple people | S (<5ms) |
 </details>
 ---
--- a/data/clone-data.rvf
+++ b/data/clone-data.rvf
@ -1,3 +0,0 @@
 {"type": "metadata", "name": "ruview-clone-traffic-history", "version": "1.0.0", "schema": "ruvector.rvf.jsonl/v1", "format": "github-traffic-snapshots", "repo": "ruvnet/RuView", "source": "GitHub Traffic API /repos/{repo}/traffic/{clones,views}", "policy": "GitHub retains only 14 days server-side; this file is the long-term record.", "segments": ["metadata", "clone_snapshot", "view_snapshot"], "created_at": "2026-05-19T23:16:22Z", "custom": {"cadence": "twice monthly (1st and 15th, ~14-day intervals)", "idempotency_key": "timestamp (per-day records de-duplicate across overlapping snapshot windows)"}}
 {"type": "clone_snapshot", "fetched_at": "2026-05-19T23:16:22Z", "window_count": 27887, "window_uniques": 6611, "per_day": [{"timestamp": "2026-05-05T00:00:00Z", "count": 620, "uniques": 218}, {"timestamp": "2026-05-06T00:00:00Z", "count": 477, "uniques": 232}, {"timestamp": "2026-05-07T00:00:00Z", "count": 685, "uniques": 268}, {"timestamp": "2026-05-08T00:00:00Z", "count": 703, "uniques": 276}, {"timestamp": "2026-05-09T00:00:00Z", "count": 352, "uniques": 184}, {"timestamp": "2026-05-10T00:00:00Z", "count": 205, "uniques": 151}, {"timestamp": "2026-05-11T00:00:00Z", "count": 1160, "uniques": 234}, {"timestamp": "2026-05-12T00:00:00Z", "count": 599, "uniques": 207}, {"timestamp": "2026-05-13T00:00:00Z", "count": 5141, "uniques": 1152}, {"timestamp": "2026-05-14T00:00:00Z", "count": 3420, "uniques": 972}, {"timestamp": "2026-05-15T00:00:00Z", "count": 1974, "uniques": 764}, {"timestamp": "2026-05-16T00:00:00Z", "count": 2917, "uniques": 617}, {"timestamp": "2026-05-17T00:00:00Z", "count": 6690, "uniques": 1169}, {"timestamp": "2026-05-18T00:00:00Z", "count": 2944, "uniques": 625}]}
 {"type": "view_snapshot", "fetched_at": "2026-05-19T23:16:22Z", "window_count": 162314, "window_uniques": 75464, "per_day": [{"timestamp": "2026-05-05T00:00:00Z", "count": 5540, "uniques": 2690}, {"timestamp": "2026-05-06T00:00:00Z", "count": 5111, "uniques": 2393}, {"timestamp": "2026-05-07T00:00:00Z", "count": 5585, "uniques": 2708}, {"timestamp": "2026-05-08T00:00:00Z", "count": 7004, "uniques": 3261}, {"timestamp": "2026-05-09T00:00:00Z", "count": 5395, "uniques": 2531}, {"timestamp": "2026-05-10T00:00:00Z", "count": 4761, "uniques": 2219}, {"timestamp": "2026-05-11T00:00:00Z", "count": 4275, "uniques": 2044}, {"timestamp": "2026-05-12T00:00:00Z", "count": 3466, "uniques": 1688}, {"timestamp": "2026-05-13T00:00:00Z", "count": 13561, "uniques": 8473}, {"timestamp": "2026-05-14T00:00:00Z", "count": 21867, "uniques": 12527}, {"timestamp": "2026-05-15T00:00:00Z", "count": 26182, "uniques": 14609}, {"timestamp": "2026-05-16T00:00:00Z", "count": 17406, "uniques": 8868}, {"timestamp": "2026-05-17T00:00:00Z", "count": 28444, "uniques": 14541}, {"timestamp": "2026-05-18T00:00:00Z", "count": 13717, "uniques": 7819}]}
--- a/examples/three.js/demos/04-skinned-fbx.html
+++ b/examples/three.js/demos/04-skinned-fbx.html
@ -572,59 +572,9 @@
            const txt = document.querySelector('#loading .text');
            if (txt) txt.textContent = `▸ Loading skinned subject · X Bot.fbx · ${pct} %`;
        }, (err) => {
-            // Graceful degradation: when the FBX 404s on gh-pages (Mixamo
+            console.error('FBX load failed', err);
-            // X Bot.fbx is gitignored — license boundary, not redistributed)
+            const txt = document.querySelector('#loading .text');
-            // we hide the spinner and show a friendly banner explaining how
+            if (txt) txt.textContent = '⚠ Load failed — see console';
            // to run this demo locally with your own Mixamo download.
