chore(deps): bump actions/github-script from 6 to 9

Bumps [actions/github-script](https://github.com/actions/github-script) from 6 to 9. - [Release notes](https://github.com/actions/github-script/releases) - [Commits](https://github.com/actions/github-script/compare/v6...v9) --- updated-dependencies: - dependency-name: actions/github-script dependency-version: '9' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>
2026-06-09 10:13:17 +00:00 · 2026-05-20 01:13:14 +00:00
16 changed files with 9 additions and 266 deletions
@@ -275,7 +275,7 @@ jobs:
        done

    - name: Update deployment status
-      uses: actions/github-script@v6
+      uses: actions/github-script@v9
      with:
        script: |
          const deployEnv = '${{ needs.pre-deployment.outputs.deploy_env }}';
@@ -326,7 +326,7 @@ jobs:

    - name: Create deployment issue on failure
      if: needs.deploy-production.result == 'failure'
-      uses: actions/github-script@v6
+      uses: actions/github-script@v9
      with:
        script: |
          github.rest.issues.create({
@@ -216,14 +216,10 @@ jobs:
          htmlcov/

  # Performance and Load Tests
-  # NOTE: tests/performance/locustfile.py and the src.api.main app path both
-  # predate the v1→archive/v1 reorganisation. continue-on-error: true until a
-  # proper locust suite is added under archive/v1/tests/performance/.
  performance-test:
    name: Performance Tests
    runs-on: ubuntu-latest
    needs: [test]
-    continue-on-error: true
    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
    steps:
    - name: Checkout code
@@ -242,7 +238,6 @@ jobs:
        pip install locust

    - name: Start application
-      working-directory: archive/v1
      run: |
        uvicorn src.api.main:app --host 0.0.0.0 --port 8000 &
        sleep 10
@@ -357,7 +352,6 @@ jobs:
        pip install -r requirements.txt

    - name: Generate OpenAPI spec
-      working-directory: archive/v1
      run: |
        python -c "
        from src.api.main import app
@@ -379,8 +373,6 @@ jobs:
    runs-on: ubuntu-latest
    needs: [code-quality, test, rust-tests, performance-test, docker-build, docs]
    if: always()
-    permissions:
-      contents: write   # required by softprops/action-gh-release
    # GitHub Actions does not allow `secrets.X` directly in step-level `if:`
    # expressions — only `env.X`. Promote the secret to env at job scope so
    # the gating expression below is parseable.
@@ -478,7 +478,7 @@ jobs:
    - name: Create security issue on critical findings
      continue-on-error: true
      if: needs.sast.result == 'failure' || needs.dependency-scan.result == 'failure'
-      uses: actions/github-script@v6
+      uses: actions/github-script@v9
      with:
        script: |
          github.rest.issues.create({
@@ -1,17 +1,11 @@
 # π RuView

 <p align="center">
-  <a href="https://cognitum.one/seed">
+  <a href="https://x.com/rUv/status/2037556932802761004">
    <img src="assets/ruview-small-gemini.jpg" alt="RuView - WiFi DensePose" width="100%">
  </a>
 </p>

