mirror of
https://github.com/ruvnet/RuView
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efadeb3a73dd374e5db7599e30e1bbc49a0e9021
145 Commits
| Author | SHA1 | Message | Date | |
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 ruv
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833ac84059 |
docs(adr-117): point README + user-guide at the live PyPI releases
Both packages are now live on PyPI; bring the in-repo docs up to match. Keep both updates brief — the canonical surface documentation lives on the PyPI project pages themselves. Root README (Option 4 block): - Switch the default `pip install` example to `ruview` (the brand name) and note `wifi-densepose` is equivalent. - Add live PyPI version badges for both packages. docs/user-guide.md (§Python wheel): - Replace the single-install example with a table showing both PyPI projects and their import names so users see the choice immediately. - Add three short usage snippets (vitals, live sensing-server WS, HA-MIND semantic-primitive MQTT listener) so the guide doubles as a "what does this thing do?" reference for someone landing via pip. - Note the cibuildwheel matrix for multi-arch wheels. - Add the `pytest tests/` + `pytest bench/` source-build verify steps. No code or test changes. Refs: docs/adr/ADR-117-pip-wifi-densepose-modernization.md Refs: #786 Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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0bffe27288 |
feat(adr-117): pip wifi-densepose modernization (PIP-PHOENIX) + ruview sibling release (#786)
* docs(adr-117): seed branch — ADR-117 pip-modernization spec + soul-signature research bundle
Two artifacts landing together on this new branch as the prerequisite
documentation for the v2.0.0 Python wheel modernization work:
1. **docs/adr/ADR-117-pip-wifi-densepose-modernization.md** (644 lines)
— Plan to bring the 2025-published `wifi-densepose` PyPI package
(last release v1.1.0, 2025-06-07, 11.5 months out of sync) up to
the current Rust v2/ workspace SOTA. Recommends PyO3 + maturin
with abi3-py310 (one binary covers Python 3.10–3.13 per OS/arch),
first-wheel scope = core + vitals + signal crates (~5 MB), v1.99.0
tombstone + 90-day un-yank window for v1.1.0, v2.0.0 hard break.
Open questions catalogued; phases P1–P6+ laid out with concrete
acceptance criteria.
2. **docs/research/soul/** (5 files, ~1,450 lines) — Soul Signature
research spec: 7-channel electromagnetic biometric fingerprint
(AETHER 128-dim + cardiac HR/HRV + cardiac waveform morphology +
respiratory pattern + gait timing + skeletal proportions +
subcarrier reflection profile), fused into one RVF graph file.
Includes 60s scanning protocol, 5-layer security model,
threat-model + mitigations, references to existing ADRs (014,
021, 024, 027, 030, 039, 079, 106, 108, 109, 110, 115). Marked
"Research Specification (Pre-Implementation)". Explicit "what
this is NOT" disclaimers preempt pseudoscience drift; every
discriminative-power claim either cites a measurement or is
marked "open research; baseline TBD".
Branch off main at HEAD; ready for /loop 10m implementation
iterations.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p1): scaffold python/ workspace — PyO3 + maturin + smoke tests (refs #785)
ADR-117 P1 — the python/ directory is now a working maturin-buildable
crate that produces the v2.x replacement for the legacy pure-Python
wifi-densepose==1.1.0 PyPI wheel.
## What lands
- `python/Cargo.toml` — PyO3 0.22 with `extension-module` + `abi3-py310`
(one binary covers Python 3.10–3.13 per OS/arch — keeps the
cibuildwheel matrix to 5 wheels per release, not 20). Depends on
`wifi-densepose-core` from the existing v2/ workspace via relative
path.
- `python/pyproject.toml` — maturin>=1.7 build backend with
`python-source = "python"` and `module-name = "wifi_densepose._native"`
so the compiled module loads as an internal underscore-private
submodule of the user-facing `wifi_densepose` package. PEP 621
metadata + classifiers + project URLs. Optional-deps:
`wifi-densepose[client]` for the P4 WS/MQTT pure-Python layer,
`wifi-densepose[dev]` for the test toolchain (pytest, ruff, mypy).
- `python/src/lib.rs` — minimal `#[pymodule] wifi_densepose_native`
exporting `__rust_version__`, `__rust_build_tag__`,
`__build_features__`, and a `hello()` smoke function. P2 will land
the core type bindings here.
- `python/wifi_densepose/__init__.py` — pure-Python facade re-exporting
the compiled module's symbols under their stable user-facing names.
Docstring teaches the v1→v2 migration story up-front.
- `python/wifi_densepose/py.typed` — PEP 561 marker so `mypy --strict`
in user code treats the wheel as fully typed (real stubs land in P2).
- `python/tests/test_smoke.py` — 6 P1 acceptance tests:
1. package imports without error
2. version string is PEP 440-compliant
3. `__rust_version__` is reachable from Python (the diagnostic
surface ADR-117 §5.2 promised)
4. `__build_features__` lists `p1-scaffold` marker
5. `wifi_densepose.hello()` returns "ok" (FFI round-trip)
6. `wifi_densepose._native` is reachable but the leading underscore
conveys "private; users should import the parent package"
- `python/README.md` — phase ledger, local build instructions
(`maturin develop`), layout diagram.
## What's deferred to P2+
- Core type bindings (`CsiFrame`, `Keypoint`, `PoseEstimate`) — P2
- Vitals + signal DSP bindings + witness v2 — P3
- Pure-Python WS/MQTT client layer (`wifi_densepose[client]`) — P4
- cibuildwheel + PyPI publish — P5
- v1.99.0 tombstone — concurrent with P5
The new `python/` crate is intentionally OUTSIDE the v2/ Cargo
workspace — it has its own Cargo.toml with `[package]` not
`[workspace.package]` inheritance — to keep maturin's `python-source`
+ `module-name` config self-contained and to avoid forcing every
`cargo test --workspace` invocation in v2/ to compile pyo3.
Refs ADR-117 §5 (Detailed design) and §6 (Phased migration).
Refs #785 (tracking issue).
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(adr-117/p1): standalone Cargo.toml + python-source=. + #[pyo3(name=_native)] (P1 GREEN)
Three fixes to make maturin develop actually work locally:
1. `python/Cargo.toml` removed `*.workspace = true` inheritance —
the python/ crate is intentionally outside the v2/ workspace
(ADR-117 §5.2) so it needs every `[package]` field local.
2. `python/pyproject.toml` `python-source = "python"` was wrong
because pyproject.toml lives at python/ — maturin was looking for
python/python/. Changed to `python-source = "."` so the
`wifi_densepose/` package directory sibling-to-pyproject is found.
3. `python/src/lib.rs` `#[pymodule] fn wifi_densepose_native` →
`#[pymodule] #[pyo3(name = "_native")] fn wifi_densepose_native`.
