This article discusses the challenges of using AI to generate code for distributed systems, emphasizing that traditional coding practices can lead to bugs that are hard to catch. It argues for frameworks like Hydro that make distributed behavior explicit and aim to reduce these bugs by design, rather than relying solely on testing.

This article discusses the benefits and challenges of using AI in programming from the perspective of a senior engineer. It shares practical tips and personal insights on how to effectively integrate AI tools into workflows while addressing common concerns about code quality and understanding.