Courses

Ninety minutes to a year's worth of intuition. If you've started writing code that calls an LLM — or you're about to — this is the most efficient course online for closing the gap between "playing with ChatGPT" and "shipping a feature that calls an LLM."

MCP is the protocol that's quietly replacing one-off tool integrations across the AI tooling ecosystem. Learn it from the source. By the end you'll have built and deployed your own MCP server, connected an LLM client to it, and understood why this standard is the closest thing the field has to USB-C.

Naive RAG (vector search → stuff into prompt) is the version that ships first and disappoints fastest. This short course shows you the upgrade: an agent that plans retrieval, picks tools, compares sources, and answers multi-document questions. Two hours, immediately applicable to a real product.

The clearest open-source treatment of agentic systems available. Anchored in the three frameworks engineers actually evaluate (smolagents, LlamaIndex, LangGraph) rather than one vendor's stack. Concludes with a benchmark assignment and public leaderboard — accountability your team can verify.

The context-window-amnesia problem is what makes most agents feel forgetful and dumb on day two. This course teaches the LangMem patterns that fix it — episodic, procedural, and semantic memory — so an agent remembers user preferences and past interactions across sessions without bloating the prompt.

Selected for its top-down pedagogy. Most ML courses spend three weeks on calculus before you train anything; Fast.ai has you classifying images in the first lesson and peels back the layers from there. Recommended for developers who want to write PyTorch and debug live models — not study abstract maths first.