AI-Assisted Development
Rolling out AI-assisted development across a whole engineering team — task decomposition, PR and review process, quality standards, and what actually changes when the tools land.
Mobile Apps That Actually Ship: How AI Changes the Development Economics
Mobile development has always been expensive because it required separate teams for iOS and Android. Cross-platform frameworks changed one side of that equation. AI-assisted development changes the other — but only if you don't skip the engineering discipline.
Building a Mobile App in 2026: What AI-Assisted React Native Development Actually Delivers
AI-assisted development has changed the economics of cross-platform mobile — not by removing the engineering discipline, but by shifting where the hours go. Here's what that looks like on a real project.
The Hidden Math Behind Your LLM API Bill
Most teams building LLM features don't notice the cost problem until the invoice arrives. Token budgets, prompt bloat, and naive integration patterns can turn a useful AI feature into an expensive mess — here's what to look at first.
The Faster AI Writes Your Code, the More You Need End-to-End Tests
Vibe coding multiplies the code your team ships while shrinking how deeply humans review it. End-to-end tests are the one safety net that doesn't care who wrote the code — here's why they matter more now, and how to build a suite that runs on every PR.
AI-Assisted Development at Team Scale: What Works, What Doesn't, and How to Roll It Out
“Yes, kind of” is the default state of AI adoption in most engineering teams. The gap between a few individuals using AI and a whole team using it well is wider than it looks — here’s how to cross it.
Rolling Out AI-Assisted Development: What Engineering Teams Get Wrong in the First Six Weeks
The AI coding tools are genuinely good now. Getting two enthusiasts productive is easy; getting a whole engineering team there is where rollouts go wrong. Here are the five most common mistakes.
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