Tutorials

Beginner

Zero to a working MCP server and an agentic coding workflow.

1. 01 How to build your first MCP server, step by step (Python and TypeScript) Build a working Model Context Protocol server with tools and resources, connect it to a real client, and understand what actually happens on the wire. ⏱ 45–60 min
2. 02 How an AI agent actually works: build the loop behind every framework Every agent framework wraps the same ~40-line loop: model decides, tool runs, result feeds back. Build it in plain Python and the whole ecosystem stops being magic. ⏱ 30 min
3. 04 MCP vs. A2A vs. AG-UI: the agent protocol stack, explained with one diagram The three agent protocols solve three different problems — agent-to-tools, agent-to-agent, and agent-to-user. Learn which layer each owns and when you need more than one. ⏱ 20 min read
4. 07 Claude Code from zero: an agentic coding workflow, step by step Install Claude Code, learn the core loop, and build up to a disciplined agentic workflow: CLAUDE.md, plan mode, custom skills, and MCP servers. ⏱ 40 min

Builder

Orchestration, local serving, memory — the systems behind real agents.

1. 03 Multi-agent orchestration explained: build an orchestrator and sub-agents from scratch Build the agent harness pattern in ~80 lines of plain Python — an orchestrator that plans, delegates to specialist sub-agents, and synthesizes — with a mock model so you can run it instantly. ⏱ 50 min
2. 08 Agent memory patterns: vector stores, knowledge graphs, and context engineering Why agents forget, the four memory patterns that fix it, and a working retrieval loop you can run in plain Python before you buy a vector database. ⏱ 40 min
3. 11 How to evaluate an AI agent: build a golden-task eval harness from scratch Agents without evals break silently on every prompt tweak. Build a small golden-task harness in plain Python — tasks, checks, scoring, and a regression gate you can run in CI. ⏱ 45 min
4. 12 How to wrap a REST API in an MCP server (without handing the agent the whole API) Turn any REST API into agent-safe MCP tools: a narrow allowlist instead of a proxy, PII redaction before the model sees data, and a fake API so you can test the whole thing offline. ⏱ 45 min
5. 13 How to build a personal AI agent for daily productivity (note and task triage) Build an agent that triages your notes into tasks, references, and archives — runnable today with zero API keys, upgradeable to any LLM, and scalable from personal to team use. ⏱ 40 min
6. 15 Structured outputs for agents: make models emit JSON you can actually trust Agents break where prose meets code. Build the validate-and-retry loop that turns flaky model JSON into a reliable contract — in plain Python, runnable without an API key. ⏱ 35 min

Enterprise

Governance, audit, and cost control for agents in regulated production.

In the pipeline — subscribe below to get it when it ships.