What you will be able to do at the end

1. Explain, to a colleague, what an LLM is and how it produces text — without hand-waving.
2. Trace the whole history: rules → statistics → word vectors → transformers → LLMs, and say why each step happened.
3. Train a tiny language model on your own laptop, from scratch.
4. Fine-tune a small open model on your own data.
5. Build a question-answering app over your own documents (RAG).
6. Understand and control tokens, context windows, cost, and quality.
7. Build an agent that uses tools safely, and connect it to anything via MCP.
8. Add guardrails so a fooled model still can't do damage.
9. Wrap an agent in a real harness, run it for hours with an outer loop, measure its reliability (pass^k), and deploy it to production.
10. Use decision models for an agent's small, fast choices, and explain what world models (JEPA) are and why they matter.
11. Know what to read/watch next, and how to keep learning.