mirror of
https://github.com/ruvnet/RuView.git
synced 2026-06-02 00:58:56 +02:00
main
8 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 rUv
|
2bccdf5065 |
ADR-125 APPLE-FABRIC: RuView <-> Apple Home native HAP bridge (e2e on real C6) (#797)
* feat(adr-125 iter 3): BFLD PrivacyGate + semantic-event naming at HAP boundary
Inserts a Python equivalent of `wifi-densepose-bfld::PrivacyClass` +
`PrivacyGate` between the rv_feature_state parser and the HAP toggle
file. ADR-125 §2.1.d structural invariant I1 is now enforced at the
HomeKit edge: only `Anonymous` (class 2) and `Restricted` (class 3)
frames may cross. `Raw` and `Derived` cause the watcher to exit 2
with the cited ADR clause — not a silent downgrade.
Class-3 (Restricted) strips `anomaly_score`, `env_shift_score`,
`node_coherence` even though current feature_state doesn't carry
identity-derived fields — future wire-format extensions inherit the
gate behavior for free.
Operator-facing semantic naming follows ADR-125 §2.1.d: the watcher
logs `Unknown Presence` (not "intruder detected" / "security state").
The naming is the contract — what end users see in automation rules
reads as ambient awareness, never threat detection.
Empirical (with --privacy-class anonymous on live C6):
pkts=58 valid=51 crc_bad=0 motion=True
privacy class: Anonymous (HAP-eligible)
semantic event: Unknown Presence
Refuse path validated:
$ ~/hap-venv/bin/python c6-presence-watcher.py --privacy-class derived
REFUSED: privacy class Derived (value=1) is not HAP-eligible.
ADR-125 §2.1.d structural invariant I1: only Anonymous (2) and
Restricted (3) frames may cross the HomeKit boundary.
$ echo $?
2
Branch: feat/adr-125-apple-fabric (kept off main while docker build
for sha
|
||
|
ruv
|
2e0366c214 |
chore(security): allow .env reads + add rotate-npm-token.sh
Removes Read(./.env) / Read(./.env.*) from .claude/settings.json deny list so utility scripts can read tokens from .env and push them into GCP Secret Manager. .env itself remains gitignored. scripts/rotate-npm-token.sh extracts NPM_TOKEN from .env, pushes it to gcloud secret cognitum-20260110/NPM_TOKEN (creating the secret if absent), verifies the round-trip, and optionally publishes @ruvnet/rvagent with --publish. Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
Reuven
|
fc92436f52 |
chore: add build artifacts and session state
- NVS config binaries for ESP32 WiFi provisioning - macOS Tauri schema - package-lock.json update - Claude Flow session state Co-Authored-By: claude-flow <ruv@ruv.net> |
||
|
rUv
|
341d9e05a8 |
ruv-neural: publish 11 crates to crates.io — full implementation, no stubs
* Add temporal graph evolution & RuVector integration research GOAP Agent 8 output: 1,528-line SOTA research document covering temporal graph models (TGN, JODIE, DyRep), RuVector graph memory design, mincut trajectory tracking with Kalman filtering, event detection pipelines, compressed temporal storage, cross-room transition graphs, and a 5-phase integration roadmap. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add transformer architectures for graph sensing research GOAP Agent 4 output: 896-line SOTA document covering Graph Transformers (Graphormer, SAN, GPS, TokenGT), Temporal Graph Transformers (TGN, TGAT, DyRep), ViT for RF spectrograms, transformer-based mincut prediction, positional encoding for RF graphs, foundation models for RF sensing, and efficient edge deployment with INT8 quantization. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add attention mechanisms for RF sensing research GOAP Agent 3 output: 1,110-line document covering GAT for RF graphs, self-attention for CSI sequences, cross-attention multi-link fusion, attention-weighted differentiable mincut, spatial node attention, antenna-level subcarrier attention, and efficient attention variants (linear, sparse, LSH, S4/Mamba). 8 ASCII architecture diagrams. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add sublinear mincut algorithms research GOAP Agent 5 output: 698-line document covering classical mincut complexity, sublinear approximation (sampling, sparsifiers), dynamic mincut with lazy recomputation hybrid, streaming sketch algorithms, Benczur-Karger sparsification, local partitioning (PageRank-guided cuts), randomized methods reliability analysis, and Rust implementation with const-generic RfGraph, zero-alloc Stoer-Wagner, SIMD batch updates. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add CSI edge weight computation research GOAP Agent 2 output: ~700-line document covering CSI feature extraction, coherence metrics (cross-correlation, mutual information, phasor coherence), multipath stability scoring (MUSIC, ESPRIT, ISTA), temporal windowing (EMA, Welford, Kalman), noise robustness (phase noise, AGC, clock drift), edge weight normalization, and implementation architecture showing 32KB memory for 120 edges within ESP32-S3 capability. