Modules¶
Sixteen modules, in reading order. Every module has the same layers: Foundations (no background assumed), In practice (what working engineers do), and Going deeper (research and open debates), plus exercises and discussion questions. The learning paths say which layers to read for your background.
| # | Module | What you'll learn |
|---|---|---|
| 00 | Orientation | What this is, what "vibe coding" means here, six ideas that outlast the hype |
| 01 | What is an LLM? | Tokens, transformers ("Attention Is All You Need"), pretraining, reasoning models, hallucination |
| 02 | Training, tuning, and self-tuning | How models are made, fine-tuning open weights with LoRA, distillation, models that update their own weights |
| 03 | The model landscape | The labs, current lineups, where Meta's Muse fits, the best model for each job |
| 04 | Open weight vs closed weight | Licenses, running models locally, where open models sit in the chain |
| 05 | How to tell if a model is good | Benchmarks and their flaws, benchmark-maxing, productivity studies, building your own eval |
| 06 | Harnesses, tools, and MCP | Chat vs IDE vs CLI vs cloud agents, MCP, skills, hooks, products like Claude Design |
| 07 | Context engineering and the buzzwords | Prompt → context → harness → loop → graph engineering, and the rise and fall of giant CLAUDE.md files |
| 08 | Agentic workflows | The workflow that works, Yegge's eight stages, how much autonomy is real today |
| 09 | Multiple agents | Agents building and reviewing together, and what the research says |
| 10 | AI code review | Review tools, cross-model review, how humans should review AI-written code |
| 11 | Security and prompt injection | Why injection is unsolved, the lethal trifecta, real incidents, sandboxing |
| 12 | Costs | Subscriptions vs API billing, why agents eat tokens, a worked cost example |
| 13 | Open-source ecosystem | Open harnesses, inference engines, frameworks, and the AI-slop problem for maintainers |
| 14 | Keeping up | Model ups and downs, which sources to trust, a sustainable routine |
| 15 | Learning, careers, and responsibility | Learning with AI without losing the skill, careers, privacy, licensing, ethics |
Each module has a matching slide deck.