AI blogs

When I first opened the codebase, I just sat there for a minute. I'd been brought onto a project mid-stream — a freelance contract to add three new features to an existing platform that had been running in production for about six years. The platform worked. Customers were using it daily. Revenue depended on it. My job was, in theory, to add a customer segmentation feature, improve the reporting dashboard, and optimize a slow checkout flow. What I found when I actually opened the repository was a PHP monolith with no tests, inconsistent naming conventions across files that had clearly been written by four or five different developers over the years, business logic scattered between controllers and database queries and occasional JavaScript files that did things that should have happened server-side, and approximately zero documentation. The original developer had left the company two years earlier. Nobody currently at the company fully understood how the whole system fit together...

The way software gets built has changed more in the last two years than in the decade before it. AI coding tools have gone from novelty to daily habit for the majority of professional developers, and the gap between teams using them well and teams using them poorly is becoming one of the more visible performance differences in the industry. But "using AI" is not a strategy. It's a starting point. What actually separates developers who get meaningful productivity gains from those who mostly get faster ways to produce bugs is having a structured workflow — a repeatable sequence of stages where AI plays a defined role at each step, rather than just being something you reach for when you're stuck. This guide is that workflow. Ten steps, from initial idea to ongoing maintenance, with practical guidance on where AI helps, where it doesn't, and what to watch out for at every stage. Whether you're building your first side project or your tenth client application, fo...

It was close to midnight on a Thursday, and I had a feature due by 9 a.m. the next morning. The feature itself wasn't complicated — a discount calculation system for an e-commerce client, applying tiered pricing rules based on order quantity and customer membership level. I'd built things like this before. But the specific combination of rules this client needed was intricate enough that I was still working through the logic well after dinner. That's when I turned to my AI coding assistant. I described the problem clearly, included the business rules as the client had specified them, and got back a clean, well-commented function that seemed to handle every case. It was exactly what I would have written if I'd had another hour and hadn't been tired. Maybe better. I read through it once. It looked right. The logic followed naturally from the requirements, the variable names made sense, and the structure was clean. I dropped it into the codebase, ran the existing te...