Full Curriculum

What you'll learn

Seven modules. Four projects. A complete arc from first prompt to deployed application — built on a workflow you'll reuse for every project after this one.

6 Modules + Capstone

4 Hands-on Projects

18 hr of content

Module 0 Lecture 1 — Why I Built This Course Module 0, L0.1 · Why I Built This Course

Peter explains why he built this course as a maker — not as a software engineer. The lecture covers the gap between having an idea and shipping software, why AI coding assistants change that equation for hardware makers and solopreneurs, and what you will have built by the end of the course.

Module 1 Lecture 3 — The Core Skill: Framing a Problem for an AI Module 1, L1.3 · The Core Skill: Framing a Problem for an AI

Framing is the core skill of AI-assisted coding. This lecture introduces the four-element structure — task, context, constraints, and output format — that turns a vague request into a specification the AI can act on. A well-framed prompt reduces correction cycles; a poorly framed one produces plausible-looking output built on wrong assumptions.

M00

Setup and Orientation

~60 min Volatile

Get your tools installed and your mental model oriented. This module answers the "what is this, really?" questions before you build anything.

L0.1 Why I Built This Course: A Maker's Perspective 8 min

L0.2 What You'll Build, What You'll Need, What You Won't Need 6 min

L0.3 What AI Coding Assistants Can (and Can't) Do 10 min

L0.4 AI Tools and LLM Landscape 12 min

L0.4.1 Frontier Models and the Rest (bonus) 8 min

L0.5 Install the Default Stack 15 min

L0.6 From Maker Itch to Product 8 min

M01

AI Coding Fundamentals

~1 hr Stable

The mental model you'll carry through the entire course: how AI-assisted coding actually works, a five-level framework for thinking about it, and the core skill of framing problems correctly.

L1.1 What AI-Assisted Coding Actually Is 8 min

L1.2 The Five Levels of AI-Assisted Coding 10 min

L1.3 The Core Skill: Framing a Problem for an AI 10 min

L1.3.1 Use AI to Create a Prompt (bonus) 6 min

L1.4 Known Failure Modes 10 min

Project P1

RC Filter Analyser

A browser tool that plots RC circuit frequency response. Built in Claude with no editor, no install. Your first Level 3–4 build.

HTMLCanvas APILevel 3–4

M02

AI Tools and Models

~1.5 hr Volatile

A tour of the current AI coding toolchain: editors, assistants, and models. This module is re-recorded annually because the tools change fast.

L2.1 The Editor: VS Code as the Default 10 min

L2.2 Coding Assistants: Cline 15 min

L2.3 Coding Assistants: Claude Code 15 min

L2.4 LLMs as a Separate Concern 12 min

L2.5 Choosing Data Sources 10 min

L2.6 Local vs Cloud: The Real Trade-offs 12 min

M03

The AI-Assisted Workflow

~2 hr Stable

The complete workflow from idea to deployed application: brainstorming, specification, implementation, testing, iteration, documentation, and deployment.

L3.1 Brainstorming and Idea Validation with AI 10 min

L3.2 Planning and Specification 12 min

L3.3 Implementation Patterns 12 min

L3.4 Testing 10 min

L3.5 Iteration and Refactoring 10 min

L3.6 Documenting for Future-You 8 min

L3.7 Deploying for Makers 10 min

L3.8 AI for the Non-Code Business Layer 10 min

Project P2

Arduino Serial Plotter

A live serial data plotter that reads multiple channels from an Arduino, streams them to a web chart, and logs everything to CSV.

PythonFlaskChart.jsLevel 4

M04

AI in Your IDE

~1.5 hr Semi-stable

Putting an LLM inside your application. API basics, structured output, RAG for document intelligence, and the practical constraints around cost and latency.

L4.1 When to Put an LLM at Runtime, and When Not To 10 min

L4.2 API Basics: Requests, Streaming, Structured Output 15 min

L4.3 RAG: When It Earns Its Complexity 12 min

L4.4 Preparing Documents for LLM Ingestion 10 min

L4.5 Cost, Latency, and Graceful Failure 10 min

Project P3

Datasheet Q&A Tool

Upload a component datasheet, ask natural-language questions, get cited answers. Your first AI-powered application using the RAG pattern.

PythonRAGPDFLLM APILevel 4–5

M05

Ship a Real Project

~3–4 hr Stable

The capstone module. Build a complete multi-user web application with a full-stack agent: database, auth, background jobs, external API, and email notifications.

L5.1 Capstone Orientation: What We're Building 8 min

L5.2 Multi-user Application Design 12 min

L5.3 Database Design and the ORM 12 min

L5.4 Building a Multi-file Project with an Agent 15 min

L5.5 External APIs and Background Jobs 12 min

L5.6 Finishing and Shipping the Capstone 15 min

L5.7 Designing for Multiple Users (solopreneur) 10 min

L5.8 The Solopreneur Roadmap (bonus) 10 min

Project P4

Makerspace Booking System (Capstone)

A full makerspace equipment booking app: multi-user auth, PostgreSQL, role-based access, conflict detection, public holiday integration, and APScheduler email reminders.

PythonPostgreSQLFlaskAuthLevel 5