I was having issues understanding this system. So I uploaded all of Jakes videos to notebook llm and used the ai studio to create me all of Jakes material explained to me easily. It has podcast, infographics, everything. I can do a video if you need one

I’m following the courses and making my first step to treat knowledge as code. ## Stack: Obsidian + NotebookLM + ContextMode + Docker + Git Thanks to mouthful Gemini i call this: Modular Context Architecture (MCA). In this system, each folder represents a specific "Knowledge Node" with a standardized file syntax to optimize how an agent routes and consumes information. ## The Node Structure Each node (folder) contains four primary files designed to tier information by depth of intent: 1. README.md (Entry Point): Contains the minimal viable information required to identify the node’s purpose. This serves as the first layer for an agent’s broad search or initial embedding match. 2. CONTEXT.md (The Schema): A deeper explanation of the node’s internal logic. It acts as a map, referencing more granular sub-files (e.g., Specific_Detail_A.md) that should only be loaded if the agent requires higher resolution on the topic. 3. REFERENCES.md (The Router): A dedicated file for internal [[wikilinks]] and external URLs. This separates the relational graph from the descriptive prose. 4. MEMORY.md (Heuristics): An optional file containing localized constraints, formatting rules, or "long-term" logic that the agent must apply strictly when working within that specific node. ## Example: Business Model Canvas - The Graph: The complete canvas. - The Node: The "Value Proposition" folder. - The Execution: An agent enters the "Value Prop" directory. It identifies the node via README.md. If the task requires analyzing customer pain points, the CONTEXT.md directs the agent to a specific Pains.md file. Throughout the process, the agent adheres to tone and formatting constraints defined in MEMORY.md. ## Technical Advantages - Context Efficiency: The agent only retrieves the specific "layer" of documentation necessary for the current task. - Scalability: New domain knowledge can be integrated by adding a standardized folder without restructuring the existing graph. - Human-AI Interoperability: The vault remains navigable for human users while providing a clear, API-like structure for LLM parsing.