AI blogs

Most React projects start small. A components folder, an App.jsx , a handful of pages. It's quick to set up, easy to navigate, and good enough for what the project is at that moment. Then the project grows. Features accumulate. The components folder fills up with files that have nothing to do with each other. API calls appear inside page components, then inside other components, then scattered everywhere. A utility function gets written twice because nobody remembered the first one existed. A new developer joins and spends half a day figuring out where things are. This is the common trajectory, and it's not the result of bad developers — it's the result of a structure that was appropriate for a small project and never got reconsidered as the project scaled. A good React project folder structure doesn't solve these problems automatically, but it makes them easier to avoid. More importantly, it makes the codebase easier to maintain, debug, and hand off — which is th...

React applications tend to feel fast when they're small. A handful of components, a few API calls, everything snappy. Then the application grows — more features, more state, more dependencies, more data — and what was once smooth starts feeling sluggish. This isn't necessarily a React problem. It's usually a product of specific, identifiable decisions that compound over time: components re-rendering more than they need to, JavaScript bundles that have grown without anyone auditing them, images served at full resolution regardless of the device requesting them, API calls that fire redundantly on every interaction. Each of these is fixable, but only if you know which one is actually causing the problem you're experiencing. That's the central principle behind React app performance optimization: measure first, optimize second. This guide covers the main categories of performance issues, how to measure them, and practical techniques for addressing them — along with gui...

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...