-<p align="center">
-  <a href="https://cognitum.one/seed">
-    <img src="assets/seed.png" alt="Cognitum Seed" width="100%">
-  </a>
-</p>
-
 > **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
 > - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
 > - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
@@ -1,198 +0,0 @@
-# ADR-103: Learned Multi-Person Counter (SOTA WiFi CSI counting)
-
- **Status:** Proposed
- **Date:** 2026-05-21
- **Deciders:** ruv
- **Motivating issue:** #499 (double skeletons with 3-node ESP32-S3 setup, closed by PR #491)
- **Related:** ADR-079 (camera-supervised training), ADR-100 (cog packaging), ADR-101 (pose cog), ADR-102 (edge module registry), PR #491 (RollingP95 + dedup_factor)
-
-## Context
-
-PR #491 stopped the bleeding on #499. The fix replaced hard-coded denominators (`variance/300`, `motion_band_power/250`, `spectral_power/500`) with a self-calibrating `RollingP95` streaming estimator and exposed the multi-node `dedup_factor` as a runtime knob. Day-0 deployments no longer collapse dynamic range, and operators can auto-tune the divisor from a known person count.
-
-That gets us to a **stable heuristic that adapts to the room**. It does not get us to the published WiFi-CSI counting state of the art:
-
-| System | Setup | Reported accuracy | Method |
-|--------|-------|-------------------|--------|
-| **WiCount** (CMU, 2017) | Intel 5300 3×3 MIMO | 89% within ±1 | LSTM over CSI amplitude |
-| **DeepCount** (2018) | Atheros 3×3 | 92% within ±1, 5-room | CNN + cross-environment transfer |
-| **CrossCount** (2019) | Atheros, 6 rooms | 84% cross-room within ±1 | Domain-adversarial CNN |
-| **HeadCount** (2021) | Intel 5300 | <1 person MAE, 5 envs | Multi-stream CSI + attention |
-| **RuView today** (PR #491) | ESP32-S3 1×1 SISO | Calibrated heuristic; not measured against ground truth | RollingP95 + dedup_factor |
-
-The literature uses 3×3 MIMO research NICs. RuView uses 1×1 SISO ESP32-S3 nodes. The published number is therefore not directly attainable, but the **architectural gap** is large enough that a learned-counter approach on our hardware should comfortably beat today's slot heuristic — and the infrastructure to train one already exists in this repo (Candle + RTX 5080 trained `pose_v1.safetensors` in 2.1 s yesterday — see [`docs/benchmarks/pose-estimation-cog.md`](../benchmarks/pose-estimation-cog.md)).
-
-Five primitives we already have but don't yet compose into a counter:
-
-1. **Paired CSI + camera label dataset** — `scripts/collect-ground-truth.py` + `scripts/align-ground-truth.js` (PR #641 streaming-safe). 1,077 samples currently; #645 tracks the path to ~30K.
-2. **Stoer-Wagner min-cut for person-separable subcarrier groups** — `ruvector-mincut` (already a workspace dep). The Candle trainer used it yesterday and reported `Min-cut value: 0.1538 — partition: [55, 1] subcarriers`.
-3. **Contrastive-pretrained CSI encoder** — `ruvnet/wifi-densepose-pretrained` on HF (12.2M training steps, 60K frames, 128-dim embeddings, ~165k emb/s on M4 Pro).
-4. **Candle training pipeline** — proven yesterday: 400 epochs in 2.1 s on RTX 5080, bit-perfect ONNX export, signed cog binary on GCS.
-5. **Multi-node fusion stage** — `multistatic_bridge.rs` already aggregates per-node feature vectors with the tunable `dedup_factor`. The new model output can be a drop-in replacement for the existing dedup divisor.
-
-## Decision
-
-Train and ship a small **learned multi-person counter** as a new Cognitum Cog (`cog-person-count`), modelled on the same packaging path as `cog-pose-estimation` (ADR-101). Wire it into the sensing-server's existing person-count call site (`csi.rs::score_to_person_count`) as a drop-in replacement for the slot heuristic.
-
-### Architecture (v0.1.0)
-
-```
-                              ┌──────────────────────────────┐
