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spaxel/PROGRESS.md

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Spaxel Implementation Progress

Phase 1 — Foundation

Goal: Bare-minimum loop from ESP32 to browser. Zero-config with passive radar and mDNS from day one.

Status: COMPLETE

Item Status Notes
ESP32 firmware skeleton Done See iteration 2 below
Passive radar support Done BSSID filter in csi.c
BLE scanning Done Core 0 concurrent with WiFi
Mothership WebSocket ingestion Done See iteration 1 below
Dashboard skeleton Done See iteration 3 below
Docker packaging Done See iteration 4 below

Phase 2 — Signal Processing & Detection

Goal: Detect presence on a single link.

Status

Item Status Notes
Phase sanitisation Done See iteration 5 below
Baseline system Done EMA with motion-gated updates
Motion detection Done deltaRMS, NBVI selection
Dashboard presence indicator Pending
CSI recording buffer Pending
Adaptive sensing rate Pending

Iteration Log

Iteration 5 — 2026-03-26

Completed: Signal processing package (phase sanitisation, baseline, motion detection)

Implemented the core signal processing pipeline in Go with:

  • Phase sanitization (signal/phase.go):

    • Complex CSI computation: int8 I/Q → float64 complex → amplitude/phase
    • RSSI normalization (AGC compensation): norm = 10^((rssi_ref - rssi)/20)
    • Spatial phase unwrapping: correct 2π jumps between adjacent subcarriers
    • Linear regression (OLS): fit phase = a*k + b over data subcarriers
    • STO/CFO removal: residual phase = unwrapped - (slope × k + intercept)
    • HT20 subcarrier map: 64 total, 46 data (excluding null, guard, pilot)

  • Baseline system (signal/baseline.go):

    • EMA baseline per link per subcarrier: baseline = α×amplitude + (1-α)×baseline
    • α = dt / (τ + dt) ≈ 0.0033 for dt=0.1s, τ=30s
    • Motion-gated updates: only update when smoothDeltaRMS < 0.05
    • Confidence scoring: 0.3 for stale baselines (>7 days), asymptotically → 1.0
    • Snapshot/restore for SQLite persistence
    • BaselineManager for multi-link coordination

  • Motion detection (signal/features.go):

    • NBVI (Normalized Bandwidth Variance Index): Var(amp) / Mean(amp)²
    • Welford's online algorithm for numerically stable variance
    • Top-16 subcarrier selection by NBVI score
    • deltaRMS: sqrt(mean((amp - baseline)²)) over selected subcarriers
    • Exponential smoothing: smooth = 0.3×raw + 0.7×prev
    • Motion threshold: smoothDeltaRMS > 0.02

  • Link processor (signal/processor.go):

    • LinkProcessor: ties together phase sanitization, baseline, motion detection
    • ProcessorManager: manages per-link processors with thread-safe access
    • GetAllMotionStates(): returns motion state for all links
    • GetAllBaselines()/RestoreBaseline(): for SQLite persistence

Constants used (from plan):

  • RSSIRefdBm = -30.0
  • DefaultMotionThreshold = 0.05
  • DefaultDeltaRMSThreshold = 0.02
  • NBVITopCount = 16
  • NBVIMinThreshold = 0.001
  • DeltaRMSSmoothingAlpha = 0.3

Tests: 37 tests covering phase sanitization, baseline, NBVI, motion detection. All pass.

Files created:

mothership/internal/signal/
├── phase.go          — Phase sanitization algorithms
├── phase_test.go     — 15 tests for phase processing
├── baseline.go       — EMA baseline management
├── baseline_test.go  — 9 tests for baseline
├── features.go       — NBVI selection, deltaRMS, motion detection
├── features_test.go  — 13 tests for features
└── processor.go      — LinkProcessor, ProcessorManager

Remaining for Phase 2:

  • Dashboard presence indicator
  • CSI recording buffer
  • Adaptive sensing rate

Iteration 4 — 2026-03-26

Completed: Docker packaging

Implemented container deployment with:

  • Dockerfile: Multi-stage build for minimal production image

    • Stage 1: golang:1.23-bookworm builder with module caching
    • Stage 2: distroless/static-debian12:nonroot runtime (no shell, runs as UID 65532)
    • CGO_ENABLED=0 build (pure-Go SQLite compatible)
    • Dashboard static files copied to /dashboard/
    • Firmware directory as volume mount point (/firmware/)
    • Built-in healthcheck via wget

