Courses
We don't run courses ourselves. Instead we hand-pick useful external courses and learning resources for each level — from a calm first introduction to deep technical depth. Every link below is free, starts free, or can be audited for free, and we check that it is still relevant today.
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Anthropic AcademyMCP: Build Rich-Context AI Apps with Anthropic
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.
DeepLearning.AI · LlamaIndexBuilding Agentic RAG with LlamaIndex
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.
DeepLearning.AI · LangChainLong-Term Agentic Memory with LangGraph
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.
Fast.aiPractical Deep Learning for Coders
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.
Also on AI Expert
Our own videos and articles on this topic — a shorter read or watch alongside the external courses above.
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Andrej Karpathy: Software Is Changing (Again)
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Deep Dive into LLMs like ChatGPT
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The 2026 LLM stack: models, inference, tooling, and trade-offs
A working architect's view of the 2026 LLM stack — the model tiers, inference providers, orchestration layers, evaluation tooling, and the trade-offs that actually matter when shipping production AI. Everything you wish someone had laid out before you started.
Use the article as decision context for adoption, risk, governance, or investment choices.
These links point to third-party learning providers and official resources. We have no commercial relationship with them — we recommend each one because it fills a clear learning need.