Code Generation

Explore how developers are leveraging AI for coding, from full codebase access in production-like environments to specific model choices, while debating the true impact of "10x" productivity and the importance of tool stability. Discover practical tips and future needs for AI in software development workflows.

Explore a deep dive into whether agentic coding truly delivers net-positive results, featuring real-world experiences, practical tips for success, and a candid look at its limitations and the evolving role of developers.

Explore how AI coding impacts COBOL development, from strict formatting and compliance hurdles to its unexpected utility as a productivity tool. Discover strategies for leveraging LLMs effectively in legacy systems.

Explore how traditional diffs struggle with AI-generated code changes and discover new strategies for effective review. Learn about semantic diffing tools and snapshot comparisons to understand meaningful code evolution beyond line-level changes.

Discover how developers are leveraging AI to boost productivity, automate code generation, and ensure quality through advanced testing strategies and high-level specification. Explore tips for rapid prototyping and adopting new tech stacks with AI assistance.

Explore how modern AI models interpret and discuss "vibe coding," a new paradigm for development where LLMs take the lead. Discover tips for effective prompt engineering to get the most out of your AI assistant.

Explore what 'bad quality code' means when generated by LLMs and discover practical strategies to ensure consistency, maintainability, and architectural integrity in your AI-assisted development workflow.

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.