Technical Debt

As the potential for a negative leap second approaches, a discussion explores the challenges of timekeeping, the risks to modern software, and the best practices for handling time in distributed systems.

This post examines the complex divide among software professionals regarding the use of AI in development, balancing the benefits of increased velocity against concerns about code quality, maintainability, and the future of the engineering craft.

Discover actionable advice on developing strong software architecture instincts, including recommended reading, practical project exercises, and how to critically evaluate AI-generated code.

Discover why human review of AI-generated code remains essential for preventing over-engineering, ensuring correctness, and maintaining system health, even with advanced LLMs.

Tired of firefighting and being criticized for building quality code? Discover industries and company cultures where meticulous engineering, thorough debugging, and robust systems are valued.

Explore the evolving landscape of software development as AI shapes code quality and junior developer skills. Uncover the challenges of AI-generated code and strategies for effective integration.

Explore how the rapid, iterative nature of AI-assisted coding can create a highly dopaminergic workflow. Learn to leverage its productivity boosts while mitigating risks like task paralysis, code sloppiness, and skill atrophy.

Explore how the spreadsheet revolution reshaped programming, offering vital insights into how AI might redefine professional roles and the future of software development. Discover the challenges and opportunities for programmers and end-users alike.

Despite compelling anecdotes, concrete studies showing AI's broad productivity gains in software development are rare. Explore the multifaceted challenges in measuring AI's true impact, from rapidly evolving tools to hidden costs like increased bugs and technical debt.

Explore how AI is changing software development, from the debate on code understanding to practical strategies for leveraging AI while maintaining quality. Discover why developing a strong mental model of your codebase and robust validation techniques are more critical than ever for architects and developers.