Part 5 of 5. ← Part 1: The Why · ← Part 2: The Architecture · ← Part 3: AI Curation Workflows · ← Part 4: Agents and Weekly Audits

The previous four parts built the system and the habits around it. Capture works, curation is mostly automated, and a weekly audit keeps quality from drifting. But none …

Part 4 of 5. ← Part 1: The Why · ← Part 2: The Architecture · ← Part 3: AI Curation Workflows · → Part 5: The Publishing Pipeline

The first three posts built the system: the reasoning in part 1, the Hugo structure in part 2, and the capture plus curation workflows in part 3. This part is about what …

Part 3 of 5. ← Part 2: The Hugo Architecture · Part 4: Agents, SEO, and Weekly Audits →

The structural layer in part 2 handles visibility and builds. This post covers the AI workflows that handle the actual ongoing work of a second mind: capturing, curating, and surfacing knowledge.

The design …

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: RAG retrieves from raw documents on each query but still rediscovers knowledge from scratch every time; a persistent wiki is better.
• Persistent compounding: The wiki is a “persistent, compounding artifact” — synthesis becomes durable rather than disposable.
• Human role: …

• 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, …