            // Local development with assets/X Bot.fbx present hits the
            // success branch above and never sees this UI.
            console.warn('FBX load failed — showing fallback banner', err);
            const loading = document.getElementById('loading');
            if (loading) {
                loading.innerHTML = `
                    <div style="
                        max-width: 540px; padding: 20px 22px;
                        background: rgba(20, 24, 38, 0.92);
                        border: 1px solid rgba(78, 205, 196, 0.4);
                        border-radius: 10px;
                        color: #e0e4f0; font-family: 'Segoe UI', system-ui, sans-serif;
                        line-height: 1.5; font-size: 14px;
                        box-shadow: 0 6px 24px rgba(0,0,0,0.5);
                    ">
                        <div style="font-size:16px; color:#4ecdc4; font-weight:600; margin-bottom:6px;">
                            🦴 Mixamo asset not bundled in this deployment
                        </div>
                        <div style="color:#c8cee0; margin-bottom:12px;">
                            This demo loads <code style="color:#4ecdc4; background:rgba(78,205,196,0.08); padding:1px 6px; border-radius:3px;">X Bot.fbx</code>
                            from Mixamo, which is intentionally not redistributed here (license boundary).
                            The ADR-097 helpers scene (grid / axes / per-node CSI boxes) is rendering behind this card —
                            click outside to interact with it.
                        </div>
                        <div style="color:#8890a8; font-size:13px; margin-bottom:14px;">
                            To run this demo with the character, clone the repo, download
                            <code style="color:#4ecdc4;">X Bot.fbx</code> (FBX Binary · T-Pose · Without Skin)
                            from <a href="https://mixamo.com" target="_blank" rel="noopener" style="color:#4ecdc4;">mixamo.com</a>
                            into <code style="color:#4ecdc4;">examples/three.js/assets/</code>, then run
                            <code style="color:#4ecdc4;">python examples/three.js/server/serve-demo.py</code>.
                        </div>
                        <div style="display:flex; gap:10px; flex-wrap:wrap;">
                            <a href="https://github.com/ruvnet/RuView/tree/main/examples/three.js" target="_blank" rel="noopener"
                               style="padding:6px 12px; background:rgba(78,205,196,0.12); border:1px solid rgba(78,205,196,0.4); border-radius:6px; color:#4ecdc4; text-decoration:none; font-size:13px;">
                                📂 Source on GitHub
                            </a>
                            <a href="https://mixamo.com" target="_blank" rel="noopener"
                               style="padding:6px 12px; background:rgba(212,165,116,0.12); border:1px solid rgba(212,165,116,0.4); border-radius:6px; color:#d4a574; text-decoration:none; font-size:13px;">
                                🦴 Get X Bot from Mixamo
                            </a>
                            <a href="../" style="padding:6px 12px; background:rgba(136,144,168,0.12); border:1px solid rgba(136,144,168,0.3); border-radius:6px; color:#8890a8; text-decoration:none; font-size:13px;">
                                ← Back to demo gallery
                            </a>
                        </div>
                    </div>
                `;
                loading.style.pointerEvents = 'auto';
                loading.style.cursor = 'default';
            }
        });
        function playClip(name) {
--- a/examples/three.js/demos/05-skinned-realtime.html
+++ b/examples/three.js/demos/05-skinned-realtime.html
@ -721,56 +721,8 @@
        const txt = document.querySelector('#loading .text');
        if (txt) txt.textContent = `▸ Loading skinned subject · X Bot.fbx · ${pct} %`;
    }, (err) => {
-        // Graceful degradation when X Bot.fbx 404s on gh-pages (license
+        console.error('FBX load failed', err);
-        // boundary — not redistributed). Local runs with the FBX present
+        document.querySelector('#loading .text').textContent = '⚠ Load failed — see console';
        // hit the success branch above and never see this banner.
