AI integration

Core Concepts

Local Execution: Prioritizing on-device processing to enhance privacy and user control.
Agent Integration: Enabling seamless interaction between AI models and productivity tools.
Open Source: Leveraging community-driven projects for transparency and customization.
Native OS Integration: Deep embedding of AI capabilities directly into operating systems to streamline user workflows.
Knowledge Management: Utilizing tools like Obsidian to create robust Second Brain systems through AI.

Retro Computing & AI: Exploring the integration of modern AI assistants with legacy hardware and retro computing environments to extend the lifespan of older systems and test AI constraints.
AI-Driven Code Optimization: Applying AI to optimize codebases, including those for custom task managers and legacy systems, to improve efficiency and performance.
Custom Task Managers: Development of specialized tools like TMOG, enhanced by AI capabilities for better workflow management.
Hardware Constraints: Analyzing AI limitations and performance when deployed on non-standard or retro computing architectures.