Most AI Talk Is Opinion. This Is Practice.
Courses built from my own AI learning, in public.
LangChain & Agent Building
A linear, one-concept-per-lesson path through LangChain and agent building, from your first API call to a local research agent.
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LangGraph
A linear, one-concept-per-lesson path through LangGraph, the graph engine LangChain agents are built on, from your first graph to a multi-agent research assistant.
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LangSmith
A linear, one-concept-per-lesson path through LangSmith: tracing, evaluating, and monitoring the agents built in the LangChain and LangGraph courses, in production.
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MCP
A linear, one-concept-per-lesson path through the Model Context Protocol (MCP): building MCP servers first (tools, resources, prompts), then MCP clients, then wiring MCP tools into a real LangChain/Gemini agent.
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pgvector
A linear, one-concept-per-lesson path through pgvector, the Postgres extension that turns an ordinary relational database into a place to store and search embeddings, from your first vector column to a production-shaped FastAPI RAG service.
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Pydantic AI
A linear, one-concept-per-lesson path through Pydantic AI: a type-safe agent framework from the Pydantic team, where an agent's output is a validated Pydantic model, not a string you hope is JSON.
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pggraph
A linear, one-concept-per-lesson path through pggraph, the Postgres extension that compiles graph traversal and GQL/Cypher pattern matching on top of ordinary Postgres tables, from registering your first table as a node to a relationship-aware context API for AI agents.
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Redis
A linear, one-concept-per-lesson path through Redis, the in-memory store agents lean on for fast, ephemeral state, from your first client connection to a multi-agent system using Redis for memory, queue, and cache.
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