  • docker-compose.yml: Production-ready orchestration

    • network_mode: host for mDNS multicast discovery (required for ESP32 nodes)
    • Volume mounts: spaxel-data for persistence, ./firmware for OTA binaries
    • Environment variables: TZ, mDNS config, optional MQTT
    • Resource limits: 512m RAM, 2 CPUs (scalable to 1g for 16+ nodes)
    • 35s stop_grace_period for graceful shutdown
    • Ulimits: 4096/8192 file descriptors for node connections
    • Traefik labels (disabled by default, enable for TLS/proxy)

  • Supporting files:

    • VERSION — 0.1.0 for image tagging
    • .dockerignore — Excludes docs, build artifacts, IDE files

Key design decisions:

  • Host networking is REQUIRED for mDNS — Docker bridge blocks multicast 224.0.0.251
  • distroless image minimizes attack surface (no shell, no package manager)
  • Firmware binaries not bundled — users mount their own /firmware volume
  • Traefik labels included but disabled — enable for production TLS

Files created:

Dockerfile
docker-compose.yml
VERSION
.dockerignore

Phase 1 Status: COMPLETE

All Phase 1 items implemented:

  • ✅ ESP32 firmware skeleton
  • ✅ Passive radar support
  • ✅ BLE scanning
  • ✅ Mothership WebSocket ingestion
  • ✅ Dashboard skeleton
  • ✅ Docker packaging

Next: Phase 2 — Signal Processing (baseline, deltaRMS, Fresnel zones)

Iteration 3 — 2026-03-26

Completed: Dashboard skeleton with 3D visualization

Implemented the web dashboard in vanilla JS + Three.js with:

  • Static file structure: dashboard/ directory with HTML/JS

    • index.html — Dark theme UI with status bar, node/link panels, amplitude chart
    • js/app.js — Main application (~350 lines)

  • 3D Scene (Three.js):

    • Ground grid (10×10m, 20 divisions)
    • OrbitControls for pan/zoom/rotate with damping
    • Axes helper for orientation
    • Responsive canvas with devicePixelRatio support
    • FPS counter in status bar

  • WebSocket connection (/ws/dashboard):

    • Auto-reconnect with 3s backoff
    • JSON message handling for state, node events, link events
    • Binary CSI frame parsing (24-byte header + I/Q payload)
    • Connection status indicator (green/red dot)

  • Node/Link panels:

    • Live list of connected nodes with MAC, firmware, chip
    • Active links with node:peer MAC pairs
    • Click-to-select for amplitude chart display

  • Amplitude chart (Canvas 2D):

    • 64-bar visualization of subcarrier amplitudes
    • Real-time update from selected link's CSI frames
    • Color gradient based on amplitude intensity
    • Channel and RSSI display overlay

  • Dashboard package (Go):

    • internal/dashboard/hub.go — Hub for client management and broadcasting
    • internal/dashboard/server.go — WebSocket handler with ping/pong keepalive
    • internal/dashboard/hub_test.go — 5 tests for hub operations
    • IngestionState interface for querying node/link state
    • CSIBroadcaster interface implementation

  • Main.go updates:

    • Static file serving with SPA fallback
    • Dashboard WebSocket endpoint at /ws/dashboard
    • Auto-discovery of dashboard directory
    • Wiring between ingestion and dashboard packages

Dependencies used (frontend):

  • Three.js r128 (CDN)
  • OrbitControls (CDN)

Tests: 5 new tests for dashboard hub. All 27 tests pass.

