Direct AI coding agents to build machine learning tools in Rust, and practice the one habit that makes that safe: the agent proposes, the compiler checks that it builds, and you verify the result by running it. Across the courses you clean and validate data with Polars, bind claims to checked terminology, verify agent changes with tests, coverage and quality gates in CI, and run parallel subagents behind receipts and quorums that refuse a bad result. None of this makes an agent correct; it makes a wrong result land somewhere visible before anyone believes it.
Applied Learning Project
Every project is a Rust program with pinned inputs, machine-checked assertions and a receipt: a bronze, silver and gold medallion pipeline that ends in verified Parquet, a SHACL shape that refuses an unchecked claim, a verification ladder from formatting to continuous integration, and a fail-closed parity sweep in which one failing reviewer blocks the merge. You build each one by directing an agent, then prove it by running it.
