AI blogs

Debugging is where development time quietly disappears. A feature might take four hours to build. Debugging a subtle bug in that feature might take another three. And that ratio — more time finding and fixing problems than building — isn't unusual for developers working on real-world applications with non-trivial complexity. I'd been using AI to help with debugging for a while, but always informally — paste an error when I was stuck, see what it said, evaluate whether it helped. I didn't have a clear sense of how reliable it actually was across different categories of bugs, or where it was genuinely useful versus where it gave me plausible-sounding nonsense. So I ran an experiment. I collected 100 real bugs from my own projects and client work — nothing invented, nothing artificial — and let AI take the first shot at every one of them. I tracked the results, categorized where it succeeded and where it failed, and paid attention to the patterns. The rules were simple: AI...

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...