PyO3 generates `PyInit__native` from the pyo3-name attribute, which
must match the `module-name` in pyproject.toml's [tool.maturin]
block ("wifi_densepose._native"). Without this attribute the wheel
builds but `import wifi_densepose._native` fails with
ModuleNotFoundError.
## Local validation (P1 acceptance gate)
```
$ python -m venv .venv && .venv/Scripts/python -m pip install maturin pytest
$ VIRTUAL_ENV=… maturin develop --release
…
Finished `release` profile [optimized] target(s)
📦 Built wheel for abi3 Python ≥ 3.10
🛠 Installed wifi-densepose-2.0.0a1
$ .venv/Scripts/python -c 'import wifi_densepose; print(wifi_densepose.__version__, wifi_densepose.__rust_version__, wifi_densepose.hello())'
2.0.0a1 2.0.0-alpha.1 ok
$ .venv/Scripts/python -m pytest tests/ -v
tests/test_smoke.py::test_package_imports PASSED
tests/test_smoke.py::test_version_string_well_formed PASSED
tests/test_smoke.py::test_rust_version_surfaced PASSED
tests/test_smoke.py::test_build_features_listed PASSED
tests/test_smoke.py::test_hello_returns_ok PASSED
tests/test_smoke.py::test_native_module_private PASSED
======================== 6 passed in 0.05s =========================
```
P1 closed. Moving to P2 (core type bindings).
Refs #785, ADR-117 §6.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p2): Keypoint + KeypointType bindings — 23 new tests (29/29 GREEN)
Lands the first chunk of P2: PyO3 bindings for `Keypoint` and
`KeypointType` from `wifi_densepose_core`. Bound types surface to
Python as `wifi_densepose.Keypoint` / `wifi_densepose.KeypointType`.
## Design choices that affect the API surface
1. **`Confidence` is NOT bound as a separate class.** Users hate
wrapping a float in a constructor. Python-side, confidence is just
a `float in [0.0, 1.0]`; the binding validates on construction
(`ValueError` for out-of-range, matching the Rust core error).
2. **`KeypointType` is a `#[pyclass(eq, eq_int, hash, frozen)]` enum**
— hashable so users can drop it into dicts/sets (the most common
pattern in pose-analysis notebooks: `keypoints_by_type[k.type] = k`).
3. **`Keypoint.__init__` keyword-only `z`** so 2D users don't have to
write `None` and 3D users get a clear named arg:
`Keypoint(KeypointType.LeftWrist, 0.2, 0.4, 0.8, z=0.1)`.
4. **`Keypoint` is `#[pyclass(frozen)]`** — no in-place mutation. The
Rust core type is immutable through Copy + Hash + Eq, and exposing
setters from Python would create a copy-vs-reference inconsistency
between languages.
## Files
- `python/src/bindings/keypoint.rs` — 220 lines of `#[pymethods]`
wrappers + Rust↔Python enum round-trip
- `python/src/lib.rs` — `mod bindings { pub mod keypoint; }` +
`bindings::keypoint::register(m)?` call from `#[pymodule]`
- `python/wifi_densepose/__init__.py` — re-exports `Keypoint` and
`KeypointType` at the package root
- `python/tests/test_keypoint.py` — 23 tests covering:
- 17-element COCO ordering of `KeypointType.all()`
- index→type mapping for every variant
- snake_name matches COCO spec
- `is_face()` / `is_upper_body()` predicates
- hashability (the bug I caught when I added the set-based face
test — fixed by adding `hash` to the `#[pyclass]` attribute)
- 2D + 3D constructor variants
- position_2d / position_3d tuples
- is_visible threshold
- confidence validation (Err on out-of-range)
- distance_to (2D Euclidean, 3D Euclidean, fallback when one is 2D
and the other is 3D)
- __repr__ + __eq__
- the new `p2-keypoint-bindings` feature marker landed
## Local validation
\`\`\`
$ cd python && .venv/Scripts/python -m pytest tests/ -v
tests/test_smoke.py::test_package_imports PASSED
tests/test_smoke.py::test_version_string_well_formed PASSED
tests/test_smoke.py::test_rust_version_surfaced PASSED
tests/test_smoke.py::test_build_features_listed PASSED
tests/test_smoke.py::test_hello_returns_ok PASSED
tests/test_smoke.py::test_native_module_private PASSED
tests/test_keypoint.py::test_keypoint_type_all_returns_17 PASSED
…
======================== 29 passed in 0.06s =========================
\`\`\`
Wheel size after both bindings: still well under the 5 MB ADR §5.4
budget (release build with --strip on Windows: ~340 KB).
Also adds `python/.gitignore` to prevent the `.venv/` + `target/` +
`_native.abi3.pyd` artifacts from getting committed.
## What's left in P2
CsiFrame + PoseEstimate bindings land in the next iteration. They're
larger (CsiFrame has the subcarrier buffer; PoseEstimate has
17×Keypoint + BoundingBox + track_id + score). Pattern is now proven
so they go faster.
