Learn
AI 101, without the hype
Plain-English guides for business leaders making sense of AI in 2026. No jargon, no marketing fluff, just what you need to know to make decent decisions about how AI fits into your work.
Read in order, or skip to whatever you're trying to figure out right now.
- 01Beginner· 6 min read
What Is AI? A Plain-English Introduction for Business Leaders
The 10-minute starting point on AI for business leaders.
- 02Beginner· 6 min read
What Is a Large Language Model (LLM)? A Plain-English Explainer
How Claude, ChatGPT, and Gemini actually work, without the math.
- 03Beginner· 7 min read
What Is an AI Agent? (And How It's Different from a Chatbot)
The plain-English difference between agents, chatbots, and automations.
- 04Beginner· 8 min read
How to Write a Good Prompt: A Beginner's Framework
The 3-part framework that fixes most bad AI output.
- 05Beginner· 8 min read
AI Tool Picker: Claude vs. ChatGPT vs. Gemini (2026)
Plain-English guide to picking Claude, ChatGPT, or Gemini for your team.
- 06Beginner· 7 min read
What Is RAG? (Retrieval-Augmented Generation), Explained Simply
How AI can 'look things up' in your documents, explained simply.
- 07Beginner· 7 min read
AI for Growth Teams: A Starting Guide
If you're a growth leader, this is where to start with AI.
- GReference· 50+ entries
AI & Automation Glossary
Plain-English definitions of every term you'll encounter: ChatGPT, Claude, Gemini, RAG, MCP, fine- tuning, AEO, and more. Searchable reference.
Once you've got the fundamentals
The /learn section is the 101 layer. When you're ready to go deeper, the rest of the site is organized around what you need to do next:
Insights
Deeper thinking, contrarian takes, and real build stories. For when you're past the fundamentals.
Tools
Calculators, assessments, and decision tools for your specific situation. Measurable outputs, not generic advice.
Resources
Templates, starter packs, and printable guides to take with you. Scoping brief, Claude Project starter pack, one-pager.
Work
Real case studies with architecture, metrics, and outcomes. What AI builds actually look like in production.