Code quality

What is an AI Code Generator? LLM Coding, Productivity, & Risk

What is an AI Code Generator? LLM Coding, Productivity, & Risk

AI code generators leverage LLMs to translate natural language into code, significantly boosting developer productivity and job satisfaction by automating boilerplate and accelerating learning. However, they introduce risks like subtle security vulnerabilities and require rigorous human review. Evaluating tools hinges on trust, demanding features like data provenance, governance, secure deployment, and curated training data for enterprise adoption.

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

This video explores AI code refactoring, differentiating between inline and autonomous agentic approaches. It highlights how AI can leverage pattern recognition for tasks like improving readability or reducing duplication, thereby addressing technical debt. A key focus is on the safety guardrails, detailing a multi-step, human-in-the-loop process involving planning, searching, reporting, human approval, patching, and verification through testing, ensuring AI-driven changes are safe for production and can integrate into CI/CD pipelines.

What Is AI Code Review? Fixing Slow PRs & Broken Workflows with AI

What Is AI Code Review? Fixing Slow PRs & Broken Workflows with AI

Anna Gutowska explains how AI code review enhances software development by addressing the slowness and inconsistency of traditional methods. The video delves into the benefits of AI in accelerating reviews, improving code quality, fostering developer learning, and reducing technical debt. It covers the underlying technologies like static/dynamic analysis and LLMs, discusses critical considerations such as over-reliance and context, and provides best practices for integrating AI while emphasizing the indispensable role of human oversight.

Reflections of AI: A Trilogy in 4 Parts • Rasmus Lystrøm • GOTO 2025

Reflections of AI: A Trilogy in 4 Parts • Rasmus Lystrøm • GOTO 2025

In a talk styled as "A Trilogy in Four Parts", Rasmus Lystrøm critically examines the real-world impact of Generative AI, debunking productivity myths and highlighting hidden costs like degraded code quality and environmental strain, while advocating for a return to solving real user problems with valuable, often simpler, technology.

It Ain't Broke: Why Software Fundamentals Matter More Than Ever — Matt Pocock, AI Hero @mattpocockuk

It Ain't Broke: Why Software Fundamentals Matter More Than Ever — Matt Pocock, AI Hero @mattpocockuk

AI coding tools are powerful but can quickly create unmanageable code if used without discipline. Matt Pocock argues that success with AI in software development comes not from delegating everything, but from applying decades-old engineering fundamentals like TDD, Domain-Driven Design, and creating deep, testable modules. The developer's role shifts from a tactical coder to a strategic system designer.

Handling AI-Generated Code: Challenges & Best Practices • Roman Zhukov & Damian Brady

Handling AI-Generated Code: Challenges & Best Practices • Roman Zhukov & Damian Brady

Roman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development, discussing its impact on developer workflows, code quality, security, and the future of developer roles. They emphasize that while AI tools are powerful amplifiers, human oversight remains essential for quality, security, and legal compliance.