AI

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.

AI is quickly integrating into education, with major releases from Anthropic (Claude) and Gemini (Google). However, OpenAI's contribution is pretty underwhelming. I call it the lack of the Actionable Outcomes Bias Principle.

Your guide to all terms AI and AI companies

Read about how the AI ecosystem has finally started to take its power problem seriously.