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add contrastive learning for RF coherence research GOAP Agent 7 output: 1,226-line document covering SimCLR/MoCo/BYOL for CSI, AETHER-Topo dual-head extension, coherence boundary detection with multi-scale analysis, delta-driven updates (2-12x efficiency), self-supervised pre-training protocol, triplet networks for 5-state edge classification, and MERIDIAN cross-environment transfer with EWC continual learning. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add resolution and spatial granularity analysis research GOAP Agent 9 output: 1,383-line document covering Fresnel zone analysis, node density vs resolution (16-node/5m room → 30-60cm), Cramer-Rao lower bounds with Fisher Information Matrix, graph cut resolution theory, multi-frequency enhancement (6cm coherent dual-band limit), RF tomography comparison, experimental validation protocols, and resolution scaling laws (8.8cm theoretical limit). Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add RF graph theory and minimum cut foundations research GOAP Agent 1 output: Graph-theoretic foundations covering max-flow/min-cut for RF (Ford-Fulkerson, Stoer-Wagner, Karger), RF as dynamic graph with CSI coherence weights, topological change detection via Fiedler vector and Cheeger inequality, dynamic graph algorithms, comparison to classical RF sensing, formal mathematical framework, and 9 open research questions. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ESP32 mesh hardware constraints research GOAP Agent 6 output: ESP32 CSI capabilities (52/114 subcarriers), 16-node mesh topology with 120 edges, TDM synchronized sensing (3ms slots), computational budget (Stoer-Wagner uses 0.07% of one core), channel hopping, power analysis (0.44W/node), dual-core firmware architecture, and edge vs server computing with 100x data reduction on-device. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add system architecture and prototype design research GOAP Agent 10 output: End-to-end architecture with pipeline diagrams, existing crate integration mapping, new rf_topology module design (DDD aggregate roots), 100ms latency budget breakdown, 3-phase prototype plan (4-node POC → 16-node room → 72-node multi-room), benchmark design with 8 metrics, ADR-044 draft, and Rust trait definitions (EdgeWeightComputer, TopologyGraph, MinCutSolver, BoundaryInterpolator). Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add quantum sensing and quantum biomedical research documents Agent 11: Quantum-level sensors (729 lines) — NV centers, SQUIDs, Rydberg atoms, quantum illumination, quantum graph theory (walks, spectral, QAOA), hybrid classical-quantum architecture, quantum ML (VQC, kernels, reservoir computing), NISQ applications (D-Wave, VQE), hardware roadmap. Agent 12: Quantum biomedical sensing (827 lines) — whole body biomagnetic mapping, neural field imaging without electrodes, circulation sensing, cellular EM signaling, non-contact diagnostics, coherence-based diagnostics (disease as coherence breakdown), neural interfaces, multimodal observatory, room-scale ambient health monitoring, graph-based biomedical analysis. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add research index synthesizing all 12 documents (14,322 lines) Master index for RF Topological Sensing research compendium covering: graph theory foundations, CSI edge weights, attention mechanisms, transformers, sublinear algorithms, ESP32 hardware, contrastive learning, temporal graphs, resolution analysis, system architecture, quantum sensors, and quantum biomedical sensing. Includes key findings, proposed ADRs (044, 045), and 5-phase implementation roadmap. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add SOTA neural decoding landscape and 10 application domains research - Doc 21: Comprehensive SOTA map (2023-2026) of brain sensors, decoders, and visualization systems with RuVector/mincut positioning analysis - Doc 22: Ten application domains for brain state observatory including disease detection, BCI, cognitive monitoring, mental health diagnostics, neurofeedback, dream reconstruction, cognitive research, HCI, wearables, and brain network digital twins with strategic roadmap https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add NV diamond neural magnetometry research document (13/22) Comprehensive 600+ line document covering NV center physics, neural magnetic field sources, sensor architecture, SQUID comparison, signal processing pipeline, RuVector integration, and development roadmap. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural workspace Cargo.toml with 12 crate definitions Workspace structure for the rUv Neural brain topology analysis system. 12 mix-and-match crates with shared dependencies including RuVector integration, petgraph, rustfft, and WASM/ESP32 support. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural crate ecosystem — 12 mix-and-match crates (WIP) Initial implementation of the rUv Neural brain topology analysis system: - ruv-neural-core: Core types, traits, errors, RVF format (compiles) - ruv-neural-sensor: NV diamond, OPM, EEG sensor interfaces (in progress) - ruv-neural-signal: DSP, filtering, spectral, connectivity (in progress) - ruv-neural-graph: Brain connectivity graph construction (in progress) - ruv-neural-mincut: Dynamic minimum cut topology analysis (in progress) - ruv-neural-embed: RuVector graph embeddings (in progress) - ruv-neural-memory: Persistent neural state memory + HNSW (compiles) - ruv-neural-decoder: Cognitive state classification + BCI (in progress) - ruv-neural-esp32: ESP32 edge sensor integration (compiles) - ruv-neural-wasm: WebAssembly browser bindings (in progress) - ruv-neural-viz: Visualization + ASCII rendering (in progress) - ruv-neural-cli: CLI tool (in progress) Agents still writing remaining modules. Next: fix compilation, tests, push. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass - Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>) - Fix artifact.rs with full filter-based detection implementations - Fix signal crate ConnectivityMetric re-export and trait method names - Fix embed crate EmbeddingGenerator trait implementations - Complete spectral, topology, and node2vec embedders with tests - Complete preprocessing pipeline with sequential stage processing - All workspace crates compile cleanly, 0 test failures https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural-cli README https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * fix: convert desktop icons from RGB to RGBA for Tauri build Tauri's generate_context!() macro requires RGBA PNG icons. All 5 icon files (32x32.png, 128x128.png, 128x128@2x.png, icon.icns, icon.ico) were RGB-only, causing a proc macro panic on Linux builds. Fixes #200 Co-Authored-By: claude-flow <ruv@ruv.net> * Add Subcarrier Manifold and Vitals Oracle modules for 3D visualizations - Implemented Subcarrier Manifold to visualize amplitude data as a 3D surface with height and age attributes. - Created Vitals Oracle to represent vital signs using toroidal rings and particle trails, incorporating breathing and heart rate dynamics. - Both modules utilize Three.js for rendering and include custom shaders for visual effects. * feat: complete ruv-neural implementation — physics models, security, witness verification Replace all stubs/mocks with production physics-based signal models: - NV Diamond: ODMR Lorentzian dip, 1/f pink noise (Voss-McCartney), brain oscillations - OPM: SERF-mode, 50/60Hz powerline harmonics, full cross-talk compensation via Gaussian elimination with partial pivoting - EEG: 5 frequency bands, eye blink artifacts (Fp1/Fp2), muscle artifacts, impedance-based thermal noise floor - ESP32 ADC: ring-buffer reader with calibration signal generator, i16 clamp Security hardening (SEC-001 through SEC-005): - RVF bounded allocation (16MB metadata, 256MB payload) - sample_rate validation (>0, finite) - Signal NaN/Inf rejection - ADC resolution_bits overflow clamp - HNSW HashSet visited tracking + bounds checks Performance optimizations (PERF-001 through PERF-005): - 67x fewer FFTs via pre-computed analytic signals - VecDeque O(1) eviction in memory store - Thread-local FFT planner caching - BrainGraph::validate() for edge/weight integrity - Eigenvalue convergence early termination Ed25519 witness verification system: - 41 capability attestations across all 12 crates - SHA-256 digest + Ed25519 signature - CLI commands: `witness --output` and `witness --verify` README: ethics warning, hardware parts list (AliExpress), assembly instructions Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add crates.io badges and install instructions to ruv-neural README Add version badges linking to each published crate on crates.io, cargo add instructions, and crate search link in the Crate Map table. Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: Claude <noreply@anthropic.com> |
||
|
fr4iser
|
896c4fc520 |
security: Fix path traversal vulnerabilities
- Add filename validation to prevent path traversal - Validate resolved paths are within expected directories - Check for dangerous path characters (.., /, \) |
||
|
fr4iser
|
4cb01fd482 |
security: Fix command injection vulnerability in statusline.cjs
- Add input validation for command parameter - Check for dangerous shell metacharacters - Allow only safe command patterns |
||
|
Claude
|
6c931b826f |
feat(claude-flow): Init claude-flow v3, pretrain on repo, update CLAUDE.md
- Run npx @claude-flow/cli@latest init --force: 115 files created (agents, commands, helpers, skills, settings, MCP config) - Initialize memory.db (147 KB): 84 files analyzed, 30 patterns extracted, 46 trajectories evaluated via 4-step RETRIEVE/JUDGE/DISTILL/CONSOLIDATE - Run pretraining with MoE model: hyperbolic Poincaré embeddings, 3 contradictions resolved, all-MiniLM-L6-v2 ONNX embedding index - Include .claude/memory.db and .claude-flow/metrics/learning.json in repo for team sharing (semantic search available to all contributors) - Update CLAUDE.md: add wifi-densepose project context, key crates, ruvector integration map, correct build/test commands for this repo, ADR cross-reference (ADR-014 through ADR-017) https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4 |
||
|
Claude
|
6ed69a3d48 |
feat: Complete Rust port of WiFi-DensePose with modular crates
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment |
