Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

Building a personalized AI Research OS transforms fragmented research notes into a structured, evolving knowledge base. This system moves beyond static note-taking by utilizing a three-layer architecture: raw data, a YAML-based index, and an LLM-generated wiki layer. By prioritizing local file systems like Obsidian and GitHub, users maintain ownership while enabling agents to perform deep research, cross-reference concepts, and generate comparisons without redundant context loading. This approach optimizes token usage through executive summaries and hierarchical referencing, ensuring that research remains actionable and contextually aware. Designed for AI engineers, the workflow emphasizes practical, agent-native memory management over complex, opaque database infrastructures, allowing users to build a self-compounding "second brain" that reflects personal values and project-specific needs.