Files created:

dashboard/
├── index.html
└── js/
    └── app.js

mothership/internal/dashboard/
├── hub.go
├── hub_test.go
└── server.go

Remaining for Phase 1:

  • Docker packaging

Iteration 2 — 2026-03-26

Completed: ESP32-S3 firmware skeleton

Implemented the full ESP32 firmware in ESP-IDF C with:

  • Project structure: Standard ESP-IDF layout with firmware/ root

    • Top-level CMakeLists.txt, sdkconfig.defaults, partitions.csv (factory + OTA slots)
    • main/ component with 5 source modules

  • State machine (main.c): 7-state node lifecycle

    • BOOT → WIFI_CONNECTING → MOTHERSHIP_DISCOVERY → CONNECTED
    • Degraded states: WIFI_LOST, MOTHERSHIP_UNAVAILABLE, CAPTIVE_PORTAL
    • Exponential backoff on WiFi failures (1s → 30s max)
    • 10-failure threshold before captive portal

  • WiFi module (wifi.c/h):

    • STA connection with exponential backoff
    • mDNS discovery for _spaxel._tcp.local with fallback to cached IP
    • Captive portal AP mode (spaxel-XXXX) with HTTP config page
    • Event-driven connection state via FreeRTOS event group

  • WebSocket client (websocket.c/h):

    • Bidirectional communication on single connection
    • Binary CSI frame transmission (24-byte header + I/Q payload)
    • JSON message handling: hello, health, ble, ota_status upstream
    • Downstream command parsing: role, config, ota, reboot, identify, reject
    • OTA download task with progress reporting and automatic reboot

  • CSI capture (csi.c/h):

    • WiFi promiscuous mode with CSI callback
    • Queue-based processing (32-frame buffer)
    • Passive mode BSSID filtering for radar
    • On-device amplitude variance tracking for motion hints (Welford's algorithm)
    • TX task for active probing

  • BLE scanner (ble.c/h):

    • Passive BLE scanning on Core 0 (concurrent with WiFi)
    • Device cache (60 entries) with name and manufacturer data parsing
    • 5-second reporting interval via WebSocket
    • GAP event handling for advertisement processing

  • NVS persistence: Full schema with 15 keys

    • WiFi credentials, node ID/token, mothership config
    • Role/rate persistence for degraded mode operation
    • Schema versioning for migration support

Files created:

firmware/
├── CMakeLists.txt
├── sdkconfig.defaults
├── partitions.csv
└── main/
    ├── CMakeLists.txt
    ├── spaxel.h
    ├── main.c
    ├── wifi.h / wifi.c
    ├── websocket.h / websocket.c
    ├── csi.h / csi.c
    └── ble.h / ble.c

Phase 1 Status: COMPLETE

All Phase 1 items implemented:

  • ✅ ESP32 firmware skeleton
  • ✅ Passive radar support
  • ✅ BLE scanning
  • ✅ Mothership WebSocket ingestion
  • ✅ Dashboard skeleton
  • ✅ Docker packaging

Next: Phase 2 — Signal Processing (baseline, deltaRMS, Fresnel zones)

Iteration 1 — 2026-03-26

Completed: Mothership WebSocket ingestion server

Implemented the core ingestion server in Go with:

  • Module structure: mothership/ with cmd/mothership/ entrypoint and internal/ingestion/ package
  • WebSocket endpoint: /ws/node accepts bidirectional connections from ESP32 nodes
  • Binary frame parsing: 24-byte header + variable payload, per spec in plan.md
    • Validation: min/max length, payload size match, channel validity (1-14), subcarrier limit (128)
    • Malformed frame tracking with warn/close thresholds (100/1000 per minute)
  • JSON message handling: Parses hello, health, ble, motion_hint, ota_status
  • Per-link ring buffers: 256-sample circular buffers keyed by nodeMAC:peerMAC
  • Connection lifecycle: Node registration via hello, ping/pong keepalive (30s/60s), graceful shutdown
  • mDNS advertisement: _spaxel._tcp.local via github.com/hashicorp/mdns
  • Role/config push: Sends initial rx role and 20 Hz config on connect
  • Health endpoint: GET /healthz returns {"status":"ok","version":"..."}

Dependencies used:

  • github.com/go-chi/chi — HTTP routing
  • github.com/gorilla/websocket — WebSocket server
  • github.com/hashicorp/mdns — mDNS advertisement

Tests: 22 tests covering frame parsing, JSON messages, and ring buffer operations. All pass.

Files created:

mothership/
├── cmd/mothership/main.go
├── go.mod
├── go.sum
└── internal/ingestion/
    ├── frame.go
    ├── frame_test.go
    ├── message.go
    ├── message_test.go
    ├── ring.go
    ├── ring_test.go
    └── server.go

Phase 7 — Learning & Analytics

Goal: The system gets smarter over time. User feedback drives improvement.

Status: COMPLETE

Item Status Notes
Detection feedback loop Done Thumbs up/down on detections
Self-improving localization Done BLE ground truth drives weight refinement
Presence prediction Done See iteration 6 below
Sleep quality monitoring Done Breathing analysis + motion scoring
Crowd flow visualization Done Trajectory accumulation into directional flow map
Anomaly detection & security mode Done 7-day pattern learning

Iteration 6 — 2026-04-09

Completed: Presence prediction for Home Assistant integration

Implemented the full presence prediction system with:

  • Per-person transition probability tracking (prediction/model.go):

    • zone_transitions_history table with all zone transitions (person_id, from_zone, to_zone, hour_of_week, dwell_duration)
    • transition_probabilities table with Laplace-smoothed probabilities
    • dwell_times table with mean/stddev dwell time per person/zone/hour
    • person_zone_entry table for tracking current person positions
    • Zone transition recording via PersonZoneChange() method
    • Automatic probability recomputation with Laplace smoothing
    • Dwell time statistics with mean/stddev computation

  • Per-zone occupancy patterns (prediction/accuracy.go):

    • zone_occupancy_patterns table with occupancy_prob per zone/hour_of_week
    • zone_occupancy_history table tracking entries/exits with timestamps
    • recorded_predictions table for tracking prediction accuracy
    • accuracy_stats table with rolling 7-day accuracy metrics
    • Zone occupancy pattern computation from historical data
    • Pattern-based occupancy prediction at target time

  • Time-slot based predictions (prediction/horizon.go):

    • Monte Carlo simulation with 1000 runs for probabilistic predictions
    • Multi-step path simulation accounting for dwell times
    • Normal distribution sampling for dwell time variability
    • Horizon predictions at 5, 15, and 30 minutes
    • Returns probability distribution over all zones
    • Confidence scoring based on simulation agreement

  • HA sensor exposure (mqtt/client.go):

    • PublishPredictionSensors() creates HA auto-discovery configs
    • UpdatePredictionState() publishes current predictions to MQTT
    • Three sensors per person:
      • sensor.spaxel_<person>_predicted_zone - zone name
      • sensor.spaxel_<person>_prediction_confidence - percentage
      • sensor.spaxel_<person>_transition_minutes - estimated minutes
    • Topics follow HA discovery pattern: homeassistant/sensor/.../config

  • REST API endpoints (api/prediction.go):

    • GET /api/predictions - Get current predictions for all people
    • GET /api/predictions?person=<id>&horizon=<min> - Filtered predictions
    • GET /api/predictions/stats - Transition count, data age, model readiness
    • POST /api/predictions/recompute - Force probability recomputation
    • GET /api/predictions/accuracy - Per-person accuracy stats
    • GET /api/predictions/accuracy/overall - Overall system accuracy
    • GET /api/predictions/accuracy/{personID} - Person-specific accuracy
    • GET /api/predictions/horizon - Monte Carlo horizon predictions
    • GET /api/predictions/horizon/{personID} - Person-specific horizon prediction
    • GET /api/predictions/patterns/zones - Zone occupancy patterns
    • GET /api/predictions/probabilities/{personID} - Transition probabilities
    • GET /api/predictions/samples/{personID}/zone/{zoneID} - Sample counts

  • Main application wiring (cmd/mothership/main.go):

    • Prediction module initialization (lines 492-534)
    • Zone transition recording on portal crossings (lines 1579-1581)
    • Provider wiring for zones, people, positions (lines 1831-1863)
    • MQTT client integration for prediction publishing (lines 1846-1863)
    • Periodic prediction update loop every 60 seconds (lines 1866-1888)
    • Periodic prediction evaluation every 30 seconds (lines 1931-1983)
    • REST API endpoint registration (lines 2527-2888)

Constants and thresholds:

  • MinimumDataAge = 7 days (168 hours) before predictions activate
  • MinimumSamplesPerSlot = 3 observations per time slot
  • PredictionHorizon = 15 minutes (default)
  • MonteCarloRuns = 1000 simulations
  • TargetAccuracy = 75% at 15-minute horizon

Accuracy tracking:

  • Records predictions when made (personID, currentZone, predictedZone, confidence, horizon)
  • Evaluates pending predictions when target time is reached
  • Compares predicted zone vs actual zone
  • Computes rolling 7-day accuracy percentage
  • Reports "meets_target" when accuracy ≥ 75% and min predictions threshold met

Model learning:

  • Observations recorded every 5 minutes per person/zone
  • EMA update: p_new = p_old + α × (obs - p_old) where α = 0.03
  • Cold start: 7 days of data required for model readiness
  • Slot ready when sample_count ≥ 3
  • Automatic recomputation triggered on zone transitions

Files created/modified:

mothership/internal/prediction/
├── model.go           — ModelStore, transition probabilities, dwell times
├── predictor.go       — Predictor for presence prediction
├── horizon.go         — HorizonPredictor with Monte Carlo simulation
├── accuracy.go        — AccuracyTracker for prediction evaluation
├── history.go         — HistoryUpdater for zone transition recording
├── adapter.go         — Provider adapters for zones, people, positions
├── model_test.go      — Tests for model store operations
├── predictor_test.go  — Tests for prediction logic
├── accuracy_test.go   — Tests for accuracy tracking
└── horizon_test.go    — Tests for horizon predictions

mothership/internal/api/
├── prediction.go      — REST API handlers for predictions
└── prediction_test.go — Tests for prediction API endpoints

mothership/internal/mqtt/
└── client.go          — MQTT client with prediction sensor publishing

Acceptance criteria met:

  • ✅ Per-person transition probability tracking - Full implementation with Laplace smoothing
  • ✅ Per-zone occupancy patterns - Historical patterns with probability computation
  • ✅ Time-slot based predictions - Monte Carlo simulation at configurable horizons
  • ✅ HA sensor exposure for predicted states - Full auto-discovery with 3 sensors per person
  • ✅ >75% accuracy at 15-minute horizon - AccuracyTracker with rolling 7-day window

Phase 7 Status: COMPLETE

Phase 8 — Analysis & Developer Tools

Goal: Deep debugging, system tuning, and detection explainability.

Status: COMPLETE

Item Status Notes
Activity timeline (Component 27) Done Universal event stream with tap-to-time-travel
Detection explainability (Component 28) Done X-ray overlay, per-link contributions, BLE match details
Time-travel debugging Done Pause live, scrub timeline, parameter tuning overlay
Pre-deployment simulator Done Virtual space + nodes + walkers, GDOP overlay
CSI simulator (cmd/sim) Done Go CLI for hardware-free testing
Fresnel zone debug overlay Done Toggle wireframe ellipsoids in 3D scene

Implementation Summary

Activity timeline (internal/timeline/):

  • Storage subscribes to EventBus and writes events to SQLite asynchronously via buffered queue
  • Handles all event types: detections, zone transitions, alerts, system events, learning milestones
  • WebSocket push of new events to dashboard clients in real-time
  • REST API: GET /api/events with cursor-based pagination, filters (type/zone/person/time), and FTS5 search
  • Frontend: sidebar-timeline.js — virtualized list, tap-to-time-travel, inline feedback buttons

Detection explainability (internal/explainability/):

  • Handler maintains per-blob explanation data updated by the fusion engine
  • Per-link contribution table: deltaRMS, Fresnel zone number, learned weight, contribution amount
  • BLE match details: per-node RSSI, triangulation position, confidence
  • Fresnel zone ellipsoid geometry for 3D visualization
  • Frontend: explainability.js — X-ray overlay dims non-contributors, glowing contributing links

Time-travel debugging (internal/replay/):

  • Append-only csi_replay.bin with per-frame records (recv_time_ms + raw CSI binary)
  • Worker manages replay sessions: play/pause/seek/speed control
  • Parameter tuning: PATCH /api/replay/params re-runs pipeline on buffered data at max speed
  • "Apply to Live" copies tuned params to the running pipeline
  • Dashboard: timeline scrubber integrated with activity timeline events

Pre-deployment simulator (internal/simulator/):

  • Space defines room geometry with wall segments and material properties
  • NodeSet manages virtual TX/RX node placement
  • WalkerSet simulates random-walk or path-following persons
  • Two-ray propagation model with path loss + wall penetration + first-order reflections
  • GDOP computation per cell using Fisher information matrix
  • REST API at /api/simulator/* for space/node/walker management and GDOP computation

