Level 1Starting out
Recommended for you
What is AI? A plain-English starter
A jargon-free introduction to modern AI: how chatbots work, what they are good and bad at, and how to use them safely from day one.
Free AI course downloads
The library
58 free PDF workbooks, from your very first prompt to frontier models. Workbooks by Mickarle Wagstaff-Irons - Micky Irons, published by Mickai LTD. No account needed.
Your path
58 workbooks
Level
Goal
Level 1
Starting out
Everyone starts here, and it is a great place to start.
Level 1Starting out
Recommended for you
Your first prompts: how to ask AI for what you actually want
Learn to ask AI for what you actually want: the six parts of a good prompt, how to improve a reply, and how to build a personal prompt library. Free tools only.
Level 1Starting out
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The free AI toolkit: how to choose tools and stay safe
How free AI tools work, how to choose and test them, how to check their current terms and privacy, how to spot fake apps and scams, and how to build your own starter stack.
Level 1Starting out
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Make YouTube videos with AI: a beginner's workflow
A beginner's end-to-end workflow for making YouTube videos with AI: script, voiceover, visuals, captions, upload, YouTube's disclosure rules, analytics and a 30-day plan.
Level 1Starting out
Recommended for you
Stay safe with AI: scams, fake media and your privacy
Spot AI-assisted scams and fake media, keep your privacy when using chatbots, know your basic UK data rights and where to report problems, with a simple routine to follow.
Level 1Starting out
Recommended for you
Use AI to study, research and write without losing your own thinking
Use an AI assistant as a study partner, not an answer machine: quiz yourself, verify every claim and citation, write in your own voice, and say how you used it.
Level 1Starting out
Recommended for you
Using AI to find a job: your CV, cover letter and interview prep
A practical, free workbook for using AI in a job search: tightening a CV against a specific job description, drafting and personalising a cover letter, and rehearsing interview answers, with honest limits on what AI can and cannot verify for you.
Level 2
Getting going
You have the basics. Time to get properly good.
Level 2Getting going
Recommended for you
AI images and video: rights, provenance, labels and quality checks
Use AI images and video responsibly: ownership in the UK, EU and US, tool terms, consent, labelling, Content Credentials, quality checks and simple records.
Level 2Getting going
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Build your first app with an AI coding assistant
Build a small bill-splitting web page with any AI coding assistant while staying in control: brief it, build in steps, review the code, test edge cases and keep safe copies.
Level 2Getting going
Recommended for you
Git for AI builders: commits, branches and pull requests
Learn Git from your first commit to a reviewed pull request, with safe habits for AI-written code: read every diff, commit small, undo safely and keep secrets out.
Level 2Getting going
Recommended for you
HTML: build a meaningful, accessible page
Build a real event page by hand with headings, landmarks, alt text, a data table and a labelled form. Check it with the W3C validator and a keyboard, then review AI-written HTML.
Level 2Getting going
Recommended for you
Running open-weight AI models on your own computer, for free
A practical, hands-on guide to running open-weight AI models on your own computer at no subscription cost, using Ollama, LM Studio or llama.cpp, with honest guidance on hardware limits and quantisation.
Level 2Getting going
Recommended for you
Which AI model should I use? Picking the right AI for the job
How to choose between AI assistants and models: what to weigh, how to run a fair five-prompt bake-off, and how to read leaderboards and benchmarks without being fooled.
Level 2Getting going
Recommended for you
Automate a spreadsheet workflow with AI, checks and an audit trail
Clean a messy spreadsheet with AI-suggested formulas without losing control: work on a copy, test every formula, log each change and reconcile the totals.
Level 2Getting going
Recommended for you
Build and launch a website with AI
Plan a simple site, have AI write the HTML and CSS, understand and check the code, then publish it free with HTTPS and sound search, accessibility and privacy basics.
Level 2Getting going
Recommended for you
CSS: responsive layouts, grids and typography
Style a page by hand so it reads well on any screen: box model, cascade, flexbox, grid, fluid type, container queries, dark mode and focus, all measured at three widths.
Level 2Getting going
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What is an AI agent? Agentic AI explained
A plain-English guide to AI agents: how they differ from chatbots and workflows, the parts and the loop, levels of autonomy, the main risks and how to keep a human in control.