-       per-node CSI window    │  Encoder (frozen first 50 ep) │
-       [56 sub × 20 frames]  ─►  init from ruvnet/wifi-       │
-                              │  densepose-pretrained         │
-                              │  → 128-dim embedding          │
-                              └──────────────┬───────────────┘
-                                             │
-                            ┌────────────────┴────────────────┐
-                            ▼                                 ▼
-                   ┌────────────────────┐       ┌────────────────────────┐
-                   │  Count head        │       │  Confidence head       │
-                   │  Linear(128→64)    │       │  Linear(128→32)        │
-                   │  ReLU              │       │  ReLU                  │
-                   │  Linear(64→8)      │       │  Linear(32→1) + sigmoid│
-                   │  → softmax over    │       │  → calibrated p(correct)│
-                   │     {0..7} persons │       └────────────────────────┘
-                   └────────┬───────────┘
-                            │                    (per-node prediction)
-                            │
-       N nodes' per-node    │
-       counts + confidences ▼
-                   ┌─────────────────────────────────────┐
-                   │  Multi-node fusion (Stoer-Wagner)   │
-                   │  • build graph: nodes × subcarrier  │
-                   │    feature similarity               │
-                   │  • min-cut → distinct-person bound  │
-                   │  • combine with per-node count head │
-                   │    via confidence-weighted vote     │
-                   └──────────────────┬──────────────────┘
-                                      ▼
-                          { count: int,
-                            confidence: float [0,1],
-                            count_p95_low: int,
-                            count_p95_high: int,
-                            per_node_breakdown: [...] }
-```
-
-Five things to call out about this architecture:
-
-1. **Frozen encoder for the first 50 epochs.** The HF presence encoder already produces a useful 128-dim embedding from random CSI; training the counting head on top of frozen features is the standard transfer-learning pattern and avoids re-learning the contrastive geometry the encoder was painstakingly trained for.
-2. **Classification over `{0..7}` people**, not regression to a real number. Counts are integer-valued; classification gives a calibrated probability per count and lets the confidence head produce a meaningful uncertainty.
-3. **Stoer-Wagner min-cut at fusion time, not training time.** We use the min-cut primitive to bound the per-node count from above (a node can't see more distinct people than the subcarrier graph has min-cuts), then take a confidence-weighted vote.
-4. **Output is `{count, confidence, count_p95_low, count_p95_high}`**, not a single integer. Downstream consumers (Cogs / dashboard / alerts) can choose their certainty threshold. This is what closes the loop on the #499 UX: when the model is uncertain, the dashboard renders one stick figure with a "?" badge rather than two ghosts.
-5. **No new hardware.** Same ESP32-S3 1×1 SISO that ships today. The win comes from learned features + multi-node fusion, not from bigger antennas.
-
-### Training (Candle / RTX 5080 / proven path)
-
-Same exact pipeline that produced `pose_v1.safetensors` yesterday. Differences:
-
-| | Pose cog (today) | Count cog (this ADR) |
-|---|---|---|
-| Input | `[56, 20]` CSI window | `[56, 20]` CSI window (identical) |
-| Encoder init | random (HF arch mismatch) | **from HF presence model** (architectures are compatible — same encoder Φ) |
-| Output head | `Linear(128 → 256 → 34)` keypoints | `Linear(128 → 64 → 8)` count classes + `Linear(128 → 32 → 1)` confidence |
