Yiuno — Building a Knowledge Architecture with AI
{{PLACEHOLDER: One-line principle. A bold statement about knowledge architecture — e.g. “A knowledge system is not a collection of documents. It is an architecture of relationships.“}}
The challenge
{{PLACEHOLDER: The market problem.
Suggested angle: Most AI education falls into two traps — either too technical (developer-first) or too shallow (listicles and hype). For non-technical learners who want to genuinely understand AI, there is no structured, principled learning framework. The result: people either never start, or they consume without understanding.
Frame this as the problem your prospective client recognises in their own organisation or audience.}}
The approach
{{PLACEHOLDER: Your methodology.
What to cover:
- The knowledge graph model (interconnected concepts with prerequisites, not isolated articles)
- The concept card architecture (researched, sourced, comprehension gates)
- Learning paths as guided journeys through the graph
- The AI-assisted authoring workflow (Claude Code + Obsidian
- vault playbooks)
- The editorial principles (substrate before mechanics, human-first, principled)
This section should make a client think: “This person has a methodology, not just tools.“}}
The build
{{PLACEHOLDER: What was created.
Specifics to include:
- 89 concept cards across [X] domains
- 18 learning paths with prerequisite chains
- Agent-ready vault templates (AGENTS.md + playbooks)
- Static site (Obsidian + Quartz) — publicly accessible
- The knowledge system taxonomy
- The content pipeline (draft → review → publish → deploy)
Include a diagram if useful. Link to yiuno.org.}}
The impact
{{PLACEHOLDER: Results and current state.
What to include:
- Site live at yiuno.org
- Visitor metrics (if available)
- Downloads or engagement (if available)
- What it led to (CoLab IA workshops, consulting enquiries, speaking invitations)
- Honest about what is in progress}}