Level 2Getting going
Recommended for you
AGI explained: what it means and what it does not
A balanced guide to artificial general intelligence: the main definitions, one published levels framework, how to read headlines and timelines, and what matters in practice.
Level 2Getting going
Recommended for you
Python: from first script to a useful automation
A complete beginner's route from installing Python to a tested, dry-run-first tool that sorts and reports on a folder, using only what comes with Python.
Level 2Getting going
Recommended for you
Code for as little as possible: VS Code, OpenCode and app builders
Work out what AI-assisted coding really costs and keep it low: how tools charge, a token cost calculator, free editors, OpenCode, local models and paid app builders.
Level 3
Building
You are ready to build things that work.
Level 3Building
Recommended for you
The loop harness: how AI agents actually run
How an agent harness runs a model in a loop: context, tools, permissions, budgets, stop conditions, logging and testing, with a minimal Python loop and a paper design exercise.
Level 3Building
Recommended for you
Build your first chatbot
Build a chatbot that behaves: model access, system prompt, history and context limits, guardrails, tone, a test set, logging and cost, on a no-code path or a code path.
Level 3Building
Recommended for you
Design a repeatable evaluation suite for an AI application
Build an offline AI evaluation harness with test cases, a rubric, regression comparisons, uncertainty checks and a reproducible release record.
Level 3Building
Recommended for you
Document pipelines: parsing, cleaning and chunking
Build a local document pipeline: clean UTF-8 text, create traceable chunks, test boundaries and measure which evidence survives together.
Level 3Building
Recommended for you
Tokenisation: train and inspect a small tokenizer
Train a byte-pair encoding tokeniser in plain Python, prove the round trip is lossless, and see how vocabulary size, languages, numbers and spaces change token counts.
Level 3Building
Recommended for you
Embeddings and vector search from first principles
Build vector search from scratch: turn text into vectors, compare them three ways, search by brute force, score it with recall@k and MRR, and see where it fails.
Level 3Building
Recommended for you
Host a chatbot on your website, safely
Put a chatbot on your site without leaking keys or running up bills: server function, limits, CORS, privacy, accessibility, fallbacks, monitoring and a launch checklist.
Level 3Building
Recommended for you
Prompt injection: threat analysis and defensive testing
Learn why prompt injection happens, analyse the harm it can do, and measure which defences work with a harmless offline Python lab that uses fake canary secrets.
Level 3Building
Recommended for you
SQL: model and query your first database
A beginner's route through SQL with SQLite: tables, keys and constraints, queries, joins, safe changes, indexes, injection-safe Python and reviewing AI-written SQL.
Level 3Building
Recommended for you
Tensors, gradients and a first training loop
Learn what training is by doing it on a CPU: tensors and shapes, the loss, gradients by hand and by autograd, the training loop, validation and the classic bugs.
Level 3Building
Recommended for you
Test-driven development with an AI coding assistant
Use failing examples to guide an AI coding assistant, test boundary cases, catch deliberate defects and hand over a small Python change with clear evidence.
Level 3Building
Recommended for you
Data analysis with Python: investigate and explain a dataset
Answer a real question from a messy table with pandas: inspect, clean with a log, group, join, chart, measure uncertainty and check AI-written analysis code.
Level 3Building
Recommended for you
GPU memory maths for LLMs and SLMs
A repeatable method for checking whether a language model fits your GPU: weights, KV cache, training memory and adapters, plus a small calculator you can run.
Level 3Building
Recommended for you
JavaScript: the browser, events and application state
Add behaviour to the event page with plain JavaScript: the DOM, events and delegation, one state object, form validation, focus, status messages and localStorage.
Level 3Building
Recommended for you
Node.js: build and test a small API
Build a local read-only HTTP API with Node.js: explicit routes, strict query validation, predictable errors and executable tests using built-in modules.
Level 3Building
Recommended for you
Privacy and data minimisation in AI applications
Keep personal data under control in an AI feature: UK GDPR principles, DPIAs, roles and transfers, and a redaction pipeline you run and test to see what it misses.
Level 3Building
Recommended for you
RAG and knowledge bases, step by step
Build a knowledge base a model can answer from: collect, clean, chunk, embed, retrieve, cite and evaluate, with security basics and a hands-on exercise using five short documents.