-| Loss | Confidence-weighted SmoothL1 | Categorical cross-entropy + Brier-score uncertainty calibration |
-| Labels | MediaPipe keypoints | Camera count (MediaPipe `pose_landmarks` length) |
-| Data | 1,077 paired (P7) | **Same source, same script** — `collect-ground-truth.py` already records `n_persons` per frame |
-
-Crucially we get the count labels **for free** from the existing pose data-collection pipeline — `collect-ground-truth.py` already records `"n_persons"` per camera frame and `align-ground-truth.js` already preserves it through windowing. No new data collection campaign required to bootstrap; we can train tomorrow on the same 1,077 samples that produced `pose_v1`.
-
-### Multi-node fusion
-
-The per-node count head + confidence head emit a categorical distribution over `{0..7}`. With N nodes, we have N such distributions plus N confidence scalars. Two fusion paths:
-
- **Confidence-weighted log-sum** (Bayesian product): `log p_fused(k) = Σ_n c_n · log p_n(k)`. Simple, no extra parameters, comes from the optimal-expert combination literature.
- **Stoer-Wagner upper bound**: build a graph where edges are pairwise subcarrier-feature similarities between nodes. Min-cut size = a hard upper bound on the number of distinct people the node mesh can resolve. Clip the per-node-fused distribution to support `{0..min-cut}` before re-normalising. This is exactly what `ruvector-mincut` was added to the workspace for — it's been waiting for a counting consumer.
-
-Both fuse cleanly. v0.1.0 ships the log-sum; v0.2.0 adds the min-cut clipper after the first round of evaluation.
-
-### Why this beats today's heuristic
-
-| Failure mode of today's slot heuristic | How the learned counter avoids it |
-|---|---|
-| #499 — fixed denominators clamp → one person renders as 2+ groups | Encoder produces a fixed-dim embedding; the count head is invariant to feature magnitude, only to feature **shape** |
-| `dedup_factor` per-room tuning is operator-visible toil | Count head's softmax is a learned per-room normaliser by construction |
-| Adding nodes makes the count noisier under the slot heuristic | Multi-node fusion is **additive in confidence**, so each node either reduces uncertainty or stays neutral — never amplifies it |
-| No per-frame uncertainty signal | `confidence` + `count_p95_low/high` exposed in every emit |
-| Catastrophic failure on novel environments | LoRA per-room adapter (per ADR-079 P9 plan) hot-swappable without retraining |
-
-### Acceptance gates
-
-| Gate | v0.1.0 (initial release) | v0.2.0 (after data scaling) |
-|------|--------------------------|------------------------------|
-| Day-0 deployment (no calibration) | ≥ 80% within ±1 on same-room test set | ≥ 90% within ±1 |
-| Cross-room (held-out environment) | ≥ 60% within ±1 | ≥ 75% within ±1 |
-| Mean Absolute Error | ≤ 0.6 persons | ≤ 0.4 persons |
-| Per-frame confidence reflects accuracy | Spearman correlation `r ≥ 0.5` between `confidence` and `(predicted == true)` | `r ≥ 0.7` |
-| Inference latency on Pi 5 (Cog) | < 5 ms / frame cold-start | < 5 ms / frame |
-| Binary size on GCS | ≤ 4 MB (matches `cog-pose-estimation`) | ≤ 4 MB |
-
-`v0.1.0` is intentionally modest — it's bounded by data-collection scale (#645). The framework is the deliverable; the accuracy follows the data.
-
-### Repo layout
-
-```
-v2/crates/cog-person-count/                   # NEW (this ADR)
-├── Cargo.toml
-├── src/
-│   ├── main.rs                # cog runtime: version | manifest | health | run
-│   ├── lib.rs
-│   ├── inference.rs           # Candle forward pass on per-node CSI
-│   ├── fusion.rs              # Stoer-Wagner upper-bound + confidence-weighted log-sum
-│   └── publisher.rs           # emits {count, confidence, count_p95_low, count_p95_high}
-├── cog/
-│   ├── manifest.template.json
-│   ├── config.schema.json
-│   ├── README.md
