Adds a self-evolving cognitive layer inspired by vibeship-spark-intelligence, adapted for Timmy's agent architecture. Spark captures swarm events, runs EIDOS prediction-evaluation loops, consolidates memories, and generates advisory recommendations — all backed by SQLite consistent with existing patterns. New modules: - spark/memory.py — event capture with importance scoring + memory consolidation - spark/eidos.py — EIDOS cognitive loop (predict → observe → evaluate → learn) - spark/advisor.py — ranked advisory generation from accumulated intelligence - spark/engine.py — top-level API wiring all subsystems together Dashboard: - /spark/ui — full Spark Intelligence dashboard (3-column: status/advisories, predictions/memories, event timeline) with HTMX auto-refresh - /spark — JSON API for programmatic access - SPARK link added to navigation header Integration: - Coordinator hooks emit Spark events on task post, bid, assign, complete, fail - EIDOS predictions generated when tasks are posted, evaluated on completion - Memory consolidation triggers when agents accumulate enough outcomes - SPARK_ENABLED config toggle (default: true) Tests: 47 new tests covering all Spark subsystems + dashboard routes. Full suite: 538 tests passing. https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
Timmy Time — Mission Control
A local-first, sovereign AI agent system. Talk to Timmy, watch his swarm, gate API access with Bitcoin Lightning — all from a browser, no cloud required.
What's built
| Subsystem | Description |
|---|---|
| Timmy Agent | Agno-powered agent (Ollama default, AirLLM optional for 70B/405B) |
| Mission Control | FastAPI + HTMX dashboard — chat, health, swarm, marketplace |
| Swarm | Multi-agent coordinator — spawn agents, post tasks, run Lightning auctions |
| L402 / Lightning | Bitcoin Lightning payment gating for API access |
| Voice | NLU intent detection + TTS (pyttsx3, no cloud) |
| WebSocket | Real-time swarm live feed |
| Mobile | Responsive layout with full iOS safe-area and touch support |
| CLI | timmy, timmy-serve, self-tdd entry points |
228 tests, 100% passing.
Prerequisites
Python 3.11+
python3 --version # must be 3.11+
If not: brew install python@3.11
Ollama — runs the local LLM
brew install ollama
# or download from https://ollama.com
Quickstart
# 1. Clone
git clone https://github.com/Alexspayne/Timmy-time-dashboard.git
cd Timmy-time-dashboard
# 2. Install
make install
# or manually: python3 -m venv .venv && source .venv/bin/activate && pip install -e ".[dev]"
# 3. Start Ollama (separate terminal)
ollama serve
ollama pull llama3.2
# 4. Launch dashboard
make dev
# opens at http://localhost:8000
Common commands
make test # run all 228 tests (no Ollama needed)
make test-cov # test + coverage report
make dev # start dashboard (http://localhost:8000)
make watch # self-TDD watchdog (60s poll, alerts on regressions)
Or with the bootstrap script (creates venv, tests, watchdog, server in one shot):
bash activate_self_tdd.sh
bash activate_self_tdd.sh --big-brain # also installs AirLLM
CLI
timmy chat "What is sovereignty?"
timmy think "Bitcoin and self-custody"
timmy status
timmy-serve start # L402-gated API server (port 8402)
timmy-serve invoice # generate a Lightning invoice
timmy-serve status
Mobile access
The dashboard is fully mobile-optimized (iOS safe area, 44px touch targets, 16px input to prevent zoom, momentum scroll).
# Bind to your local network
uvicorn dashboard.app:app --host 0.0.0.0 --port 8000 --reload
# Find your IP
ipconfig getifaddr en0 # Wi-Fi on macOS
Open http://<your-ip>:8000 on your phone (same Wi-Fi network).