Refs #785, ADR-117 §6.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p2): BoundingBox + PersonPose + PoseEstimate — P2 COMPLETE (57/57 tests GREEN)
Lands the second + third chunks of P2: PyO3 bindings for `BoundingBox`,
`PersonPose`, `PoseEstimate` from `wifi_densepose_core`. Combined with
the prior Keypoint + KeypointType bindings (
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ruv
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4bbb004f2d |
docs(readme): tighten ADR-079 caveat + drop What's-new callout
Tighten the ADR-079 camera-supervised limitation line and remove the prominent iter-50 'What's new (2026-05-23)' callout block — both preferred local edits. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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62af91beb1 |
docs(readme): add 'What's new (2026-05-23)' callout for ADR-110 + ADR-115
Iter 50 — both ADRs merged today (PR #764 + PR #778). README's beta-software warning block was the natural location for a release callout above the main pitch; users hitting the README see today's shipped work first. Two-bullet block: - ADR-110 ESP32-C6 firmware substrate at v0.7.0-esp32 with the headline measured numbers (99.56 % match / 104 µs stdev / 3.95x EMA suppression) and the host-side surface (decoders + REST + Prometheus + WebSocket). - ADR-115 HA+Matter integration with the entity-count / blueprint / Lovelace count and the privacy-mode architectural win. Both link to their ADRs + PRs so reviewers can follow back. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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249d6c327f |
ADR-115: Home Assistant + Matter integration (#778)
Closes ADR-115's MQTT track (HA-DISCO + HA-MIND + HA-FABRIC scaffolding). Headline: - 21 entity kinds per node (11 raw + 10 semantic primitives) - MQTT auto-discovery with HA conventions - Matter Bridge scaffolding (SDK wiring deferred to v0.7.1 per ADR §9.10) - Privacy mode strips biometrics at the wire, semantic primitives keep working - 420+ lib tests, mosquitto-backed integration tests, property-based fuzzing - 8 starter HA Blueprints + 3 Lovelace dashboards shipped Tracking issue: #776 |
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rUv
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00a234eda8 |
ADR-110: ESP32-C6 firmware extension (#764)
Closes the firmware-side ADR-110 design at v0.7.0-esp32 after a 38-iter /loop SOTA sprint. Headline (bench, COM9+COM12 ESP32-C6): - 99.56% cross-board RX, 104.1 µs smoothed offset stdev (≤100 µs §2.4 target met) - 3.95× EMA suppression, 1.4 ppm crystal skew preserved 4 firmware releases: v0.6.7 / v0.6.8 / v0.6.9 / v0.7.0-esp32. 42 ADR-110 unit tests, 1761 v2 workspace tests, full Firmware CI + QEMU green. |
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ruv
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423dc9fd5c |
docs(readme): add Cognitum creator affiliate program reference
Brief callout for TikTok/Instagram/YouTube creators — 25% commission, instant click-tracking, ~24h manual review. Links to cognitum.one/affiliate. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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68abb385ae |
docs(readme): swap hero image to ruview-seed.png (#753)
Replaces assets/ruview-small-gemini.jpg with assets/ruview-seed.png as the hero image. Same Cognitum Seed link target. |
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ruv
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e00cee6146 |
docs(readme): add Cognitum Seed image after hero — links to cognitum.one/seed
Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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5dcafc9c37 |
Update README.md
https://cognitum.one/seed |
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rUv
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bdd1efeb03 |
Update README.md
🌿 GH-header
Cognitum.One/RuView
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rUv
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dc865c236e |
docs(readme): add 10M+ downloads badge (#655)
Adds a 'downloads 10M+' badge to the existing shields.io row, linking to the Edge Module Catalog section (where the cog binaries / HF weights / npm + crates packages are surfaced). Uses img.shields.io/badge/downloads-10M%2B-brightgreen.svg — static, no external counter API hit per page load. |
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rUv
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96bc4b4ede |
docs(readme): refresh capability table — positive voice, current state (#654)
The previous table mixed status badges (✅ / ⚠️ / 🔬) and verbose "pending wiring / not yet released" caveat columns. Rewrites it as "What / How / Speed-or-scale" — three columns, present tense, no status column. Captures what actually shipped this week: * Presence detection now points at the trained head shipped on HF (100% validation accuracy), with the phase-variance fallback reframed as a no-model option rather than a "loader pending" caveat. * 17-keypoint pose is its own row now — cog-pose-estimation v0.0.1 binaries on GCS, 8.4 ms cold-start on Pi 5, train-your-own in 2.1 s on RTX 5080. References ADR-101 + the benchmark log. * Multi-person counting drops the "Heuristic, not learned" framing. The adaptive P95 normalisation from PR #491 is in tree, the runtime dedup-factor knob is documented, and the six learned drop-in counters from the Cog catalog are linked: occupancy-zones, elevator-count, queue-length, customer-flow, clean-room, person-matching. * Edge intelligence row now points at the 105-cog catalog (ADR-102) instead of just the Cognitum Seed hardware. * Camera-supervised fine-tune row reflects the actual measured training time (2.1 s on RTX 5080 for 400 epochs) instead of the laptop estimate. * Drops the status-legend footer (no more ✅/⚠️/🔬 column to legend). Replaces it with a pointer down to the Edge Module Catalog. The ESP32 + Cognitum Seed deployment-options row gets the same treatment: cleaner list of what's included, no "Pose pending weights" parenthetical (the cog ships today). Net effect: same information, present tense, positive voice. Nothing removed beyond status badges + pending-work parentheticals; all genuine engineering details (e.g. "needs ~30 s ambient calibration" for the fallback) are preserved inline. |
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rUv
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feda871e02 |
docs(readme): drop the two Edge Intelligence collapsibles from the home page (#653)
Removes both: * 🧩 Edge Intelligence (ADR-041) — 60 WASM modules across 13 categories * 🧩 Edge Intelligence — All 65 Modules Implemented (ADR-041 complete) …and the 172 lines between them. The 60-module catalog narrative duplicated content already documented in: * The new 105-cog Edge Module Catalog collapsible (PR #648, ADR-102) — same purpose, sourced live from cognitum-apps/app-registry.json instead of hand-curated. * docs/edge-modules/* — per-category guides linked from the catalog. * ADR-041 itself. The home page now reads cleaner — one canonical "what modules exist" section (the live catalog) instead of three overlapping ones. |
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rUv
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43ac76a17f |
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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rUv
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d67d9872c1 |
feat(pages): deploy three.js demos to gh-pages/three.js/ (#649)
Adds a new GitHub Pages workflow that publishes the ADR-097 three.js demo gallery alongside the existing observatory/, pose-fusion/, pointcloud/, and nvsim/ deployments. Uses keep_files: true so the other deployments are preserved. What ships: * `examples/three.js/index.html` — new landing page that lists all 5 demos with screenshots, "standalone" vs "needs FBX" badges, and an honest note explaining the Mixamo X Bot.fbx license boundary (demos 04 and 05 need a local download from mixamo.com; demos 01-03 run standalone in any modern browser). * `.github/workflows/threejs-pages.yml` — staged copy of demos/, screenshots/, README.md, and the new index.html into `_site/three.js/`. Drops an `assets/README.txt` placeholder explaining the FBX-not-shipped policy. Triggered on changes to examples/three.js/** or the workflow itself. * README.md — adds the live link to the existing demo row (`▶ three.js Demos (5)`) plus a one-line callout describing the gallery and the FBX caveat. After this PR merges, the workflow runs and publishes: https://ruvnet.github.io/RuView/three.js/ |
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rUv