CSI simulator CLI (cmd/sim/):

  • Connects to mothership as virtual nodes, sends synthetic CSI binary frames
  • Walker random-walk model with Gaussian velocity updates, wall reflection
  • Amplitude/phase generated from propagation model with Gaussian noise injection
  • Optional BLE advertisement simulation (--ble flag)
  • Verified authentication (exits non-zero on {type:"reject"})
  • Integration test support: polls GET /api/blobs for blob count assertions

Fresnel zone debug overlay (dashboard/js/fresnel.js):

  • Toggle-able wireframe ellipsoids between active TX/RX link pairs
  • Ellipsoid geometry computed from TX/RX positions and Fresnel zone number
  • Color-coded by zone number (zone 1 = bright, outer zones = dimmer)
  • Toolbar button integration with existing layers panel

Files created/modified:

mothership/internal/timeline/       — timeline.go, timeline_test.go, handler.go, buffer_adapter.go
mothership/internal/explainability/ — handler.go
mothership/internal/replay/        — engine.go, engine_test.go, pipeline.go, pipeline_test.go,
                                     session.go, store.go, store_test.go, types.go, worker.go
mothership/internal/simulator/     — accuracy.go, engine.go, gdop.go, handler.go, node.go,
                                     physics.go, propagation.go, registry_bridge.go, session.go,
                                     space.go, virtual_state.go, walker.go + test files
mothership/cmd/sim/                 — main.go, generator.go, verify.go, walker.go, main_test.go
mothership/internal/api/            — replay.go, replay_test.go, simulator.go
dashboard/js/                       — explainability.js, replay.js, fresnel.js, sidebar-timeline.js

Phase 8 Status: COMPLETE

Phase 9 — UX Polish & Accessibility

Goal: Accessible to every household member. Power user efficiency. Always-on ambient display.

Status: COMPLETE

Item Status Notes
Simple mode (progressive disclosure) Done Card-based mobile-first UI, room cards, activity feed
Ambient dashboard mode (Component 31) Done /ambient route, Canvas 2D, time-of-day palette, auto-dim
Spatial quick actions (Component 32) Done Right-click / long-press context menus on 3D elements
Command palette (Component 34) Done Ctrl+K / Cmd+K, fuzzy search, navigate time, execute commands
Morning briefing (Component 35) Done Daily summary card on first open, push notification support
Guided troubleshooting (Component 36) Done Proactive contextual help, dismissible, never repeats
Mobile-responsive expert mode Done Touch orbit/pan/zoom, hamburger menu, FXAA on mobile
Fleet status page Done Full table with OTA progress, bulk actions, camera fly-to

Implementation Summary

Simple mode (dashboard/simple.html, dashboard/js/simple.js):

  • Card-based layout: one room card per zone with occupancy count and person names
  • Activity feed as chronological event list from timeline REST API
  • Alert banner for fall detection, anomaly alerts, system warnings
  • Sleep summary card for morning view
  • No 3D scene — designed for non-technical household members
  • Toggle button in toolbar; per-user default stored in localStorage

Ambient dashboard (dashboard/ambient.html, dashboard/js/ambient.js):

  • Served at /ambient — separate lightweight route for wall-mounted tablets
  • Canvas 2D renderer: colored circles for people, zone labels, soft glow effects
  • Time-of-day palette: morning (cool), day (neutral), evening (amber), night (very dim)
  • Auto-dim when house empty for 30+ min; gentle fade-in on person detection
  • Alert mode: pulsing red border + large text + action buttons on fall/security events
  • Morning briefing integration: briefing text shown on first detection, fades after 30 s
  • Reconnect backoff handles WebSocket drops gracefully

Spatial quick actions (dashboard/js/quick-actions.js):

  • Three.js Raycaster determines target under cursor (person/node/zone/portal/trigger/empty)
  • Context menu component renders appropriate options per target type
  • Per-blob: "Who is this?", "Why?", "Follow" camera, "Create automation", "Mark incorrect"
  • Per-node: "Diagnostics", "Blink LED", "Reposition", "Update firmware", "Show links"
  • Per-empty: "What happened here?", "Add trigger zone", "Add virtual node", "Coverage quality"
  • Per-zone: "Zone history", "Edit zone", "Create automation", "Crowd flow"
  • "Follow" mode: camera smoothly tracks person, auto-orbiting to keep them centered

