LLMs are changing how we code, introducing significant waiting times. Discover practical strategies from experienced developers on how to stay productive and focused while your AI writes code, from parallel tasking to creative side projects.
Discover practical strategies for tackling colossal, AI-generated "vibe-coded" pull requests, from effective rejection tactics to collaborative review methods. Learn how to maintain code quality and developer sanity amidst the push for AI-driven velocity.
A Hacker News discussion explores whether a programming language designed specifically for AI generation could improve code reliability by emphasizing explicitness, and how this interacts with LLM limitations, training data needs, and human usability.
Developers discuss how much credit to take for code written with LLMs, debating attribution, responsibility, copyright, and the evolving nature of authorship in software development.
Developers share their real-world experiences with AI coding assistants, discussing benefits like speed and learning, alongside crucial caveats such as verification and potential pitfalls. Discover how to leverage AI effectively in your workflow.