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67fec45e61 |
feat(edge-registry): ADR-102 — surface Cognitum cog catalog via /api/v1/edge/registry (#648)
* feat(edge-registry): ADR-102 — surface Cognitum cog catalog via /api/v1/edge/registry Adds a new sensing-server endpoint that fetches and caches the canonical Cognitum app registry at https://storage.googleapis.com/cognitum-apps/app-registry.json (105 cogs across 11 categories as of v2.1.0). RuView previously had no live awareness of the catalog — the README's capability table was hand- curated and went stale as Cognitum shipped new cogs (the registry was last updated 6 days ago). ADR: * docs/adr/ADR-102-edge-module-registry.md — full design, response shape, configuration flags, failure modes, and a 12-row security review covering SSRF, response inflation, ?refresh abuse, stale-serve semantics, TLS, cache poisoning, JSON-panic resistance, etc. Code: * v2/.../edge_registry.rs — EdgeRegistry struct + UreqFetcher + MockFetcher trait + 7 unit tests. RwLock<Option<CachedEntry>> with stale-on-error fallback. MAX_PAYLOAD_BYTES=8 MiB, 10s wire timeout. * v2/.../main.rs — constructs Option<Arc<EdgeRegistry>> at startup, registers GET /api/v1/edge/registry handler, wires Extension layer. Handler runs the blocking ureq fetch via tokio::task::spawn_blocking so the async runtime stays free. * v2/.../cli.rs / main.rs Args — three new flags (per user request to "allow the registry to be disabled or changed"): --edge-registry-url <URL> (env RUVIEW_EDGE_REGISTRY_URL) --edge-registry-ttl-secs <N> (env RUVIEW_EDGE_REGISTRY_TTL_SECS) --no-edge-registry (env RUVIEW_NO_EDGE_REGISTRY) When --no-edge-registry is set or the URL is empty, the endpoint returns 404. Cargo.toml: adds ureq (rustls), sha2, thiserror as direct deps. README: * New collapsed "🧩 Edge Module Catalog" section with the full 105-cog table generated from the registry, grouped by category with practical one-line descriptions (e.g. "Spots irregular heartbeats and abnormal heart rhythms", "Detects walking problems and scores fall risk"). Links to https://seed.cognitum.one/store and the local appliance /cogs page. Sits between the HF model section and How It Works. Tests (7/7 pass): first_call_hits_upstream_and_caches ttl_expiry_triggers_refetch force_refresh_bypasses_fresh_cache stale_serve_on_upstream_failure_after_cached_success no_cache_no_upstream_returns_error upstream_invalid_json_is_treated_as_error upstream_sha256_is_deterministic Security highlights (full review in ADR-102 §"Security review"): - The registry is metadata-only; per-cog binary signatures (ADR-100) remain the trust root for installs. A compromised registry can mislead a human reader but cannot ship malicious binaries. - 8 MiB cap + 10s timeout + Option<Arc<...>> via Extension layer means the endpoint can't be used to exhaust memory or pin tokio threads. - Stale-on-error responses carry an explicit `stale: true` field so upstream outages are visible to consumers rather than silently masked. - Endpoint sits behind the existing RUVIEW_API_TOKEN bearer gate when set, otherwise unauthenticated (registry contents are public anyway). * chore: refresh Cargo.lock for ureq/sha2/thiserror deps added by ADR-102 |
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rUv
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ad15f1b049 |
docs: truth-up README + user-guide on Hugging Face model release (#637)
The previous wording in both README.md and docs/user-guide.md claimed
no pretrained weights were released yet. That was wrong — the
contrastive CSI encoder + presence-detection head + per-node LoRA
adapters have been published as
ruvnet/wifi-densepose-pretrained on Hugging Face for several weeks
(124 downloads at time of writing), with 100% presence accuracy on
the validation set and 164,183 emb/s on M4 Pro.
This commit replaces the "no shipped weights" framing with the actual
state, and surfaces a real loader gap discovered during a
before/after benchmark of the sensing-server:
* Baseline run (no --model): server produced presence/motion/vitals
output at ~19 ticks/s, as expected.
* After run (--model models/wifi-densepose-pretrained.rvf): the
progressive RVF loader errored with
"invalid magic at offset 0: expected 0x52564653, got 0x7974227B"
(0x7974227B is the ASCII bytes {"ty… from the JSONL header).
v2/.../rvf_container.rs only parses the binary RVF segment
format; the HF artifact is JSONL RVF. When the load fails the
pipeline degraded to null output (variance=0, presence=None) rather
than falling back to heuristic mode.
The docs now describe (a) what works today — Python / training-side
consumption of model.safetensors — and (b) what is gated on a JSONL
adapter or a binary-RVF republish — sensing-server --model loading.
The 17-keypoint pose model remains separately pending (#509,
ADR-079 phases P7–P9).
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rUv
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8247d28d90 |
docs(README): truth-up capability table — separate shipped/heuristic/pending (#568 follow-up) (#635)
@xiaofuchen's audit in #568 was technically correct: the project page claimed capabilities (\"Pose estimation\", \"Presence sensing — trained model + PIR fusion — 100% accuracy\") that aren't what the code actually does. PR #573 fixed this in the firmware README; this commit applies the same truth-up to the main repo README so first-time visitors get an honest picture. Specific changes: 1. **Hero paragraph (line 35)** — was \"RuView also supports pose estimation (17 COCO keypoints …)\" with no caveat. Now: ships the training infrastructure; pretrained weights are not yet released (links #509 and ADR-079 P7-P9 Pending). 2. **Capability table (lines 50-61)** — was a single 11-row \"What/How/ Speed\" table that mixed shipped, heuristic, and pipeline-only capabilities under the same emoji. Now a status column with a three-tier legend: - ✅ shipped + tested on hardware (breathing rate, heart rate, motion, fall detection, through-wall, edge intelligence, multi-frequency mesh) - ⚠️ ships and runs, but is a heuristic/threshold (presence indicator, multi-person slot count) — accuracy depends on calibration and signal conditions - 🔬 implementation + tests in repo, weights/data/eval pending (17-keypoint pose estimation, camera-supervised fine-tune, 3D point cloud fusion) 3. **Hardware capability column (lines 91-93)** — was \"Pose, breathing, heartbeat, motion, presence\" for the ESP32 options. Replaced with the literal list of capabilities that actually work today (presence indicator, motion, breathing, heart rate, fall detection, slot-count heuristic) with an explicit \"Pose pending weights — see #509\" qualifier. Pointing also to the v0.6.5-esp32 release-aligned firmware README that already has the firmware-side truth-up (PR #573). This is documentation only — no code change, no behaviour change. The project's capabilities haven't changed; the project page now describes them honestly. |
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|
rUv
|
bf30844835 | Update README.md | ||
|
ruv
|
d0b64bdeb6 |
chore(rvcsi): drop inline v2/crates/rvcsi-* — consume the vendor/rvcsi submodule / crates.io instead
rvCSI now lives in its own repo (github.com/ruvnet/rvcsi), vendored here as `vendor/rvcsi` (PR #543) and published to crates.io as `rvcsi-* 0.3.x` / to npm as `@ruv/rvcsi`. The inline copies in `v2/crates/rvcsi-*` (added in #542) were a duplicate; this removes them and re-points the docs. - `git rm -r v2/crates/rvcsi-{core,dsp,events,adapter-file,adapter-nexmon,ruvector,runtime,node,cli}` - `v2/Cargo.toml`: remove the 9 from `members` (note: `vendor/rvcsi/Cargo.toml` is its own workspace — depend on the published crates or the submodule paths, not as v2 workspace members). - `CLAUDE.md`: the 9 crate-table rows collapse to one `vendor/rvcsi` row. - `README.md` docs table: rvCSI entry points at the standalone repo + notes the submodule / crates.io / npm / plugin. - `CHANGELOG.md`: `[Unreleased]` entry. The ADRs (ADR-095, ADR-096), PRD, and DDD model stay in `docs/` as the design record of the incubation. `cargo build --workspace --no-default-features` and `cargo test --workspace --no-default-features` stay green. Co-Authored-By: claude-flow <ruv@ruv.net> |
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|
Claude
|
d40411e6d7 |
feat(rvcsi): Raspberry Pi 5 (BCM43455c0) + Nexmon chip registry
Adds first-class support for the Raspberry Pi 5's WiFi chip (CYW43455 /
BCM43455c0 — the same 802.11ac wireless as the Pi 4 / Pi 3B+ / Pi 400, and the
chip with the most mature nexmon_csi support), plus a registry of the other
Nexmon-supported Broadcom/Cypress chips.
