- The author spends significant time in AI chat sessions for personal and professional tasks, but faces frustrations in retrieving and reusing context from past conversations.
- Current tools like Claude Projects, linear issues, or doc platforms fail to align with human mental models for organizing knowledge, leading to inefficiencies in storing and loading AI context.
- Key insights include the mismatch between session-based organization and human memory, the lack of meta-context in AI readings, and the overlap between human and AI memory needs for natural language artifacts.
- The author built a minimal solution using Claude Code skills to create a knowledge base with folders as topics and files as items, enabling better context capture and retrieval.
- Future plans involve developing an Obsidian-like view layer and more granular control over context loading to enhance the system.