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Modular, communications-first field computer designed for outdoors, EDC, survival, and trades/handyman use.

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# Waycore

Modular, communications-first field computer for the outdoors

<a href="docs/vision.md">Vision</a> •
 <a href="docs/overview.md">Overview</a> •
 <a href="docs/architecture/architecture.md">Architecture</a> •
 <a href="#screenshots">Screenshots</a> •
 <a href="progress/SUMMARY.md">Progress</a> •
 <a href="docs/local_dev_docker.md">Development</a> •
 <a href="CONTRIBUTING.md">Contributing</a>

---

---

## What is Waycore?

Waycore is a **modular, rugged, handheld field computer** designed for outdoors,
EDC, survival, and trades/handyman use. The system uses a split-brain design: a
Linux SBC "main brain" (UI, AI, storage, networking) paired with a low-power
ESP32-S3 sidecar (radios, GPS, SOS, power policy) to ensure reliability even
when the UI is busy or rebooting.

### Key Principles

- **Communications-first** — Works when phones and networks fail
- **Modular by design** — Core device + expandable external modules
- **On-device intelligence** — AI without cloud dependency
- **Reliable & resilient** — SOS works even if the UI crashes
- **Developer-friendly** — Clear interfaces and mockable hardware

## Documentation

### Core Documentation

| Document                                          | Description                                                   |
| ------------------------------------------------- | ------------------------------------------------------------- |
| [Vision](docs/vision.md)                          | Full aspirational scope and long-term direction               |
| [Overview](docs/overview.md)                      | Project scope, architecture decisions, and MVP definition     |
| [Architecture](docs/architecture/architecture.md) | Detailed software architecture, services, APIs, and standards |
| [API Reference](docs/api/)                        | Auto-generated OpenAPI specifications for all services        |
| [Database Schema](docs/database-schema.md)        | Database structure, tables, and data models                   |
| [AI Model Guide](docs/models/README.md)           | Model management, uploads, and supported formats              |
| [Knowledge Base](docs/knowledge-base.md)          | RAG knowledge base for AI-powered outdoor assistance          |
| [Progress](progress/SUMMARY.md)                   | Current implementation status and task tracking               |

### Developer Guides

| Document                                                         | Description                                     |
| ---------------------------------------------------------------- | ----------------------------------------------- |
| [App Development Guide](docs/developer/app-development-guide.md) | Complete guide to building Waycore apps         |
| [App Manifest Schema](docs/developer/app-manifest.md)            | Manifest configuration and validation reference |
| [App Backend Integration](docs/developer/app-backend.md)         | Building Python backend services for apps       |
| [UI Components](docs/developer/ui-components.md)                 | Core UI component library and theme system      |
| [Local Development](docs/local_dev_docker.md)                    | Docker-based development setup and workflows    |
| [IPC Guide](local_plan/12-ipc-implementation-guide.md.md)        | Inter-process communication strategy            |

### User Manual

| Document                                                    | Description                                           |
| ----------------------------------------------------------- | ----------------------------------------------------- |
| [User Manual](docs/manual/README.md)                        | Complete user documentation and app guides            |
| [Getting Started](docs/manual/getting-started.md)           | First-time setup and home screen overview             |
| [AI Assistant](docs/manual/apps/ai-assistant.md)            | Using the AI for outdoor knowledge and image analysis |
| [Troubleshooting](docs/manual/reference/troubleshooting.md) | Common issues and solutions                           |

### Legal

| Document                                                           | Description                                                 |
| ------------------------------------------------------------------ | ----------------------------------------------------------- |
| [AI Disclaimer](docs/legal/ai-disclaimer.md)                       | Important AI limitations and safety information             |
| [Third-Party Attributions](docs/legal/third-party-attributions.md) | Licenses and attributions for dependencies and data sources |

## Quick Start

### Prerequisites

- Docker & Docker Compose
- Python 3.11+
- Poetry

### Development Scripts (Recommended)

```bash
# Start everything (downloads models on first run)
./scripts/dev-start.sh

# Start with UI
./scripts/dev-start.sh --ui

# Check status
./scripts/dev-status.sh

# Restart services
./scripts/dev-restart.sh

# Restart specific service with rebuild
./scripts/dev-restart.sh --service ai-service --rebuild

# Stop everything
./scripts/dev-stop.sh
```

