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.
Explore how developers are truly integrating AI tools, from battling 'AI slop' to harnessing them for specific tasks like code snippets and test generation. Learn practical tips for effective AI use while maintaining your workflow.
Discover a range of innovative projects built using 'vibe coding' with AI tools like ChatGPT and Claude Code. Learn practical tips for rapid development, from managing AI-generated code to architectural insights.
For software engineers, understanding when to build from scratch and when to use AI is crucial. This article provides a strategic approach to integrating AI tools while mastering core development skills to build robust and maintainable projects.
Despite soaring claims of 10x to 1000x productivity boosts in software development from advanced AI, real-world evidence remains scarce. Explore why many are skeptical of 'PhD-level' AI and how economic structures might impact actual productivity gains.
Facing frustration with AI code agents introducing unwanted changes during refactoring? Discover proven strategies like granular task breaking, precise positive prompting, and iterative review to keep your LLM agents focused and effective.
Explore why AI coding agents often falter with basic front-end layout and CSS challenges, and discover how leveraging specific models and component-based frameworks can dramatically improve their UI generation capabilities. Learn practical tips for maximizing AI's effectiveness in your front-end projects.
Discover the best strategies for finding free AI coding assistants. Learn about the trade-offs between cloud services with limited free tiers and the powerful, cost-effective alternative of running models locally.
An analysis of why many skilled software developers are hesitant to adopt AI coding assistants, citing concerns over code quality, long-term maintainability, and the nature of their work.