Best-Practices

Introduction: Why Clean Code Still Matters in the AI Era We’re flooded with AI tools that promise to do the coding for us. But after experimenting with them, I noticed a recurring pattern: AI writes code in a vacuum. It doesn’t see the “messy” reality of my project three files away. I realized that the faster the AI generates code, the faster my codebase can turn into a nightmare if I’m not careful. This brought me back to the basics. Why did we start talking about Clean Code in the first place? As Uncle Bob famously noted in his book years before ChatGPT existed: ...

Why AI Automation Fails to Reach the Frontlines AI automation software ships at a breakneck pace, yet most industries outside software development aren’t experiencing the transformation. After conversations with people across manufacturing, and traditional business sectors, the pattern is clear: the gap between AI capability and actual adoption is substantial. Three core problems explain why. Digital Transformation Comes First Executives get excited about AI transformation (AX), but most organizations haven’t completed basic digital transformation (DX). AI needs data. If your business processes exist on paper, in people’s heads, or in disconnected silos, AI cannot assist your business. ...

Engineering Is The Art of Trade-offs Before diving into this, I should mention that my career isn’t particularly long yet. My thoughts on technology are still evolving, and they might be immature at times. But there’s one clear truth I’ve learned from facing various challenges in the field: there’s no “perfect answer” in software engineering. Trade-offs: More Than a Zero-Sum Game We often think of engineering trade-offs as simple opportunity costs. ...