        console.warn('FBX load failed — showing fallback banner', err);
        const loading = document.getElementById('loading');
        if (loading) {
            loading.innerHTML = `
                <div style="
                    max-width: 580px; padding: 20px 22px;
                    background: rgba(20, 24, 38, 0.92);
                    border: 1px solid rgba(78, 205, 196, 0.4);
                    border-radius: 10px;
                    color: #e0e4f0; font-family: 'Segoe UI', system-ui, sans-serif;
                    line-height: 1.5; font-size: 14px;
                    box-shadow: 0 6px 24px rgba(0,0,0,0.5);
                ">
                    <div style="font-size:16px; color:#4ecdc4; font-weight:600; margin-bottom:6px;">
                        🦴 Mixamo asset not bundled in this deployment
                    </div>
                    <div style="color:#c8cee0; margin-bottom:12px;">
                        This realtime pose demo retargets webcam + MediaPipe onto
                        <code style="color:#4ecdc4; background:rgba(78,205,196,0.08); padding:1px 6px; border-radius:3px;">X Bot.fbx</code>,
                        which Mixamo licenses for direct download by end users and is intentionally not
                        redistributed here. The ADR-097 helpers scene is still rendering behind this card.
                    </div>
                    <div style="color:#8890a8; font-size:13px; margin-bottom:14px;">
                        To run locally: clone the repo, get
                        <code style="color:#4ecdc4;">X Bot.fbx</code> (FBX Binary · T-Pose · Without Skin)
                        from <a href="https://mixamo.com" target="_blank" rel="noopener" style="color:#4ecdc4;">mixamo.com</a>,
                        drop it in <code style="color:#4ecdc4;">examples/three.js/assets/</code>, then
                        <code style="color:#4ecdc4;">python examples/three.js/server/serve-demo.py</code>.
                    </div>
                    <div style="display:flex; gap:10px; flex-wrap:wrap;">
                        <a href="https://github.com/ruvnet/RuView/tree/main/examples/three.js" target="_blank" rel="noopener"
                           style="padding:6px 12px; background:rgba(78,205,196,0.12); border:1px solid rgba(78,205,196,0.4); border-radius:6px; color:#4ecdc4; text-decoration:none; font-size:13px;">
                            📂 Source on GitHub
                        </a>
                        <a href="https://mixamo.com" target="_blank" rel="noopener"
                           style="padding:6px 12px; background:rgba(212,165,116,0.12); border:1px solid rgba(212,165,116,0.4); border-radius:6px; color:#d4a574; text-decoration:none; font-size:13px;">
                            🦴 Get X Bot from Mixamo
                        </a>
                        <a href="../" style="padding:6px 12px; background:rgba(136,144,168,0.12); border:1px solid rgba(136,144,168,0.3); border-radius:6px; color:#8890a8; text-decoration:none; font-size:13px;">
                            ← Back to demo gallery
                        </a>
                    </div>
                </div>
            `;
            loading.style.pointerEvents = 'auto';
            loading.style.cursor = 'default';
        }
    });
    // ---------------------------------------------------------------------