- Part 11 min
What AI actually is (a no-jargon primer)
Before you can use AI well you need a working mental model of what it is. This post unpacks the words "AI", "LLM", "model", "training", "inference", "agent", "RAG" and a few others, in plain language. Post 1 of the AI Foundations series.
- Part 21 min
Your AI roadmap, the index for everything that follows
A self-paced training course in AI, offered as a series of blog posts. This page is the syllabus. Each section links to dedicated lessons. Skip nothing in Track 1. Stop where it stops being useful for your job.
- Part 31 min
The AI vocabulary you'll keep hearing
A working glossary of the words you'll hit reading anything about AI in 2026. Grouped by category. One-line definitions and one-line "why it matters" notes. Bookmark it. Post 3 of the AI Foundations series.
- Part 41 min
Talking to AI well, prompt engineering for everyone
Prompt engineering is just the discipline of being clear, specific, and structured when you ask a model for something. This post collects the patterns we reach for daily, with copy-paste examples you can try right now. Post 2 of the AI Foundations series.
- Part 51 min
Inside an LLM, how language models actually work
A non-mathematical look at what is happening inside a large language model when you press send. Tokens, embeddings, attention, and why "predict the next word" turns out to be enough to write code and pass exams. Post 5 of the AI Foundations series.
- Part 61 min
AI tools worth your time in 2026
An opinionated tour of the AI tools we actually keep open every day. What each is good at, where each falls down, and why a small toolkit beats a sprawling one. Post 6 of the AI Foundations series.
- Part 71 min
AI in your daily workflow, for people who do not code
Concrete patterns for embedding AI into knowledge work. Drafting, summarising, researching, dictating, planning. The skill is recognising the next opportunity to apply the pattern. Post 7 of the AI Foundations series.
- Part 81 min
Pair-programming with AI, for working engineers
AI as the partner you always wanted, but only if you keep doing the parts of programming that make you better. The patterns we use, the failure modes we have learned to spot, and the rules we put around the agent. Post 8 of the AI Foundations series.