Jeff Dean, Google's former Chief Scientist, gave a lecture that breaks down the full AI engineering stack from building LLMs from scratch to coordinating multiple AI agents. The talk covers practical ground — how to actually construct language models, then scale up to systems where one person manages a hundred agents working together. The value here is that it works whether you're brand new to machine learning or already shipping agent systems in production. The core argument is that most developers are taking the wrong approach to learning AI. Prompt engineering courses miss the real work. What actually matters in production is understanding orchestration (how to wire systems together), RAG (retrieval-augmented generation for grounding models in real data), evals (testing and measuring what works), context engineering (managing what information the model sees), and inference (actually running the thing efficiently). These are the skills that separate people who tinker from people who ship. The recommendation is to treat this as a serious weekend investment — something worth bookmarking and actually watching rather than scrolling through another tutorial. The implicit claim is that understanding this material could be more valuable than most things you'd learn in a given week, which is a high bar but reflects that this covers the gap between theoretical knowledge and what production systems actually need.