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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
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
Everyone starts here, and it is a great place to start.

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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.

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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.

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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.

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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.

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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.

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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.

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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
You have the basics. Time to get properly good.

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Use AI images and video responsibly: ownership in the UK, EU and US, tool terms, consent, labelling, Content Credentials, quality checks and simple records.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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
You are ready to build things that work.

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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.

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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.

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Build an offline AI evaluation harness with test cases, a rubric, regression comparisons, uncertainty checks and a reproducible release record.

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Build a local document pipeline: clean UTF-8 text, create traceable chunks, test boundaries and measure which evidence survives together.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Add behaviour to the event page with plain JavaScript: the DOM, events and delegation, one state object, form validation, focus, status messages and localStorage.

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Build a local read-only HTTP API with Node.js: explicit routes, strict query validation, predictable errors and executable tests using built-in modules.

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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.

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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.

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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.

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Build a standard-library Go HTTP service with bounded workers, ordered results, input limits, cancellation and executable tests.

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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.

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Build a typed reading-list boundary with runtime validation, explicit display states, compiler checks and executable tests.

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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.

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Test an agent loop locally: enforce budgets, replay model decisions, reject late results and verify bounded retries without paying for model calls.
Level 4
You are at the edge. Let's go deeper.

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How inference engines run a model: memory maths, quantisation, prefill and decode, batching, hardware, and serving a model safely over a local API.

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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.

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A ten-step method for judging a new model: primary sources, licence, architecture, hardware, benchmarks, your own tests, safety, jurisdiction and a decision record.

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Implement reciprocal rank fusion, validate a reranking boundary and compare query-level retrieval results using a small reproducible Python lab.

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Build a tested batch queue simulator, compare burst and sparse arrivals, calculate timing metrics and design a measured model-server experiment.

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Implement scaled attention, test causal visibility and assemble a small forward-only transformer block using standard-library Python.

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Train an original character-bigram language model with explicit gradients, held-out evaluation, bounded sampling and a reusable JSON checkpoint using standard-library Python.

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Build a reproducible RAG evaluation fixture, score retrieval separately from claim support and completeness, and detect regressions hidden by a single headline number.

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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.

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Build and test a small retrieval policy that separates tenants, checks document groups before ranking and refuses missing permission matches.

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Build a standard-library Rust command that reads bounded fictional token counts, validates records and reports totals, with ownership examples and executable tests.

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Build a small SQLite state store with versioned checkpoints, inspectable facts, expiry, tenant-scoped deletion and tested recovery using standard-library Python.

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Build a small directed knowledge graph in Python, retrieve bounded evidence paths and test direction, cycles, missing facts and source traceability.

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Map an agent tool boundary and test a local gateway for identity, ownership, exact-action approval, expiry, replay and stale writes using fictional notes.

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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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