Make a project you can explain.

Before you start

Complete the shared skills; choose a project scope that fits the available environment.

Each lesson follows the same rhythm: understand three small ideas, edit and run the code, then explain the result. Take one lesson at a time.

In your browser, JavaScript and local tools really execute against inspectable inputs. Model decisions and classroom records are authored examples. The optional SDK project runs separately on your computer with its own setup.

Your lesson sequence

1. 1Learn → build → reflectChoose a person and a small problemTurn an interest-pathway idea into a bounded task with a user, permitted inputs, and measurable success.
2. 2Learn → build → reflectBuild the smallest complete loopConnect the input, model or explicit workflow, allowed tools, evidence, and output into a reproducible prototype.
3. 3Learn → build → reflectTry to break your own resultCompare the prototype and baseline on held-out cases, misleading input, missing evidence, and resource failures.
4. 4Learn → build → reflectTell the story of what you builtDemonstrate the agent, justify its architecture, and explain the sources, checks, observed resource use, and limits. Actual API cost needs real usage records.

CONNECT THE FOUR LESSONS

Your course checkpoint

Give a candidate version one improvement and one regression. In the report exercise, remove a version field and keep an errored run in the input.

• Show why equal total scores can hide a new failure.
• Keep errors in the attempted-case count and missing metadata explicit.
• Use your notebook to explain one success, one failure, and the next experiment for a major-specific project.

Use these lessons’ experiment inputs and lab notebooks to record your evidence. This is a self-review prompt; only the lesson questions and code cases contribute to the progress shown above.