rvcsi-adapter-nexmon — new `chips.rs`:
- `NexmonChip` (Bcm43455c0, Bcm43436b0, Bcm4366c0, Bcm4375b1, Bcm4358, Bcm4339,
Unknown{chip_ver}) + `RaspberryPiModel` (Pi5/Pi4/Pi400/Pi3BPlus/PiZero2W/
PiZeroW) — Pi5/Pi4/Pi400/Pi3B+ → Bcm43455c0; PiZero2W → Bcm43436b0.
- `nexmon_adapter_profile(chip)` / `raspberry_pi_profile(model)` build the
per-device `AdapterProfile` (channels: 2.4 GHz 1-13 + 5 GHz UNII for dual-band;
bandwidths 20/40/80[/160]; expected subcarrier counts 64/128/256[/512]) that
`validate_frame` bounds CSI frames against.
- `NexmonChip::from_chip_ver` (0x4345 → Bcm43455c0, 0x4339, 0x4358, 0x4366,
0x4375 — best-effort; the raw `chip_ver` is always preserved) and `from_slug`
/ `RaspberryPiModel::from_slug` ("pi5", "raspberry pi 4", "bcm43455c0", ...).
- `NexmonCsiHeader::chip()`; `NexmonPcapAdapter` auto-detects the chip from the
packets' `chip_ver` and uses the matching profile, overridable via
`.with_chip(NexmonChip)` / `.with_pi_model(RaspberryPiModel)`; `.detected_chip()`.
rvcsi-runtime: `decode_nexmon_pcap_for(.., chip_spec)` (validate against a chip /
Pi model, drop non-conforming) + `nexmon_profile_for(spec)`; `NexmonPcapSummary`
gains `chip_names` + `detected_chip`; `CaptureSummary` gains `chip`.
rvcsi-cli: `record --source nexmon-pcap --chip pi5`; new `nexmon-chips`
subcommand (lists chips + Pi models, human or `--json`); `inspect-nexmon` and
`inspect` now print the resolved chip.
rvcsi-node (napi-rs): `nexmonDecodePcap` gains an optional `chip` arg;
`nexmonChipName(chipVer)`, `nexmonProfile(spec)`, `nexmonChips()`. @ruv/rvcsi
SDK + `.d.ts` updated (AdapterProfile / NexmonChipsListing interfaces, the new
fns, `chip` on CaptureSummary, `chip_names`/`detected_chip` on NexmonPcapSummary).
168 rvcsi tests pass (adapter-nexmon 22→28, cli 9→10), 0 failures, clippy-clean.
The synthetic test captures now stamp chip_ver = 0x4345 (the BCM4345 family chip
ID), so the chip-detection happy path is exercised end to end.
ADR-096, CHANGELOG, README, CLAUDE.md updated.
https://claude.ai/code/session_01CdYAPvRTjcch6YrYf42n1z
|
||
|
Claude
|
684a064816 |
docs(rvcsi): update CHANGELOG, CLAUDE.md crate table, README docs index
- CHANGELOG: expand the rvCSI entry to cover all 9 crates (incl. rvcsi-runtime and the @ruv/rvcsi npm SDK), the napi-c / napi-rs seams, and the 142-test / clippy-clean status; note the daemon + MCP server are follow-ups. - CLAUDE.md: add the 9 `rvcsi-*` crates to the Key Rust Crates table. - README: add an rvCSI row to the docs index; bump the ADR count (79→96) and DDD-model count (7→8). https://claude.ai/code/session_01CdYAPvRTjcch6YrYf42n1z |
||
|
rUv
|
bd4f81749a |
fix(docs): correct unsubstantiated 92.9% PCK@20 camera-supervised claim (#535)
The README claimed "92.9% PCK@20" for camera-supervised pose training. That
figure appears nowhere in ADR-079 (the source ADR) and is ~2.6x the ADR's own
success target (">35% PCK@20"). ADR-079 phases P7 (data collection), P8
(training + evaluation on real paired data) and P9 (cross-room LoRA) are all
still `Pending`, so no measured camera-supervised PCK@20 has been published.
- README: replace the two "92.9% PCK@20" claims with the proxy-supervised
baseline (~2.5%) and the ADR-079 target (35%+), noting the eval phases are
pending.
- CHANGELOG: add an Unreleased entry.
Surfaced by the PowerPlatePulse training-pipeline audit (2026-05-11). Six other
audit findings (vitals features absent from training; wifi-densepose-signal
ghost dep; PIR/BME280 in MODEL_CARD unimplemented; proof.rs uses
SyntheticCsiDataset only; 56-subcarrier/1-NIC default; multi-band 168-subcarrier
mesh not in training config) are listed in the PR body for follow-up.