Level 3Building
Recommended for you
Tool calling and structured outputs: validate every response
Treat every model reply as untrusted input: write JSON Schema contracts, parse and validate each tool call, retry with feedback, and run tools with limits, dry runs and logs.
Level 3Building
Recommended for you
Go: build a concurrent service
Build a standard-library Go HTTP service with bounded workers, ordered results, input limits, cancellation and executable tests.
Level 3Building
Recommended for you
Open model licences, datasets and deployment obligations
Decide whether you may use, host and redistribute an 'open' model or dataset: licence families, dataset rights, model cards, AI-BOMs, hash checks and a checklist tool.
Level 3Building
Recommended for you
TypeScript: types for safer web applications
Build a typed reading-list boundary with runtime validation, explicit display states, compiler checks and executable tests.
Level 3Building
Recommended for you
AI governance: risk registers, evidence and human accountability
Govern an AI system without being an engineer: map hazards, score and evidence them, name owners and stop rights, make oversight real, and check the register with tested code.
Level 3Building
Recommended for you
Testing agent loops: budgets, termination and failure recovery
Test an agent loop locally: enforce budgets, replay model decisions, reject late results and verify bounded retries without paying for model calls.
Level 4
Frontier
You are at the edge. Let's go deeper.
Level 4Frontier
Recommended for you
Inference engines explained
How inference engines run a model: memory maths, quantisation, prefill and decode, batching, hardware, and serving a model safely over a local API.
Level 4Frontier
Recommended for you
Reasoning models and reasoning engines
What reasoning models are, when extra thinking pays off, how to control it, why a reasoning trace is not proof, and how classical reasoning engines differ.
Level 4Frontier
Recommended for you
How to evaluate a new frontier model the day it lands
A ten-step method for judging a new model: primary sources, licence, architecture, hardware, benchmarks, your own tests, safety, jurisdiction and a decision record.
Level 4Frontier
Recommended for you
Hybrid search and reranking for better retrieval
Implement reciprocal rank fusion, validate a reranking boundary and compare query-level retrieval results using a small reproducible Python lab.
Level 4Frontier
Recommended for you
Throughput, latency and batching: capacity planning for a model server
Build a tested batch queue simulator, compare burst and sparse arrivals, calculate timing metrics and design a measured model-server experiment.
Level 4Frontier
Recommended for you
Transformers: attention explained and implemented
Implement scaled attention, test causal visibility and assemble a small forward-only transformer block using standard-library Python.
Level 4Frontier
Recommended for you
Build a tiny language model from scratch
Train an original character-bigram language model with explicit gradients, held-out evaluation, bounded sampling and a reusable JSON checkpoint using standard-library Python.
Level 4Frontier
Recommended for you
Evaluate a RAG system with a grounded test set
Build a reproducible RAG evaluation fixture, score retrieval separately from claim support and completeness, and detect regressions hidden by a single headline number.
Level 4Frontier
Recommended for you
Model routing, caching and graceful fallbacks
Build a scoped response cache and bounded model router, test expiry and failure boundaries, and compare cost, latency and answer quality on a replayable trace.
Level 4Frontier
Recommended for you
Permission-aware retrieval and private knowledge bases
Build and test a small retrieval policy that separates tenants, checks document groups before ranking and refuses missing permission matches.
Level 4Frontier
Recommended for you
Rust: ownership, safety and a small command-line tool
Build a standard-library Rust command that reads bounded fictional token counts, validates records and reports totals, with ownership examples and executable tests.
Level 4Frontier
Recommended for you
Harness state and agent memory: checkpoints, retention and deletion
Build a small SQLite state store with versioned checkpoints, inspectable facts, expiry, tenant-scoped deletion and tested recovery using standard-library Python.
Level 4Frontier
Recommended for you
Knowledge graphs and structured retrieval
Build a small directed knowledge graph in Python, retrieve bounded evidence paths and test direction, cycles, missing facts and source traceability.
Level 4Frontier
Recommended for you
Threat-model an agent and its tool boundary
Map an agent tool boundary and test a local gateway for identity, ownership, exact-action approval, expiry, replay and stale writes using fictional notes.
Level 4Frontier
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Orchestrating multiple agents with explicit responsibilities
Build a tested Python coordinator with scripted research, draft and review roles, checked handoffs, dependency failures, bounded retries and a shared call budget.
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