For engineers whose code AI can now write too
The thing that has changed in your job is not typing speed. It is that the visible, production-ready-looking part of the work is now generated, and the hard part has compounded: spec’ing carefully, reading critically, knowing when generated code is plausible-but-wrong, holding architecture in your head while a tool fills in the leaves. Our software-engineers track is built around those disciplines, with the tools that exercise them as the lever.
The tool training is concrete and hands-on – Claude Code, Cursor, GitHub Copilot, JetBrains Junie – in fast-track, deep-dive, and applied formats depending on how far your team has already gone. Pick the one your stack has settled on (or the one you suspect is coming), and book the format that matches your team’s stage.
Where the leverage compounds is the methods training next to the tools: TDD with AI as the implementer (so you stop merging plausible code that does not satisfy the requirement), spec-driven development as the layer that holds the work together across sessions, and AI software architecture for the review disciplines that catch the things tests do not.
The leverage is not in the keystrokes – it is in the methods sitting next to the tool, which is why this track pairs every tool course with the discipline that makes it pay off. The AI-Assisted Software Development path sequences the whole journey from tool to method; and if your code runs on hardware, the Embedded AI school curates the variants that do not pretend the deployment target is in the cloud.