About this series
Who it's for
Business professionals and nonprofit employees — from your twenties to your eighties — who use or are about to use AI at work, and who want conceptual confidence rather than programming skills. No technical background is assumed; every term is defined before it's relied on.
How it teaches
Each lesson introduces exactly one idea and follows the same shape: a prediction you commit to before the reveal, one concrete analogy (with its limits stated honestly), a hands-on interaction that lets you cause the effect yourself, a realistic workplace example, a knowledge check that tests understanding rather than vocabulary, and one habit you can start the same day. Optional "Go deeper" sections carry the technical detail for those who want it.
You'll meet four recurring professionals throughout: Maya, an AI experience designer at a financial advice firm; Luka, a nonprofit fundraiser and grants consultant; Zane, an education and student-intake consultant; and Kerry, an allied health practitioner. Their situations are composites, built to be realistic.
Standards we hold ourselves to
Honesty about machines. We never describe an AI model as thinking, knowing or understanding the way a person does, and we never call it a database, search engine or brain. When an analogy helps, we also tell you exactly where it breaks.
Honesty about claims. Durable concepts are taught as concepts; perishable facts (product features, prices, regulations) are flagged and dated, verified before publication, and scheduled for review. Where models and providers differ, we say so.
Honesty about limits. The demonstrations on this site are simulations running in your browser, labelled as illustrative. We use simulated behaviour wherever reproducibility matters, and would only use a live AI where variability itself is the lesson — with clear notice first.
Balance. No doom, no hype. Capabilities, limitations and consequences, in proportion.
A model-neutral course
This series is model neutral. It does not speak for, favour or promote any one frontier model or any one open-weight or open-source model — it teaches the concepts that hold true across providers, not the habits of a single product.
Multiple models were used to help build it. That wasn't strictly necessary, but it was an important learning exercise in its own right. Now the meta part — how it was built:
- Orchestration & planning: Claude Fable 5
- Outline: Qwen 32B (running locally on a DGX Spark)
- Reasoning & verification: DeepSeek 32B (also local)
- UX review: OpenAI GPT-5.6
- Improvement passes: Kimi K3
- Code build: Claude Sonnet 5
- QA: a human — Shrav and others in the team — every single lesson, two to three full iteration cycles
Standing on the shoulders of giants
The course follows part of my own learning journey, and draws on the work of these people and pairs:
- Andrew Ng — accessible foundations and industry direction
- Andrej Karpathy — generation, neural networks and language models
- Jay Alammar — visual explanation
- Lilian Weng — research synthesis
- Sebastian Raschka — technical construction and precision
- Jeremy Howard — practical learning through experimentation
- Ethan Mollick — workplace and educational consequences
- Simon Willison — current capabilities, tools and security
- Arvind Narayanan and Sayash Kapoor — claim checking and responsible scepticism
- Melanie Mitchell or François Chollet — intelligence, reasoning and unresolved questions
Best sources by your AI stack
If a particular layer of the stack interests you, here is who to read next.
| Your lesson layer | Best people to consult |
|---|---|
| Training data | Andrew Ng, Jeremy Howard, Sebastian Raschka |
| Tokens and generation | Andrej Karpathy, Jay Alammar, Sebastian Raschka |
| Neural networks and attention | Karpathy, Alammar, Raschka, Howard |
| Context and prompting | Lilian Weng, Ethan Mollick, Simon Willison |
| Multimodality | Jay Alammar, Chip Huyen |
| Search and retrieval | Simon Willison, Eugene Yan, Jason Liu |
| Tools and actions | Simon Willison, Chip Huyen, Hamel Husain |
| Memory and agents | Lilian Weng, Nathan Lambert, Simon Willison |
| Failure and safety | Narayanan, Kapoor, Mitchell, Willison |
| Intelligence and the future | Chollet, Mitchell, Marcus, Lambert |
| Human judgement and work | Mollick, Ng, Narayanan and Kapoor |
Where this is going
All 21 lessons are live — the series is complete. Optional accounts (synchronised progress, a completion certificate and a capability profile) are still in production, and will never be required for learning. Content follows Australian privacy, consumer protection and accessibility expectations while staying globally relevant.
This series is general information, not legal, financial or clinical advice.