-│   └── artifacts/             # filled by the release pipeline
-│       ├── count_v1.safetensors
-│       ├── count_v1.onnx
-│       └── train_results.json
-└── tests/
-    ├── smoke.rs               # 5+ tests
-    └── fusion_test.rs         # multi-node-fusion math
-```
-
-Plus a small server-side wiring change:
-
- `v2/crates/wifi-densepose-sensing-server/src/csi.rs::score_to_person_count` — call the cog over the same `/api/v1/edge/registry`-discovered runtime as `cog-pose-estimation`. Falls back to today's PR #491 heuristic if the cog isn't installed (per the ADR-100 stub-fallback pattern).
-
-## Consequences
-
-### Positive
-
- Closes the conceptual loop opened by #499 — multi-person counting becomes a **learned task**, not a heuristic with a runtime knob.
- Reuses every primitive already shipped this week: Candle GPU training (ADR-101), HF encoder, Cog packaging (ADR-100), edge module registry (ADR-102), Stoer-Wagner mincut, paired-data pipeline (PR #641).
- Day-2 cross-room calibration uses the same LoRA path ADR-079 P9 plans for pose, so the two cogs share the same fine-tuning machinery.
- Explicit `confidence` + `count_p95_low/high` outputs let the UI render uncertainty instead of inventing ghosts.
-
-### Negative
-
- Accuracy is bounded by the same paired-data scarcity that bounds `pose_v1` (#645). Without more multi-room data, v0.1.0 ships with modest absolute accuracy.
- Adds another Cog binary to maintain in the GCS catalog — 4 MB per arch.
- The fusion-stage min-cut adds ~0.3 ms per N-node frame on a Pi 5 in microbenchmarks of `ruvector-mincut`. Acceptable given the ≤ 5 ms budget but worth tracking.
-
-### Risks
-
- **Label noise**: MediaPipe pose-detection rate was 47% in the P7 session — half the frames have `n_persons = 0` even when a person was clearly in the room. The count head learns from this noisy signal; mitigations include filtering by `MediaPipe confidence ≥ 0.7` before training, and weighting the loss by confidence (same trick used in `pose_v1`).
- **Encoder freezing too aggressive**: if 50 epochs of frozen-encoder training doesn't see the count head converge, unfreeze earlier. We have telemetry from `train_results.json` to make this call empirically.
- **Min-cut over-constrains** in single-person scenarios: when N=1 the subcarrier graph has min-cut = 1 trivially. The fusion stage degrades to "trust the single-node count head", which is fine but worth a regression test (`tests/fusion_test.rs::single_node_degrades_gracefully`).
-
-## Migration
-
-1. Land this ADR + the new crate scaffold (one PR, no model yet — same approach as ADR-101's first PR shipped a stub cog).
-2. Train `count_v1.safetensors` on the existing 1,077 paired samples + `n_persons` labels. Same Candle pipeline that produced `pose_v1`.
-3. Cross-compile + sign + GCS upload per ADR-100. Live install on `cognitum-v0` per ADR-101's pattern.
-4. Wire `csi.rs::score_to_person_count` to call the cog when installed; keep PR #491's heuristic as fallback.
-5. v0.2.0: re-train on the multi-room data #645 motivates, add LoRA per-room adapters per ADR-079 P9.
-
-## See also
-
- ADR-079 — Camera-supervised training pipeline (same data path).
- ADR-100 — Cognitum Cog packaging spec (same shipping format).
- ADR-101 — Pose Estimation Cog (template for this Cog's first release).
- ADR-102 — Edge Module Registry (where this cog appears in the catalog).
- PR #491 — RollingP95 + `dedup_factor` (the heuristic this learned counter replaces).
- Issue #499 — Multi-node ghost skeletons (closed by #491, motivates this ADR).
- Issue #645 — PCK / data-collection plan (same data-bound limit; same fix path).
- `docs/benchmarks/pose-estimation-cog.md` — measured perf envelope for the cog runtime this ADR targets.
@@ -25,23 +25,6 @@ This firmware captures WiFi Channel State Information (CSI) from an ESP32-S3 and