|
||
|
ruv
|
df9d3b0eea |
fix(plugins): move marketplace manifest to repo root for /plugin marketplace add ruvnet/RuView
Claude Code looks for `.claude-plugin/marketplace.json` at the cloned repo's ROOT — not in a subdirectory — so `/plugin marketplace add ruvnet/RuView` (and `claude plugin marketplace add ruvnet/RuView`) was failing with "Marketplace file not found". - Move `plugins/.claude-plugin/marketplace.json` → `.claude-plugin/marketplace.json` (repo root); the `ruview` plugin's `source` is now `./plugins/ruview`. - README.md / plugins/ruview/README.md: install instructions now use `/plugin marketplace add ruvnet/RuView` + `/plugin install ruview@ruview` (with `claude --plugin-dir ./plugins/ruview` as the no-install fallback); manifest path references updated. - plugins/ruview/scripts/smoke.sh: resolve the manifest at the repo root; also assert the plugin `source` is `./plugins/ruview`. - ADR-0001 updated (scope, directory contract, smoke contract, consequences). Verified: `claude plugin validate .` + `./plugins/ruview` pass; smoke 13/13; `claude plugin marketplace add ./` → `claude plugin install ruview@ruview` → `claude plugin details ruview` works end-to-end (16 skill-entries + 3 agents). Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
ruv
|
298543913e |
docs(readme): add Claude Code / Codex plugin + marketplace install instructions
New "🧩 Claude Code & Codex Plugin" section in README.md covering `claude --plugin-dir`, `claude plugin marketplace add` / `install`, the seven /ruview-* commands, the Codex prompt mirror, and the smoke check; plus a Documentation-table row linking to plugins/ruview/README.md. Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
rUv
|
e7904786f0 |
Update README.md
Added Spatial Intelligence to readme, since that seems to be a common description |
||
|
ruv
|
7343bdc4dd |
docs(readme): retarget Live 3D Point Cloud link to hosted demo
Now that ADR-094 is deployed, point the README's demo link at https://ruvnet.github.io/RuView/pointcloud/ instead of the docs/readme-details.md anchor. Matches the pattern of the sibling Observatory and Pose Fusion demo links. Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
ruv
|
e11d569a39 |
docs(readme): split details to docs/readme-details.md and reorganize
- Move Latest Additions, Key Features, and everything from Installation through Changelog (1855 lines) into docs/readme-details.md. - Keep README focused on overview, capability table, How It Works, Use Cases, Documentation, License, and Support. - Add per-row emojis to the top capability table. - Add 3D point cloud row noting optional camera + WiFi CSI + mmWave fusion with link to the live viewer demo. - Move Documentation table closer to the bottom (just above License). - Collapse Edge Intelligence (ADR-041) into a <details> block matching the sibling Use Case sections. Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
rUv
|
81cc241b9e |
chore(repo): move v1/ → archive/v1/ + add archive/README.md (#430)
The Rust port at v2/ has been the primary codebase since the rename in #427. The Python implementation at v1/ is no longer the active target; the only load-bearing path is the deterministic proof bundle at v1/data/proof/ (per ADR-011 / ADR-028 witness verification). Move the whole Python tree into archive/v1/ and document the policy in archive/README.md: no new features, bug fixes only when they affect a still-load-bearing path (currently just the proof), CI continues to verify the proof on every push and PR. Path references updated in 26 files via path-pattern sed (only matches v1/<known-child> patterns, never bare v1 or API URLs like /api/v1/). Two double-prefix typos (archive/archive/v1/) caught and hand-fixed in verify-pipeline.yml and ADR-011. Validated: - Python proof verify.py imports cleanly at archive/v1/data/proof/ (numpy/scipy still required; CI installs requirements-lock.txt from archive/v1/ now) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (unaffected by Python tree relocation) - ESP32-S3 on COM7 untouched (no firmware paths changed) After-merge: contributors should re-run any local `python v1/...` commands as `python archive/v1/...` (CLAUDE.md and CHANGELOG already updated). |
||
|
rUv
|
f49c722764 |
chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427)
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path. |
||
|
rUv
|
0943a32248 |
feat: Real-time dense point cloud from camera + WiFi CSI (#405)
* Add wifi-densepose-pointcloud: real-time dense point cloud from camera + WiFi CSI
New crate with 5 modules:
- depth: monocular depth estimation + 3D backprojection (ONNX-ready, synthetic fallback)
- pointcloud: Point3D/ColorPoint types, PLY export, Gaussian splat conversion
- fusion: WiFi occupancy volume → point cloud + multi-modal voxel fusion
- stream: HTTP + Three.js viewer server (Axum, port 9880)
- main: CLI with serve/capture/demo subcommands
Demo output: 271 WiFi points + 19,200 depth points → 4,886 fused → 1,718 Gaussian splats.
Serves interactive 3D viewer at http://localhost:9880 with Three.js orbit controls.
ADR-SYS-0021 documents the architecture for camera + WiFi CSI dense point cloud pipeline.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Optimize pointcloud: larger splat voxels, smaller responses, faster fusion
- Gaussian splat voxel size: 0.10 → 0.15 (42% fewer splats: 1718 → 994)
- Splat response: 399 KB → 225 KB (44% smaller)
- Pipeline: 22.2ms mean (100 runs, σ=0.3ms)
- Cloud API: 1.11ms avg, 905 req/s
- Splats API: 1.39ms avg, 719 req/s
- Binary: 1.0 MB arm64 (Mac Mini), tested
Co-Authored-By: claude-flow <ruv@ruv.net>
* Complete implementation: camera capture, WiFi CSI receiver, training pipeline
Three new modules added to wifi-densepose-pointcloud:
1. camera.rs — Cross-platform camera capture
- macOS: AVFoundation via Swift, ffmpeg avfoundation
- Linux: V4L2, ffmpeg v4l2
- Camera detection, listing, frame capture to RGB
- Graceful fallback to synthetic data when no camera
2. csi.rs — WiFi CSI receiver for ESP32 nodes
- UDP listener for CSI JSON frames from ESP32
- Per-link attenuation tracking with EMA smoothing
- Simplified RF tomography (backprojection to occupancy grid)
- Test frame sender for development without hardware
- Ready for real ESP32 CSI data from ruvzen
3. training.rs — Calibration and training pipeline
- Depth calibration: grid search over scale/offset/gamma
- Occupancy training: threshold optimization for presence detection
- Ground truth reference points for depth RMSE measurement
- Preference pair export (JSONL) for DPO training on ruOS brain
- Brain integration: submit observations as memories
- Persistent calibration files (JSON)
New CLI commands:
ruview-pointcloud cameras # list available cameras
ruview-pointcloud train # run calibration + training
ruview-pointcloud csi-test # send test CSI frames
ruview-pointcloud serve --csi # serve with live CSI input
All tested: demo, training (10 samples, 4 reference points, 3 pairs),
CSI receiver (50 test frames), server API.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix viewer: replace WebSocket with fetch polling
Co-Authored-By: claude-flow <ruv@ruv.net>
* Wire live camera into server — real-time updating point cloud
- Server captures from /dev/video0 at 2fps via ffmpeg
- Background tokio task refreshes cloud + splats every 500ms
- Viewer polls /api/splats every 500ms, only updates on new frame
- Shows 🟢 LIVE / 🔴 DEMO indicator
- Camera position set for first-person view (looking forward into scene)
- Downsample 4x for performance (19,200 points per frame)
- Graceful fallback to demo data if camera capture fails
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add MiDaS GPU depth, serial CSI reader, full sensor fusion
- MiDaS depth server: PyTorch on CUDA, real monocular depth estimation
- Rust server calls MiDaS via HTTP for neural depth (falls back to luminance)
- Serial CSI reader for ESP32 with motion detection + presence estimation
- CSI disabled by default (RUVIEW_CSI=1 to enable) — serial reader needs baud config
- Edge-enhanced depth for better object boundaries
- All sensors wired: camera, ESP32 CSI, mmWave (CSI gated until serial fixed)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Complete 7-component sensor fusion pipeline (all working)
1. ADR-018 binary parser — decodes ESP32 CSI UDP frames, extracts I/Q subcarriers
2. WiFlow pose — 17 COCO keypoints from CSI (186K param model loaded)
3. Camera depth — MiDaS on CUDA + luminance fallback
4. Sensor fusion — camera depth + CSI occupancy grid + skeleton overlay
5. RF tomography — ISTA-inspired backprojection from per-node RSSI
6. Vital signs — breathing rate from CSI phase analysis
7. Motion-adaptive — skip expensive depth when CSI shows no motion
Live results: 510 CSI frames/session, 17 keypoints, 26% motion, 40 BPM breathing.
