The Idea After deciding to build an iOS app using AI, the first thing I set out to create was a metronome app designed for dark stage environments. Back in college, I played drums — and while that was a while ago, there weren’t many metronome apps that felt both clean and professional. (Turns out, that’s still true today.) That’s what led me to the idea: a simple, black-and-white metronome where
I have a confession: I'm a productivity app addict. Notion, Todoist, Things, TickTick, Bear, Obsidian — I've tried them all. And every single one failed me in the same way. Not because they were bad apps. But because they let me add unlimited tasks. So I'd wake up Monday morning, open my to-do app, and see 47 items staring back at me. By 9am I was already paralyzed. Decision fatigue is real. When
I'm going to give you the comparison I couldn't find when I was choosing. Most "Claude Code vs Cursor" articles are either vibe-based or benchmarks that don't match solo indie dev workflows. I wanted something grounded in an actual multi-product project: 4 iOS apps, 5 distribution surfaces, 11 public repos, CI/CD across all of them. So I spent 14 days building exactly that — exclusively with Claud
It's a one-line item on the roadmap. "Send a push notification when X happens." Estimate is two days, three if the backend doesn't have FCM credentials yet. There's a library for it. The library is the visible part. The other 90% is platform lifecycle, registration state machines, race conditions with navigation, payload archaeology, and a half-dozen iOS and Android quirks. Nobody writes them down
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
Mobile tests are where the bugs actually live. A signup flow that works on an iPhone 15 falls apart on a lower-end Android because the keyboard pushes a button off-screen. A push notification mid-flow leaves the app in a state nothing else reproduces. Memory pressure on a four-year-old Android does things you can't make a simulator do. I wrote simulator-only tests anyway, for years. Real-device ru
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about