Mobile-specific routes:
/mobile— single-column optimized layout/mobile-test— 21-scenario HITL test harness (layout, touch, scroll, notch)
AirLLM — big brain backend
Run 70B or 405B models locally with no GPU, using AirLLM's layer-by-layer loading. Apple Silicon uses MLX automatically.
pip install ".[bigbrain]"
pip install "airllm[mlx]" # Apple Silicon only
timmy chat "Explain self-custody" --backend airllm --model-size 70b
Or set once in .env:
TIMMY_MODEL_BACKEND=auto
AIRLLM_MODEL_SIZE=70b
| Flag | Parameters | RAM needed |
|---|---|---|
8b |
8 billion | ~16 GB |
70b |
70 billion | ~140 GB |
405b |
405 billion | ~810 GB |
Configuration
cp .env.example .env
# edit .env
| Variable | Default | Purpose |
|---|---|---|
OLLAMA_URL |
http://localhost:11434 |
Ollama host |
OLLAMA_MODEL |
llama3.2 |
Model served by Ollama |
DEBUG |
false |
Enable /docs and /redoc |
TIMMY_MODEL_BACKEND |
ollama |
ollama | airllm | auto |
AIRLLM_MODEL_SIZE |
70b |
8b | 70b | 405b |
L402_HMAC_SECRET |
(default — change in prod) | HMAC signing key for macaroons |
L402_MACAROON_SECRET |
(default — change in prod) | Macaroon secret |
LIGHTNING_BACKEND |
mock |
mock | lnd |
Architecture
Browser / Phone
│ HTTP + HTMX + WebSocket
▼
┌─────────────────────────────────────────┐
│ FastAPI (dashboard.app) │
│ routes: agents, health, swarm, │
│ marketplace, voice, mobile │
└───┬─────────────┬──────────┬────────────┘
│ │ │
▼ ▼ ▼
Jinja2 Timmy Swarm
Templates Agent Coordinator
(HTMX) │ ├─ Registry (SQLite)
├─ Ollama ├─ AuctionManager (L402 bids)
└─ AirLLM ├─ SwarmComms (Redis / in-memory)
└─ SwarmManager (subprocess)
│
├── Voice NLU + TTS (pyttsx3, local)
├── WebSocket live feed (ws_manager)
├── L402 Lightning proxy (macaroon + invoice)
├── Push notifications (local + macOS native)
└── Siri Shortcuts API endpoints
Persistence: timmy.db (Agno memory), data/swarm.db (registry + tasks)
External: Ollama :11434, optional Redis, optional LND gRPC
Project layout
src/
config.py # pydantic-settings — all env vars live here
timmy/ # Core agent (agent.py, backends.py, cli.py, prompts.py)
dashboard/ # FastAPI app, routes, Jinja2 templates
swarm/ # Multi-agent: coordinator, registry, bidder, tasks, comms
timmy_serve/ # L402 proxy, payment handler, TTS, serve CLI
voice/ # NLU intent detection
websocket/ # WebSocket connection manager
notifications/ # Push notification store
shortcuts/ # Siri Shortcuts endpoints
self_tdd/ # Continuous test watchdog
tests/ # 228 tests — one file per module, all mocked
static/style.css # Dark mission-control theme (JetBrains Mono)
docs/ # GitHub Pages landing page
AGENTS.md # AI agent development standards ← read this
.env.example # Environment variable reference
Makefile # Common dev commands
Troubleshooting
ollama: command not found — install from brew install ollama or ollama.com
connection refused in chat — run ollama serve in a separate terminal
ModuleNotFoundError: No module named 'sqlalchemy' — re-run install to pick up the updated agno[sqlite] dependency:
make install
ModuleNotFoundError: No module named 'dashboard' — activate the venv:
source .venv/bin/activate && pip install -e ".[dev]"
Health panel shows DOWN — Ollama isn't running; chat still works but returns the offline error message
L402 startup warnings — set L402_HMAC_SECRET and L402_MACAROON_SECRET in
.env to silence them (required for production)
For AI agents contributing to this repo
Read AGENTS.md. It covers per-agent assignments, architecture
patterns, coding conventions, and the v2→v3 roadmap.
Roadmap
| Version | Name | Status | Milestone |
|---|---|---|---|
| 1.0.0 | Genesis | ✅ Complete | Agno + Ollama + SQLite + Dashboard |
| 2.0.0 | Exodus | 🔄 In progress | Swarm + L402 + Voice + Marketplace |
| 3.0.0 | Revelation | 📋 Planned | Lightning treasury + single .app bundle |