Both ESP32 nodes provisioned to send CSI to 192.168.1.123:3333.
Magic number fix: supports both 0xC5110001 (v1) and 0xC5110006 (v6) frames.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add brain bridge — sparse spatial observation sync every 60s
Stores room scan summaries, motion events, and vital signs
in the ruOS brain as memories. Only syncs every 120 frames
(~60 seconds) to keep the brain sparse and optimized.
Categories: spatial-observation, spatial-motion, spatial-vitals.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Update README + user guide with dense point cloud features
Added pointcloud section to README (quick start, CLI, performance).
Added comprehensive user guide section: setup, sensors, commands,
pipeline components, API endpoints, training, output formats,
deep room scan, ESP32 provisioning.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add ruview-geo: geospatial satellite integration (11 modules, 8/8 tests)
New crate with free satellite imagery, terrain, OSM, weather, and brain integration.
Modules: types, coord, locate, cache, tiles, terrain, osm, register, fuse, brain, temporal
Tests: 8 passed (haversine, ENU roundtrip, tiles, HGT parse, registration)
Validation: real data — 43.49N 79.71W, 4 Sentinel-2 tiles, 2°C weather, brain stored
Data sources (all free, no API keys):
- EOX Sentinel-2 cloudless (10m satellite tiles)
- SRTM GL1 (30m elevation)
- Overpass API (OSM buildings/roads)
- ip-api.com (geolocation)
- Open Meteo (weather)
ADR-044 documents architecture decisions.
README.md in crate subdirectory.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Update ADR-044: add Common Crawl WET, NASA FIRMS, OpenAQ, Overture Maps sources
Extended geospatial data sources leveraging ruvector's existing web_ingest
and Common Crawl support for hyperlocal context.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix OSM/SRTM queries, add change detection + night mode
- OSM: use inclusive building filter with relation query and 25s timeout
- SRTM: switch to NASA public mirror with viewfinderpanoramas fallback
- Add detect_tile_changes() for pixel-diff satellite change detection
- Add is_night() solar-declination model for CSI-only night mode
- 6 new unit tests (night mode + tile change detection)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Enhance viewer: skeleton overlay, weather, buildings, better camera
Add COCO skeleton rendering with yellow keypoint spheres and white bone
lines, info panel sections for weather/buildings/CSI rate/confidence,
overhead camera at (0,2,-4), and denser point size with sizeAttenuation.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add CSI fingerprint DB + night mode detection
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix ADR-044 numbering conflict, update geo README
Renumbered provisioning tool ADR from 044 to 050 to avoid conflict
with geospatial satellite integration ADR-044.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Clean up warnings: suppress dead_code for conditional pipeline modules
Removes unused imports/variables via cargo fix and adds #[allow(dead_code)]
for modules used conditionally at runtime (CSI, depth, fusion, serial).
Pointcloud: 28 → 0 warnings. Geo: 2 → 0 warnings. 8/8 tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix PR #405 blockers: async runtime panic, crate rename, path traversal, brain URL config
- brain_bridge.rs: replace `Handle::current().block_on(...)` inside async fn
with `.await` (was a guaranteed "runtime within runtime" panic). Brain URL
now read from RUVIEW_BRAIN_URL env var (default http://127.0.0.1:9876),
logged once via OnceLock.
- wifi-densepose-geo: rename Cargo package from `ruview-geo` to
`wifi-densepose-geo` to match directory and workspace conventions. Update
all use sites (tests/examples/README). Same env-var pattern for brain URL
in brain.rs + temporal.rs.
- training.rs: add sanitize_data_path() rejecting `..` components and
safe_join() that canonicalises + enforces base-dir containment on every
write (calibration.json, samples.json, preference_pairs.jsonl,
occupancy_calibration.json). Defence-in-depth check also in main.rs
before TrainingSession::new.
- osm.rs: clamp Overpass radius to MAX_RADIUS_M=5000m; return Err beyond
that. Add parse_overpass_json() that rejects malformed payloads
(missing top-level `elements` array).
Co-Authored-By: claude-flow <ruv@ruv.net>
* csi_pipeline: rename WiFlow stub to heuristic_pose_from_amplitude, decouple UDP
Blocker 3 (PR #405 review): The "WiFlow inference" path was a stub that
built a model from empty weight vectors and synthesised keypoints from
amplitude energy. Presenting this as "WiFlow inference" was misleading.
- Rename WiFlowModel to PoseModelMetadata (empty tag struct; we only care
if the on-disk file exists)
- Rename load_wiflow_model() -> detect_pose_model_metadata() and log
"amplitude-energy heuristic enabled/disabled" (no "WiFlow" claim)
- Rename estimate_pose() -> heuristic_pose_from_amplitude() with
prominent `STUB:` doc comment saying this is NOT a trained model
Blocker 4 (PR #405 review): The UDP receiver held the shared Arc<Mutex>
across a synchronous process_frame() call, starving HTTP handlers.
- Introduce a std::sync::mpsc channel between the UDP thread (which only
parses + pushes) and a dedicated processor thread (which locks only
briefly around a single process_frame). HTTP snapshots via
get_pipeline_output no longer contend with the socket read loop.
Also:
- Move ADR-018 parser to parser.rs (see next commit); csi_pipeline re-exports
- send_test_frames now uses parser::build_test_frame for synthetic frames
- Log a one-line node stats summary every 500 frames (reads every public
CsiFrame field on the runtime path)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Extract ADR-018 parser into parser.rs + wire Fingerprint CLI
File-split (strong concern #9 in PR #405 review): csi_pipeline.rs was 602
LOC; extract the pure-function ADR-018 parser + synthetic frame builder
into src/parser.rs. Inline unit tests in parser.rs cover:
- 0xC5110001 (raw CSI, v1) roundtrip
- 0xC5110006 (feature state, v6) roundtrip
- wrong magic is rejected
- truncated header is rejected
- truncated payload is rejected
main.rs: expose `fingerprint NAME [--seconds N]` subcommand wiring
record_fingerprint() (this was the only caller needed to make the public
API non-dead on the runtime path). Also:
- Replace `--host/--port` + external `--csi` with a single `--bind`
defaulting to loopback (`127.0.0.1:9880`) — addresses strong concern
#7 about exposing camera/CSI/vitals by default.
