Explores why top engineers prioritize discipline over speed in AI-driven software development, emphasizing careful code over rapid generation.
How to improve AI agent feedback loops using custom linters to enforce coding standards automatically.
Explores the growing divide between AI enthusiasts and skeptics in tech, highlighting the risks of uncritical adoption and communication breakdowns.
Analysis of the growing divide between AI enthusiasts and skeptics in tech, exploring their conflicting views on AI's impact on software development.
Practical habits for using AI effectively as a software engineer, focusing on fundamentals, context, and understanding.
A guide on shifting left in DevOps with automated checks before merging code to prevent bad code and ensure quality.
An analysis of how AI is transforming software development, shifting focus from building to planning and judgment.
Tips to make AI code review effective by focusing on small MRs and understanding its purpose.
Jump open sources 14 opinionated Credo checks for Elixir to improve code quality and catch common mistakes in production and test code.
A developer argues that writing code by hand first, then using AI for maintenance, is more effective than AI generating initial drafts.
Explores whether CSS can be considered 'wrong', arguing that working code is valid and changeable.
Article discusses the true purpose of code review beyond bug-finding, emphasizing judgment, communication, and codebase health.
A quote from John Carmack on the pitfalls of over-architecting software for future needs.
Argues that AI coding agents should be used to improve code quality and reduce technical debt, not just speed up development.
Argues that AI coding agents can help developers produce higher quality code and reduce technical debt by automating tedious refactoring tasks.
Explains 5 essential architecture tests for .NET projects to enforce design rules and prevent technical debt using tools like ArchUnitNET.
Argues for rigorous scientific studies on the impact and risks of using LLMs in software development, highlighting current lack of impartial research.
A developer shares critical drawbacks of using Claude Code for AI-assisted programming, focusing on hidden issues like problematic test generation and maintenance challenges.
A critical analysis of why LLMs like Claude Code struggle with complex coding tasks, arguing the real issue is not intelligence but poor software engineering practices.
Explains the three key growth curves—exponential, linear, and logarithmic—that define a scalable software business and an engineer's role in building long-term assets.
