homeai/TODO.md

# HomeAI — Master TODO

> Track progress across all sub-projects. See each sub-project `PLAN.md` for detailed implementation notes.
> Status: `[ ]` pending · `[~]` in progress · `[x]` done

---

## Phase 1 — Foundation

### P1 · homeai-infra

- [ ] Install Docker Desktop for Mac, enable launch at login
- [ ] Create shared `homeai` Docker network
- [ ] Create `~/server/docker/` directory structure
- [ ] Write compose files: Home Assistant, Portainer, Uptime Kuma, Gitea, code-server, n8n
- [ ] Write `.env.secrets.example` and `Makefile`
- [ ] `make up-all` — bring all services up
- [ ] Home Assistant onboarding — generate long-lived access token
- [ ] Write `~/server/.env.services` with all service URLs
- [ ] Install Tailscale, verify all services reachable on Tailnet
- [ ] Gitea: create admin account, initialise all 8 sub-project repos, configure SSH
- [ ] Uptime Kuma: add monitors for all services, configure mobile alerts
- [ ] Verify all containers survive a cold reboot

### P2 · homeai-llm

- [ ] Install Ollama natively via brew
- [ ] Write and load launchd plist (`com.ollama.ollama.plist`)
- [ ] Write `ollama-models.txt` with model manifest
- [ ] Run `scripts/pull-models.sh` — pull all models
- [ ] Run `scripts/benchmark.sh` — record results in `benchmark-results.md`
- [ ] Deploy Open WebUI via Docker compose (port 3030)
- [ ] Verify Open WebUI connected to Ollama, all models available
- [ ] Add Ollama + Open WebUI to Uptime Kuma monitors
- [ ] Add `OLLAMA_URL` and `OPEN_WEBUI_URL` to `.env.services`

---

## Phase 2 — Voice Pipeline

### P3 · homeai-voice

- [ ] Compile Whisper.cpp with Metal support
- [ ] Download Whisper models (`large-v3`, `medium.en`) to `~/models/whisper/`
- [ ] Install `wyoming-faster-whisper`, test STT from audio file
- [ ] Install Kokoro TTS, test output to audio file
- [ ] Install Wyoming-Kokoro adapter, verify Wyoming protocol
- [ ] Write + load launchd plists for Wyoming STT (10300) and TTS (10301)
- [ ] Connect Home Assistant Wyoming integration (STT + TTS)
- [ ] Create HA Voice Assistant pipeline
- [ ] Test HA Assist via browser: type query → hear spoken response
- [ ] Install openWakeWord, test wake detection with USB mic
- [ ] Write + load openWakeWord launchd plist
- [ ] Install Chatterbox TTS (MPS build), test with sample `.wav`
- [ ] Install Qwen3-TTS via MLX (fallback)
- [ ] Write `wyoming/test-pipeline.sh` — end-to-end smoke test
- [ ] Add Wyoming STT/TTS to Uptime Kuma monitors

---

## Phase 3 — Agent & Character

### P5 · homeai-character *(no runtime deps — can start alongside P1)*

- [ ] Define and write `schema/character.schema.json` (v1)
- [ ] Write `characters/aria.json` — default character
- [ ] Set up Vite project in `src/`, install deps
- [ ] Integrate existing `character-manager.jsx` into Vite project
- [ ] Add schema validation on export (ajv)
- [ ] Add expression mapping UI section
- [ ] Add custom rules editor
- [ ] Test full edit → export → validate → load cycle
- [ ] Record or source voice reference audio for Aria (`~/voices/aria.wav`)
- [ ] Pre-process audio with ffmpeg, test with Chatterbox
- [ ] Update `aria.json` with voice clone path if quality is good
- [ ] Write `SchemaValidator.js` as standalone utility

### P4 · homeai-agent

- [ ] Confirm OpenClaw installation method and Ollama compatibility
- [ ] Install OpenClaw, write `~/.openclaw/config.yaml`
- [ ] Verify OpenClaw responds to basic text query via `/chat`
- [ ] Write `skills/home_assistant.py` — test lights on/off via voice
- [ ] Write `skills/memory.py` — test store and recall
- [ ] Write `skills/weather.py` — verify HA weather sensor data
- [ ] Write `skills/timer.py` — test set/fire a timer
- [ ] Write skill stubs: `music.py`, `vtube_studio.py`, `comfyui.py`
- [ ] Set up mem0 with Chroma backend, test semantic recall
- [ ] Write and load memory backup launchd job
- [ ] Symlink `homeai-agent/skills/` → `~/.openclaw/skills/`
- [ ] Build morning briefing n8n workflow
- [ ] Build notification router n8n workflow
- [ ] Verify full voice → agent → HA action flow
- [ ] Add OpenClaw to Uptime Kuma monitors

---

## Phase 4 — Hardware Satellites

### P6 · homeai-esp32

- [ ] Install ESPHome: `pip install esphome`
- [ ] Write `esphome/secrets.yaml` (gitignored)
- [ ] Write `base.yaml`, `voice.yaml`, `display.yaml`, `animations.yaml`
- [ ] Write `s3-box-living-room.yaml` for first unit
- [ ] Flash first unit via USB
- [ ] Verify unit appears in HA device list
- [ ] Assign Wyoming voice pipeline to unit in HA
- [ ] Test full wake → STT → LLM → TTS → audio playback cycle
- [ ] Test LVGL face: idle → listening → thinking → speaking → error
- [ ] Verify OTA firmware update works wirelessly
- [ ] Flash remaining units (bedroom, kitchen, etc.)
- [ ] Document MAC address → room name mapping

---

## Phase 5 — Visual Layer

### P7 · homeai-visual

- [ ] Install VTube Studio (Mac App Store)
- [ ] Enable WebSocket API on port 8001
- [ ] Source/purchase a Live2D model (nizima.com or booth.pm)
- [ ] Load model in VTube Studio
- [ ] Create hotkeys for all 8 expression states
- [ ] Write `skills/vtube_studio.py` full implementation
- [ ] Run auth flow — click Allow in VTube Studio, save token
- [ ] Test all 8 expressions via test script
- [ ] Update `aria.json` with real VTube Studio hotkey IDs
- [ ] Write `lipsync.py` amplitude-based helper
- [ ] Integrate lip sync into OpenClaw TTS dispatch
- [ ] Symlink `skills/` → `~/.openclaw/skills/`
- [ ] Test full pipeline: voice → thinking expression → speaking with lip sync
- [ ] Set up VTube Studio mobile (iPhone/iPad) on Tailnet

---

## Phase 6 — Image Generation

### P8 · homeai-images

- [ ] Clone ComfyUI to `~/ComfyUI/`, install deps in venv
- [ ] Verify MPS is detected at launch
- [ ] Write and load launchd plist (`com.homeai.comfyui.plist`)
- [ ] Download SDXL base model
- [ ] Download Flux.1-schnell
- [ ] Download ControlNet models (canny, depth)
- [ ] Test generation via ComfyUI web UI (port 8188)
- [ ] Build and export `quick.json` workflow
- [ ] Build and export `portrait.json` workflow
- [ ] Build and export `scene.json` workflow (ControlNet)
- [ ] Build and export `upscale.json` workflow
- [ ] Write `skills/comfyui.py` full implementation
- [ ] Test skill: `comfyui.quick("test prompt")` → image file returned
- [ ] Collect character reference images for LoRA training
- [ ] Train SDXL LoRA with kohya_ss
- [ ] Load LoRA into `portrait.json`, verify character consistency
- [ ] Symlink `skills/` → `~/.openclaw/skills/`
- [ ] Test via OpenClaw: "Generate a portrait of Aria looking happy"
- [ ] Add ComfyUI to Uptime Kuma monitors

---

## Phase 7 — Extended Integrations & Polish

- [ ] Deploy Music Assistant (Docker), integrate with Home Assistant
- [ ] Complete `skills/music.py` in OpenClaw
- [ ] Deploy Snapcast server on Mac Mini
- [ ] Configure Snapcast clients on ESP32 units for multi-room audio
- [ ] Configure Authelia as 2FA layer in front of web UIs
- [ ] Build advanced n8n workflows (calendar reminders, daily briefing v2)
- [ ] Create iOS Shortcuts to trigger OpenClaw from iPhone widget
- [ ] Configure ntfy/Pushover alerts in Uptime Kuma for all services
- [ ] Automate mem0 + character config backup to Gitea (daily)
- [ ] Train custom wake word using character's name
- [ ] Document all service URLs, ports, and credentials in a private Gitea wiki
- [ ] Tailscale ACL hardening — restrict which devices can reach which services
- [ ] Stress test: reboot Mac Mini, verify all services recover in <2 minutes

---

## Open Decisions

- [ ] Confirm character name (determines wake word training)
- [ ] Confirm OpenClaw version/fork and Ollama compatibility
- [ ] Live2D model: purchase off-the-shelf or commission custom?
- [ ] mem0 backend: Chroma (simple) vs Qdrant Docker (better semantic search)?
- [ ] Snapcast output: ESP32 built-in speakers or dedicated audio hardware per room?
- [ ] Authelia user store: local file vs LDAP?