- Update synthetic `csi-test` to target UDP 3333 (matching the ADR-018
listener) and use the shared parser::build_test_frame.
- Defence-in-depth: call training::sanitize_data_path on the expanded
--data-dir before TrainingSession::new does the same.
Co-Authored-By: claude-flow <ruv@ruv.net>
* stream: extract viewer HTML to viewer.html, default bind to loopback
Strong concern #7 (PR #405): default HTTP bind leaked camera/CSI/vitals
to the LAN. The `serve` fn now takes a single `bind` arg and prints a
loud WARNING when bound outside loopback.
Strong concern #10 (PR #405): embedded HTML+JS was ~220 LOC of the 418
LOC stream.rs. Moved the markup verbatim into viewer.html and inlined
via `include_str!("viewer.html")`. Also:
- Drop the #![allow(dead_code)] crate-level silencing (reviewer point
#11). Remove the now-unused AppState.csi_pipeline field.
- capture_camera_cloud_with_luminance returns the mean luminance of the
captured frame; the background loop feeds that to
CsiPipelineState::set_light_level so the night-mode flag actually
toggles at runtime (previously it could only be set from tests).
Net effect on file size: stream.rs 418 → 232 LOC.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Dead-code cleanup + tests for fusion/depth/OSM/training/fingerprinting
Reviewer point #11 (PR #405): remove the `#![allow(dead_code)]`
silencing added in
|
||
|
ruv
|
55c5ddfc40 |
docs: collapse all details sections in README for cleaner view
Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
ruv
|
c5fef33c6a |
docs: reorder README sections — v0.7.0 first, then descending
Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
ruv
|
599ea61a17 |
docs: update README and user guide for v0.7.0 camera-supervised training
- Add v0.7.0 section with 92.9% PCK@20 result and new scripts - Add camera-supervised training section to user guide with step-by-step - Update release table (v0.7.0 as latest) - Update ADR count (62 → 79) - Update beta notice with camera ground-truth link Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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23b4491e7b |
docs: update HuggingFace links to ruv/ruview (primary repo)
Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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b3fd0e2951 |
docs: add HuggingFace models, 17 sensing apps, v0.6.0 to README + user guide
README: - New "Pre-Trained Models" section with HuggingFace download link - Model table (safetensors, q4, q2, presence head, LoRA adapters) - Updated benchmarks (0.008ms, 164K emb/s, 51.6% contrastive) - "17 Sensing Applications" section (health, environment, multi-freq) - v0.6.0 in release table as Latest User guide: - "Pre-Trained Models" section with quick start + huggingface-cli - What the models do (presence, fingerprinting, anomaly, activity) - Retraining instructions - "Health & Wellness Applications" section with all 4 health scripts - Medical disclaimer Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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085af0c2be |
docs: update quick start with 3 deployment options
Option 1: Docker (simulated, no hardware) Option 2: ESP32 live sensing ($9) Option 3: Full system with Cognitum Seed ($140) Also shows RF scan, SNN, and MinCut commands for v0.5.5 capabilities. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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f4e636aaa2 |
docs: refocus README introduction on WiFi sensing
WiFi sensing (presence, vitals, activity, sleep, environment) is now the primary narrative. Pose estimation repositioned as an advanced capability. Highlights: multi-frequency mesh, SNN adaptation, witness chain, Cognitum Seed integration. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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582d51aed6 |
docs: fix Cognitum Seed pricing — $131 (not $15)
Updated all BOM references: ESP32 $9 + Cognitum Seed $131 = $140 total Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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b31efe5e92 |
docs: improve README benchmarks — results-focused with context
Replace dry metric table with human-readable results that explain why each number matters. 14 benchmarks with real-world significance. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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f03b484dd1 |
docs: update README limitations — remove 2 resolved items
Removed: - "No pre-trained model weights" — weights now published (v0.5.4+) - "Multi-person counting overcounts #348" — fixed by MinCut (ADR-075) Added: - Camera-free pose accuracy limitation (2.5% PCK@20, honest about it) Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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73ce72d39c |
docs: update README with v0.5.5 capabilities and benchmarks
- New "What's New in v0.5.5" section: SNN, MinCut (#348 fix), CNN spectrogram, WiFlow, multi-frequency mesh, graph transformer - Before/after comparison table (person counting, channels, model) - 15 new script commands with usage examples - Release table updated with v0.5.5 as Latest - v0.5.4 section collapsed (not open by default) Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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b6c032d665 |
docs: add multi-frequency mesh + RF scanner to README
New capabilities: 6-channel hopping, neighbor APs as passive radar, real-time RF spectrum visualization with null/reflector/movement detection Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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73d4cb9fc2 |
docs: update README + user guide with v0.5.4 capabilities
README: - Test badge 1300+ → 1463 - Updated capability table (171K emb/s, 100% presence, 0.012ms) - Added "What's New in v0.5.4" section with full benchmark table - Training pipeline quick start commands User guide: - Camera-Free Pose Training section (10 sensor signals, 5-phase pipeline) - ruvllm Training Pipeline section (5 phases, quantization options) - Publishing to HuggingFace section - Updated table of contents Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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6464023780 |
docs: update README banner — Alpha → Beta, remove fixed issues
- #249 (multi-node person counting) fixed by ADR-068 in v0.5.3 - #318 (training plateau) resolved - Add #348 (n_persons overcount) as current known issue - Add Cognitum Seed link for spatial resolution improvement Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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27d17431c5 |
docs: update README and user guide with Cognitum Seed integration
- Add ESP32 + Cognitum Seed as recommended hardware option ($27 BOM) - Add v0.5.4-esp32 to firmware release table - Add Cognitum Seed setup section to user guide with bridge usage, feature vector dimensions, and architecture diagram - Update table of contents Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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23dedecf0c |
docs(adr): ADR-068 per-node state pipeline for multi-node sensing (#249)
Documents the architectural change from single shared state to per-node HashMap<u8, NodeState> in the sensing server. Includes scaling analysis (256 nodes < 13 MB), QEMU validation plan, and aggregation strategy. Also links README hero image to the explainer video. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c2e564a9f4 |
docs(readme): expand alpha notice with known limitations
List specific known issues (multi-node detection, training plateau, no pre-trained weights, hardware compatibility) to set expectations for new users. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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e6068c5efe |
Enhance README with Cognitum.One reference
Updated project description to include Cognitum.One. |