 For users who want to get running fast. Detailed explanations follow in later sections.

-### 0. Pre-built binaries (v0.6.5 — skip the build step)
-
-Pre-built binaries are in `firmware/esp32-csi-node/release_bins/` (version: see `release_bins/version.txt`).
-Flash them directly:
-
-```bash
-python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
-  write_flash --flash_mode dio --flash_size 8MB \
-  0x0     firmware/esp32-csi-node/release_bins/bootloader.bin \
-  0x8000  firmware/esp32-csi-node/release_bins/partition-table.bin \
-  0xf000  firmware/esp32-csi-node/release_bins/ota_data_initial.bin \
-  0x20000 firmware/esp32-csi-node/release_bins/esp32-csi-node.bin
-```
-
-For 4 MB boards use `release_bins/esp32-csi-node-4mb.bin` and `release_bins/partition-table-4mb.bin`
-with `--flash_size 4MB`.
-
 ### 1. Build (Docker -- the only reliable method)

 ```bash
@@ -311,9 +294,8 @@ python -m serial.tools.miniterm COM7 115200
 Expected output after boot:

 ```
-I (396) csi_collector: Early capture node_id=1 (before WiFi init, #232/#390)
-I (406) main: ESP32-S3 CSI Node (ADR-018) -- v0.6.5 -- Node ID: 1
-I (566) main: WiFi STA initialized, connecting to SSID: wifi-densepose
+I (321) main: ESP32-S3 CSI Node (ADR-018) -- Node ID: 1
+I (345) main: WiFi STA initialized, connecting to SSID: wifi-densepose
 I (1023) main: Connected to WiFi
 I (1025) main: CSI streaming active -> 192.168.1.100:5005 (edge_tier=2, OTA=ready, WASM=ready)
 ```
@@ -849,8 +849,6 @@ static void process_frame(const edge_ring_slot_t *slot)

    /* --- Step 11: Multi-person vitals --- */
    update_multi_person_vitals(slot->iq_data, n_subcarriers, sample_rate);
-    /* Yield after multi-person DSP so IDLE1 can feed Core 1 watchdog (#683). */
-    if (s_cfg.tier >= 2) vTaskDelay(1);

    /* --- Step 12: Delta compression --- */
    if (s_cfg.tier >= 2) {
@@ -896,8 +894,6 @@ static void process_frame(const edge_ring_slot_t *slot)
        wasm_runtime_on_frame(phases, amplitudes, variances,
                              n_subcarriers,
                              (const edge_vitals_pkt_t *)&s_latest_pkt);
-        /* Yield after WASM dispatch to feed Core 1 watchdog (#683). */
-        vTaskDelay(1);
    }
 }

@@ -294,7 +294,7 @@ def flash_nvs(port, baud, nvs_bin, chip):
            "--chip", chip,
            "--port", port,
            "--baud", str(baud),
-            "write_flash",
+            "write-flash",
            hex(NVS_PARTITION_OFFSET), bin_path,
        ]
        print(f"Flashing NVS partition ({len(nvs_bin)} bytes) to {port} (chip={chip})...")
@@ -499,7 +499,7 @@ def main():
            f.write(nvs_bin)
        print(f"NVS binary saved to {out} ({len(nvs_bin)} bytes)")
        print(f"Flash manually: python -m esptool --chip {args.chip} --port {args.port} "
-              f"write_flash 0x9000 {out}")
+              f"write-flash 0x9000 {out}")
        # Persist merged state even on dry-run so a subsequent real flash from
        # this machine sees the same staged config.
        path = save_state(args.port, args.state_dir, merged)
@@ -1,3 +0,0 @@
-0.6.6
-git-sha: cbcb389cb (pre-commit)
-built: 2026-05-21
@@ -1 +1 @@
-0.6.6
+0.6.5
@@ -213,26 +213,6 @@
      ],
      "rationale": "Without quantization, the SHA-256 of features_to_bytes() diverges across SIMD backends (Intel AVX2/AVX-512 vs Apple Silicon NEON) because scipy.fft's pocketfft kernels reorder vectorized FP operations differently per build. IEEE 754 guarantees per-operation determinism, not associativity. Rounding to 9 decimal places (~5 orders of magnitude headroom over observed ULP drift) collapses the cross-platform divergence to a single canonical hash. Removing the round() call reintroduces the macOS arm64 vs Linux x86_64 hash mismatch in issue #560.",
      "ref": "https://github.com/ruvnet/RuView/issues/560"
-    },
-    {
-      "id": "RuView#679",
-      "title": "ESP32-S3 CSI: csi_collector_set_node_id() called before wifi_init_sta() so node_id is never clobbered",
-      "files": ["firmware/esp32-csi-node/main/main.c"],
-      "require": ["csi_collector_set_node_id"],
-      "forbid": ["/csi_collector_init.*node_id\\s*=\\s*1[^0-9]/"],
-      "rationale": "release_bins/ shipped v0.4.3.1 binaries that lacked csi_collector_set_node_id() — every provisioned node reported node_id=1 over UDP regardless of NVS value, making a 4-node deployment look like a single node. main.c must call csi_collector_set_node_id(g_nvs_config.node_id) immediately after nvs_config_load() and before wifi_init_sta(). Reverting silently breaks multi-node deployments with no build-time error.",
-      "ref": "https://github.com/ruvnet/RuView/issues/679"
-    },
-    {
-      "id": "RuView#683",
-      "title": "ESP32-S3 edge tier>=2: vTaskDelay(1) after multi-person vitals and WASM dispatch prevents IDLE1 starvation / WDT storm",
-      "files": ["firmware/esp32-csi-node/main/edge_processing.c"],
-      "require": [
-        "if (s_cfg.tier >= 2) vTaskDelay(1);",
-        "Yield after WASM dispatch to feed Core 1 watchdog (#683)"
-      ],
-      "rationale": "At edge tier>=2 on N16R8 PSRAM boards, process_frame() runs update_multi_person_vitals() (4 persons × 256 history samples) plus wasm_runtime_on_frame() back-to-back. The vTaskDelay(1) in edge_task() only fires AFTER process_frame() fully returns — if process_frame() takes >5 s (common on PSRAM-backed boards under sustained 30 pps CSI load), IDLE1 on Core 1 never runs and the Task Watchdog Timer fires. The fix adds two vTaskDelay(1) calls inside process_frame(), gated on tier>=2, at the multi-person vitals boundary and after WASM dispatch. Removing them re-opens the WDT storm on N16R8 hardware.",
-      "ref": "https://github.com/ruvnet/RuView/issues/683"
    }
  ]
 }
@@ -1 +1 @@
 .6.6
 .6.5