LLM roadmap
The phases of the OctoMY project, and who each one serves
How the OctoMY project plans to bring language models into its development and its applications. The LLM strategy gives the principles that every phase keeps.
Context
In 2025, ignoring LLMs was not an option. OctoMY uses them in three places:
- Development tooling - Accelerating the development process
- Runtime features - User-facing AI capabilities
- Server-side services - Backend AI integration
Target users
| Priority | Audience | Benefit |
|---|---|---|
| Primary | Hobby robotics community | Accessible AI-powered devices |
| Secondary | Expert roboticists | Sophisticated tooling |
| Immediate | OctoMY developers | LLM-accelerated development |
Implementation phases
Phase 1: Development acceleration
Shorten the LLM-fueled development feedback loop.
Components:
- uiautomation - Tool for UI testing and screenshot capture
- mocknode - Standardized test harness mimicking any node type
- Documentation generation - Automated screenshot capture
Value: Benefits developers immediately. LLM can invoke tools directly, creating closed feedback loops.
Phase 2: Automated testing
Maintain quality as development accelerates.
Components:
- uitest - Extends uiautomation for regression testing
- Visual regression - Screenshot comparison
- LLM-aided test authoring - Beyond unit tests
Value: While Phase 1 lives in the moment, Phase 2 secures the legacy.
Phase 3: OPAL runtime layer
Enable LLM-driven agent management with proper access control.
Components:
- Permission system
- Command API (structured JSON)
- Self-documenting endpoints
- User consent UI
Value: Safe, auditable LLM control of hardware.
Phase 4: Convenience features
User-facing LLM helpers that depend on OPAL.
Components:
- Autocomplete/suggestions
- Natural language commands
- Personality-driven interactions
Value: Accessible AI for end users.