AI

AI-assisted development needs proof that matches production. Repo Testing Setup creates a repeatable, containerized local gate so code is tested the same way it will be shipped.

AI agents often prove the wrong thing. Feature Design Pre-flight turns requirements into auditable outcomes, mapped impacts, and test plans before implementation starts.

Most bad AI builds start with ambiguity. Clarify Before Build forces the conversation to slow down long enough to turn vague requests into a shared, written contract before implementation starts.

Large language models can finally take on much of the software development lifecycle. But shipping secure software still demands testing, threat modeling, logging, input validation, and vulnerability management. Here's how to think about closing that gap.

What if AI systems did not just create code, but continuously fixed, secured, and improved it? A practical thesis for autonomous software quality and cybersecurity.

The real promise of AI software development is not just faster code. It is a system of skills that can keep quality, security, vulnerability management, and technical debt under control as the codebase changes.