AI can produce a plausible change quickly, but shipping still requires evidence. My current minimum is: observable acceptance criteria, an explicit architecture boundary, a focused test that fails before the change, review of the actual diff, and release evidence from the production surface.
I am launching Vibe Coding Engineering on August 4 PT as a field guide to that workflow, based on real C++/Qt, QML, React, SvelteKit, AWS, payment, and delivery work.
Which gate catches the most AI-assisted coding failures for you and which gate do today s tools still skip?
A Korean-and-English field guide for controlling AI-proposed changes through planning, architecture, TDD, review, release gates, security, payment, delivery, and operations. Built from real work across C++/Qt, C++20, QML, React, SvelteKit, AWS, and production commerce systems. Includes free verified samples and paid PDF, EPUB, DOCX, checklist, templates, and team-license editions.
