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
Hello Developers! 👋 Most developers today pick a side: Let’s talk about combining C++ and JavaScript—the ultimate hybrid stack for high-performance applications. 👇 1. The Core Engine (C++) ⚙️ 2. The Browser Bridge (WebAssembly) 🌉 3. The Cinematic Experience (Vanilla JS + UI/UX) ✨ The Takeaway 🎯 Keep optimizing, keep building! 💻✨ ~ Ujjwal Sharma | @stackbyujjwal About the Author 👨💻 Ujjwal
I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode
Hash tables feel like the default choice for membership tests. std::unordered_set promises average O(1) lookup, so we reach for it automatically. In performance-sensitive C++ code, that habit can cost you an order of magnitude. I ran into this while building a Vamana graph index for approximate nearest neighbor search. The algorithm needs to track visited nodes. Node ids are dense integers, and th