The 2-hour practice lab for AI engineers.
Hands-on labs for the patterns that make AI products actually work. Theta is the AI teacher across the curriculum. Each lab has its own AI TA — a focused expert for that domain. You write your version, the TA grades it, and a real model runs through what you built. No videos. No fluff.
Browse the labs.
Hands-on labs anchored on a working principle from someone who has actually shipped agents to production. Each lab has its own AI TA.
Harness
“An LLM running tools in a loop. That's the whole field, in one sentence.”
— Simon Willison
The agent loop, tools, and the safety wrapping around an LLM.
Evals
“Evals before agents.”
— Andrew Ng
Rubrics, LLM judges, error analysis, and CI gates for AI output.
Context
“It's context engineering, not prompt engineering.”
— Andrej Karpathy
The window is the program. RAG, embeddings, chunking, hybrid search.
MCP
“MCP is USB-C for AI agents.”
— Anthropic
JSON-RPC, tools, resources, prompts — and the seven sins that break production.
Fine-tune
“Most fine-tuning problems are prompting problems in disguise.”
— Forge
When to fine-tune, when not to — and how to actually train a small open-weight model with LoRA.
Claude Code
“Senior engineers don't type first. Neither should the model.”
— Pair
Plan mode, slash commands, CLAUDE.md, subagents, hooks, skills. The harness Anthropic shipped for engineers, taught at engineering speed.
Voice
“Voice is a real-time relay race, not a request-response.”
— Echo
VAD, STT, the realtime model, barge-in, telephony, eval. The whole stack for shipping a voice agent that doesn't sound like 2009.
Generative UI
“LLMs don't return UIs. They return intents. The runtime returns the UI.”
— Canvas
Component contracts, streaming hydration, WebMCP (W3C Draft), Google A2UI (shipped Dec 2025), MCP Apps. Where chatbots become apps.
Frequently asked questions
Theta is your AI teacher across the whole curriculum. In machine learning, θ is the symbol for the parameters being learned during training — so we named the teacher after the thing being shaped. Each lab also has its own AI TA (Loop for agents, Cal for evaluation, Verba for context, Wire for protocols). They're focused on their domain; Theta keeps the through-line.
Two hours, designed for one focused sitting. The progress saves to your browser, so you can split it across two sessions if you have to — but the lab is shaped for one go.
Yes — these labs assume you're an engineer. They don't teach Python or Git. They teach the specific patterns of building AI products: agent loops, evals, RAG, MCP. If you've shipped any production code, you're qualified.
Two things. First, format: every lab is two hours, finishable in one sitting, with a clear artifact at the end. Second, AI-graded worksheets: before you read the canonical answer, you write your own, and your lab's AI TA reads what you wrote and gives specific feedback. No videos. No multiple choice.
No. The artifact is the certificate — you finish each lab having built a working thing you understood. If your employer wants proof, share the lab URL plus what you built.
Free during the launch period. Future labs may be paid, but everything currently shipped stays free.
New labs ship roughly once a month. The next ones in the queue are Tools, Production, Safety, and Multi-agent. Theta stays the teacher; each new lab gets its own AI TA.
An independent engineering studio — Theta Studio. The curriculum is anchored on canonical sources (Karpathy, Anthropic, Ng, Husain, Willison) and built with the discipline you'd want in production systems, not in vibe-y demos.