Artesanal coding emphasizes the importance of craftsmanship in software development amidst the rise of AI and “vibe coding.” It advocates for intentional, quality-driven coding practices that foster deep understanding and connection to the code. By balancing AI assistance with craftsmanship, developers can preserve their skills and create sustainable, high-quality software.
Month: October 2025
Brainrot and the Slow Death of Code
The rise of AI tools in software development is leading to a decline in genuine coding skills, as developers increasingly rely on automation. This reliance dampens critical thinking and creativity, replacing depth with superficial efficiency. Ultimately, the industry risks producing inferior code devoid of understanding, undermining the essence of craftsmanship in programming.
The Art of Reusability and Why AI Still Doesn’t Understand It
AI can generate code but lacks understanding of design intent, making it struggle with reusability. True reusability involves encoding shared ideas and understanding context, which AI cannot grasp. This leads to overgeneralized or underabstracted code. Effective engineering requires human judgment and foresight that AI is currently incapable of providing.
The AI Detox Movement: Why Engineers Are Taking Back Their Code
In 2025, AI tools transformed coding but led developers to struggle with debugging and understanding their code. This sparked the concept of “AI detox,” a period where developers intentionally stop using AI to regain coding intuition and problem-solving skills. A structured detox can improve comprehension, debugging, and creativity, fostering a healthier relationship with AI.
When 200,000 Lines of AI Code Disappeared and Nothing Broke
A team deleted 200,000 lines of AI-generated code yet maintained app functionality, highlighting the pitfalls of unchecked AI development. AI may accelerate chaos in weak systems, making existing issues worse. Effective engineering culture remains crucial; AI should enhance rather than replace human judgment in creating a quality codebase.
Why AI Can’t (Yet) Write Maintainable Software
In the past few years, large language models (LLMs) have burst onto the software development scene like a meteor bright, exciting, and full of promise. They can write entire applications in seconds, generate boilerplate code with ease, and explain complex algorithms in plain English. It’s hard not to be impressed. But after spending serious time…
Returning to the Rails World: What’s New and Exciting in Rails 8 and Ruby 3.3+
It’s 2025, and coming back to Ruby on Rails feels like stepping into a familiar city only to find new skyscrapers, electric trams, and an upgraded skyline.The framework that once defined web development simplicity has reinvented itself once again. If you’ve been away for a couple of years, you might remember Rails 6 or early…
What You Should Learn to Master but Never Ship
Every engineer should build a few things from scratch search, auth, caching just to understand how much complexity lives beneath the surface. But the real skill isn’t rolling your own; it’s knowing when not to. In the age of AI, understanding how things work under the hood isn’t optional it’s how you keep control over what your tools are actually doing.
AI Vibe Coding vs. Outsourcing vs. Local Developers. What Really Works Best
The way we build software is changing fast.You can now code alongside AI in real time. You can hire an offshore team across time zones. Or you can build with local developers right next to you the old-school way that suddenly feels new again. Each model works, but they work differently. And when it comes…
?? → BI → ML → AI → ??
AI’s past and the future Where acronyms in business come from, what they sold, who won, and what might come after “AI” Acronyms are the currency of business storytelling. They compress complex technology into a neat package a salesperson can pitch in a single slide: CRM, ERP, BI, ML, AI. Each one marked a shift…