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
Most TypeScript teams shopping for an agent framework don't need one. A single generateObject call covers classification, extraction, summarization, tagging — the 80% case for production LLM work in TS right now. But once the model starts deciding what to do next, surviving deploys, or coordinating with other agents, you start shopping. And the moment you do, you discover the TS agent ecosystem is
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
A production-grade embedded system enabling communication across speech, text, Morse, and haptic signals within a single unified pipeline. Official Project Page: https://anandps.in/projects/unified-assistive-communication-system GitHub Repository: https://github.com/anand-ps/unified-assistive-communication-system Problem Assistive communication systems are fragmented. Most tools so
All frameworks are eventually replaced. React is probably the first that won’t be. It's not the best language out there, it's not the language developers love the most, it's the language the robots just won't quit. Request ChatGPT to develop a todo app for you. You'll receive React. Request Copilot to generate the basic structure of a component. React. Request Claude to design a prototype for a da
The problem Pattern matching on a large set of literal values looks clean in code but hits a wall at runtime. Every on() call constructs case objects for every arm. With 128 arms, that is 128 object constructions per match call. At 11ns per call, this is fine for one-off use. Inside a hot loop, it is a disaster. // Clean syntax, 128 case objects constructed per call return match(x) | on( lit(0