Part 2 of 5. ← Part 1: The Why · Part 3: AI Curation Workflows →

The structural core of this system is deliberately simple. Hugo already does most of the work. The job is to wire it together correctly.

The visibility toggle: draft as a first-class concept

Hugo’s draft frontmatter field has …

I’ve been thinking seriously about knowledge management for years. I’ve tried Notion, Obsidian, Loop, and a half-dozen other tools. Each time the system works brilliantly for about three months and then quietly falls apart. Not because the tool is bad. Because maintenance is hard, and …

• Core argument: Traditional second brain systems built on CODE/PARA methodology become impossible to maintain manually at scale.
• Solution: Integrate AI agents to actively participate — organising notes, creating cross-links, and distilling insights automatically.
• Three pillars: Machine-readable …

• The Enterprise Context Layer (ECL) is proposed as the essential solution to the “most alluring problem” in enterprise AI: moving beyond simple chatbots to truly integrated systems.
• It emphasizes the shift from AI having “memory” to AI having “authority,” focusing …

• Core problem: AI memory alone creates context collapse — it recalls fragments but can’t reason across them. A 3-layer Context Stack turns scattered facts into structured, business-specific knowledge your AI uses before every response.
• Layer 1 — Identity Context: Who you are: business model, ICP, …