The curriculum spine

What are you working on? How much are you responsible for?

Families group related kinds of work. Tracks name the task; levels name the blast radius if it goes wrong.

Swipe sideways to compare responsibility levels. Track names stay visible while you browse.

Family 1

Use

Get value from AI in your own work

Use tracks arranged by responsibility level.
Track	L1UserOnly you	L2Power userYour team sees a bad document or a wrong figure	L3BuilderColleagues depend on something you built	L4IntegratorCompany or lab systems and real data are in the loop	L5OperatorWork stops when it breaks, and it must survive you leaving
Track 01Foundations & judgement	T01-L01 What AI can and can't do for your work 22 min read	T01-L02 Trust but verify: checking output before it leaves your desk 20 min read	T01-L03 The simplest thing that works: chat, automation, app or agent 25 min read	No book yet	No book yet
Track 02Chat & assistants	T02-L01 Getting good answers from a chatbot 18 min read	T02-L02 Your daily driver: files, projects and memory 21 min read	T02-L03 A shared assistant for your team 28 min read	T02-L04 A self-hosted assistant 30 min read	T02-L05 Running the assistant for everyone 30 min read
Track 03Prompting & context	T03-L01 Prompting basics 20 min read	T03-L02 Context engineering 20 min read	T03-L03 Test-driven prompting 60 min read	T03-L04 Prompts as versioned assets 32 min read	T03-L05 Regression sets and evaluation ops 35 min read
Track 09Create media	T09-L01 Images, voice and video for your own work 30 min read	T09-L02 Consistent assets for your company or lab 45 min read	T09-L03 Media pipelines 60 min read	T09-L04 Self-hosted media models 90 min read	T09-L05 Scale, provenance and rights 60 min read
Track 15AI in research	T15-L01 AI in your research workflow 24 min read	T15-L02 Reproducibility with AI in the loop 45 min read	T15-L03 Data, ethics and review 30 min read	No book yet	No book yet

Family 2

Build

Make something that runs

Build tracks arranged by responsibility level.
Track	L1UserOnly you	L2Power userYour team sees a bad document or a wrong figure	L3BuilderColleagues depend on something you built	L4IntegratorCompany or lab systems and real data are in the loop	L5OperatorWork stops when it breaks, and it must survive you leaving
Track 04Your documents & knowledge	T04-L01 Ask questions about your own files 30 min read	T04-L02 A knowledge base your team can ask 26 min read	T04-L03 Retrieval that cites and doesn't lie 90 min read	T04-L04 Permission-aware retrieval over real systems 36 min read	T04-L05 Keeping a knowledge base alive 45 min read
Track 05Automation	T05-L01 Your first automation 24 min read	T05-L02 Automations with AI in the middle 24 min read	T05-L03 The pipeline that writes itself 38 min read	T05-L04 Making an automation safe to fail 38 min read	T05-L05 Operating automations 45 min read
Track 06Build apps	No book yet	T06-L02 A tool from a prompt 24 min read	T06-L03 An app your colleagues actually use 42 min read	T06-L04 Real data behind it 40 min read	T06-L05 Owning and operating your app 120 min read
Track 07Agents	No book yet	No book yet	T07-L03 Your first agent 120 min read	T07-L04 Agents with real credentials 90 min read	T07-L05 Many agents, and memory 120 min read
Track 08Write code	No book yet	T08-L02 Code without being a developer 34 min read	T08-L03 Spec-driven development 68 min read	T08-L04 Working in a real repo 90 min read	T08-L05 Agents in CI, and what they pull in 120 min read
Track 10Data & analysis	T10-L01 Ask questions about a spreadsheet 30 min read	T10-L02 Clean a messy export 60 min read	T10-L03 A reproducible analysis pipeline 90 min read	T10-L04 Pipelines over real data 120 min read	T10-L05 Operating data pipelines 90 min read
Track 14Connect your tools	No book yet	No book yet	T14-L03 Connect an assistant to your tools 28 min read	T14-L04 Build your own connector 55 min read	T14-L05 Operating connectors 45 min read

Family 3

Run

Operate it yourself

Run tracks arranged by responsibility level.
Track	L1UserOnly you	L2Power userYour team sees a bad document or a wrong figure	L3BuilderColleagues depend on something you built	L4IntegratorCompany or lab systems and real data are in the loop	L5OperatorWork stops when it breaks, and it must survive you leaving
Track 11Run your own AI	T11-L01 A model on your own laptop 24 min read	T11-L02 A local model your tools can call 30 min read	T11-L03 Containers, honestly 60 min read	T11-L04 Your own AI server 120 min read	T11-L05 Serving at scale 120 min read

Family 4

Govern & spread

Make it safe and make it stick

Govern & spread tracks arranged by responsibility level.
Track	L1UserOnly you	L2Power userYour team sees a bad document or a wrong figure	L3BuilderColleagues depend on something you built	L4IntegratorCompany or lab systems and real data are in the loop	L5OperatorWork stops when it breaks, and it must survive you leaving
Track 12Security, privacy & governance	T12-L01 What you may and may not paste into AI at work 18 min read	T12-L02 Privacy and labelling in everyday work 30 min read	T12-L03 Rules for things that run without you 45 min read	T12-L04 Put a lock on it: securing a self-hosted stack 60 min read	T12-L05 Governance, evidence and handover 90 min read
Track 13Adoption & enablement	No book yet	T13-L02 Show a colleague: the 30-minute demo 20 min read	T13-L03 Your AI usage policy 35 min read	T13-L04 Rolling it out 42 min read	T13-L05 Training records and AI literacy 50 min read

Every book keeps its address even while its title and content evolve.

What AI can and can't do for your work

Trust but verify: checking output before it leaves your desk

The simplest thing that works: chat, automation, app or agent

Getting good answers from a chatbot

Your daily driver: files, projects and memory

A shared assistant for your team

A self-hosted assistant

Running the assistant for everyone

Prompting basics

Context engineering

Test-driven prompting

Prompts as versioned assets

Regression sets and evaluation ops

Images, voice and video for your own work

Consistent assets for your company or lab

Media pipelines

Self-hosted media models

Scale, provenance and rights

AI in your research workflow

Reproducibility with AI in the loop

Data, ethics and review

Ask questions about your own files

A knowledge base your team can ask

Retrieval that cites and doesn't lie

Permission-aware retrieval over real systems

Keeping a knowledge base alive

Your first automation

Automations with AI in the middle

The pipeline that writes itself

Making an automation safe to fail

Operating automations

A tool from a prompt

An app your colleagues actually use

Real data behind it

Owning and operating your app

Your first agent

Agents with real credentials

Many agents, and memory

Code without being a developer

Spec-driven development

Working in a real repo

Agents in CI, and what they pull in

Ask questions about a spreadsheet

Clean a messy export

A reproducible analysis pipeline

Pipelines over real data

Operating data pipelines

Connect an assistant to your tools

Build your own connector

Operating connectors

A model on your own laptop

A local model your tools can call

Containers, honestly

Your own AI server

Serving at scale

What you may and may not paste into AI at work

Privacy and labelling in everyday work

Rules for things that run without you

Put a lock on it: securing a self-hosted stack

Governance, evidence and handover

Show a colleague: the 30-minute demo

Your AI usage policy

Rolling it out

Training records and AI literacy