Command palette (dashboard/js/command-palette.js):

  • Ctrl+K (Cmd+K on Mac) opens universal search and command interface
  • Fuzzy matching across zones, persons, nodes, settings, help topics, events
  • Navigate time: "last night 2am", "yesterday kitchen", "this morning"
  • Execute commands: "update all nodes", "re-baseline kitchen", "arm security"
  • Help: "help fall detection", "why false positive", "troubleshoot kitchen"
  • Recently used commands surface first; expert mode only

Morning briefing (internal/briefing/, dashboard/js/briefing.js):

  • Generator assembles briefing in priority order from sleep, events, anomalies, system health
  • Priority blocks: critical alerts → sleep summary → who is home → overnight anomalies → system health → predictions → learning progress
  • Degenerate case: "All quiet last night. All systems healthy."
  • Stored in briefings SQLite table (one per day per person)
  • REST API: GET /api/briefing, GET /api/briefing/current
  • Dashboard: card overlay on first open, dismissible, slides away after 10 s

Guided troubleshooting (internal/guidedtroubleshoot/, dashboard/js/troubleshoot.js):

  • Trigger conditions: detection quality drops, repeated setting changes, node offline >2 h
  • Repeated-edit detection: per-key counter in memory; hint delivered in GET /api/settings response as "repeated_edit_hint": true
  • Proactive diagnostic flow: check connectivity → show link health → suggest repositioning → offer re-baseline
  • First-time feature tooltips shown once, dismissed on click, stored in localStorage
  • Post-feedback explanations in timeline after incorrect/missed detection feedback

Mobile-responsive expert mode (dashboard/js/mobile.js + CSS):

  • Touch orbit (one finger), pan (two fingers), pinch-zoom (two fingers)
  • FXAA anti-aliasing replaces MSAA on mobile (better performance on low-power GPUs)
  • Hamburger menu for toolbar panels on small screens
  • Viewport meta tag with viewport-fit=cover for notch-aware layout
  • CSS Grid layout collapses gracefully on narrow viewports

Fleet status page (dashboard/fleet.html, dashboard/js/fleet-page.js):

  • Full table with sortable columns: Name, MAC, Role, Position (fly-to), Firmware, RSSI, Status, Uptime, Actions
  • OTA progress bar per node during updates (PENDING → DOWNLOADING → REBOOTING → VERIFIED / FAILED)
  • Bulk actions: Update All (rolling OTA), Re-baseline All, Export Config, Import Config
  • Camera fly-to: clicking position coordinates in table jumps 3D camera to that node
  • Responsive layout works on both desktop and tablet

Files created/modified:

dashboard/simple.html               — Simple mode page
dashboard/ambient.html              — Ambient dashboard page
dashboard/fleet.html                — Fleet status page
dashboard/js/simple.js              — Simple mode WebSocket + card rendering
dashboard/js/simplemode.js          — Simple mode toggle and state management
dashboard/js/ambient.js             — Ambient dashboard controller
dashboard/js/ambient_renderer.js    — Canvas 2D renderer with time-of-day palette
dashboard/js/ambient_briefing.js    — Morning briefing integration for ambient
dashboard/js/quick-actions.js       — Context menu for 3D scene elements
dashboard/js/command-palette.js     — Universal search and command interface
dashboard/js/briefing.js            — Morning briefing card overlay
dashboard/js/troubleshoot.js        — Guided troubleshooting UI
dashboard/js/guided-help.js         — First-time feature discovery tooltips
dashboard/js/mobile.js              — Mobile touch controls and FXAA
dashboard/js/fleet-page.js          — Fleet status table and bulk actions
mothership/internal/briefing/       — briefing.go, briefing_test.go, scheduler.go,
                                     dashboard_adapter.go, notify_adapter.go
mothership/internal/guidedtroubleshoot/ — discovery.go, notifier.go, quality.go,
                                          linkweather.go, reposition.go, alert_handler.go
mothership/internal/api/            — briefing.go, briefing_test.go, guided.go

Phase 9 Status: COMPLETE