### Manual Setup

```bash
# Start infrastructure and services
docker compose -f docker/compose/dev.yml up -d

# Verify services are running
curl http://localhost:8000/health   # comms-bridge
curl http://localhost:8001/health   # sensor-hub
curl http://localhost:8002/health   # data-logger
curl http://localhost:8010/health   # ai-service
```

### Run Tests

```bash
poetry install --no-interaction

# Unit tests
poetry run pytest device/libs/ -q
poetry run pytest device/services/ -q

# Quality checks
poetry run ruff check device/
poetry run black --check device/
poetry run mypy device/ --strict
```

## Repository Structure

```
device/
├── apps/ui/              # Qt/QML frontend with PySide6 backend
├── libs/
│   ├── schemas/          # Pydantic message schemas
│   ├── hil/              # Hardware abstraction interfaces
│   ├── messaging/        # MQTT message bus
│   ├── database/         # Domain-specific SQLite databases (general, mesh, ai)
│   └── common/           # Shared utilities
├── services/
│   ├── core_daemon/      # System state, power policy
│   ├── module_manager/   # Module discovery & lifecycle
│   ├── comms_bridge/     # Radio management, TAK gateway
│   ├── ai_service/       # On-device inference
│   └── data_logger/      # Event persistence
└── drivers/
    ├── mock/             # Mock hardware for development
    └── real/             # Real hardware drivers
docs/                     # Documentation
progress/                 # Task tracking system
docker/                   # Docker Compose and configs
```

## Services

| Service        | Port | Description                                       |
| -------------- | ---- | ------------------------------------------------- |
| core-daemon    | 8000 | System state machine, power policy, orchestration |
| module-manager | 8001 | Module discovery and lifecycle management         |
| data-logger    | 8002 | Event persistence and retrieval                   |
| comms-bridge   | 8003 | Radio coordination and TAK gateway                |
| ai-service     | 8010 | Visual Q&A, image classification, chat inference  |

All services support Unix domain sockets (preferred) and HTTP/JSON APIs.

## UI Application

The Qt/QML frontend provides:

- **Home grid** with app tiles (AI, Maps, Compass, Meshtastic, Camera, Notes,
  etc.)
- **Status bar** with time, battery, connectivity indicators
- **Theme system** with dark mode optimized for outdoor visibility
- **Settings** for units, display, developer options

Run the UI locally:

```bash
cd device/apps/ui && python main.py
```

## AI Model Management

Waycore runs AI models locally on-device for offline-first operation. Due to
hardware constraints (Raspberry Pi 5 with 4GB RAM), only **one model per
category** (language, vision) can be active at a time.

### Default Models

| Model         | Type     | Size   | Description                            |
| ------------- | -------- | ------ | -------------------------------------- |
| Phi-3 Mini 4K | Language | 2.3 GB | Chat/Q&A (GGUF, 4-bit quantized)       |
| MobileNetV3   | Vision   | 5 MB   | Image classification (TensorFlow Lite) |

### Uploading Custom Models

Upload models via the API or convenience script:

```bash
# Via script
./scripts/upload-model.sh path/to/model.gguf --id my-model --type language

# Via API
curl -X POST http://localhost:8010/api/models/upload \
  -F "file=@model.gguf" \
  -F "id=my-model" \
  -F "type=language"
```

### Supported Formats

- **Language models**: GGUF (llama.cpp compatible) — Phi-3, TinyLlama, etc.
- **Vision models**: TensorFlow Lite (.tflite) — MobileNet, EfficientNet, etc.

For detailed instructions, model download links, and troubleshooting, see the
[Model Management Guide](docs/models/README.md).

## Contributing

We welcome contributions! Please read:

- [Contributing Guide](CONTRIBUTING.md) — Code style, commit conventions, PR
  process
- [Code of Conduct](CODE_OF_CONDUCT.md) — Community guidelines
- [Security Policy](SECURITY.md) — Reporting vulnerabilities

## Progress Tracking

We use a file-based task tracking system:

- **[Progress Summary](progress/SUMMARY.md)** — Current status overview
- **[Task System](progress/README.md)** — How tasks are organized
- **[Tracking Guide](docs/ai_instructions/progress_tracking.md)** — Detailed
  workflow

Task categories include sequential phases, production tasks, improvements, and
ideas.

## License

This project is licensed under the terms in [LICENSE](LICENSE).

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