When Google announced the Manifest V3 deadline, the developer community had a lot to say — most of it negative. The service worker model was rightly criticized as a regression for ad blockers and complex extensions. I've now migrated 18 extensions from MV2 to MV3, or built them MV3-native from the start. The commonly documented issues (no persistent background pages, limited webRequest) are real.
An opinionated list of Python frameworks, libraries, tools, and resources
Modern yazılım geliştirme ekosisteminde altyapının kod olarak yönetilmesi hız ve ölçeklenebilirlik açısından devrim yaratırken GitOps yaklaşımı bu süreci merkezi bir doğruluk kaynağına bağlamaktadır. Ancak tüm yapılandırma detaylarının tek bir platformda toplanması kritik siber güvenlik risklerini de beraberinde getirmektedir. Nesil Teknoloji olarak TSE A Sınıfı sızma testi yetkimizle endüstriyel
Manifest V3 Is Here — And It Broke Everything Google's Manifest V3 migration deadline has come and gone. After migrating 17 Chrome extensions from MV2 to MV3, I've compiled every pitfall, workaround, and lesson learned. If you're still migrating — or building new extensions — this guide will save you weeks of debugging. The problem: MV3 replaces persistent background pages with service workers.
المشكلة لو بتكتب عربي وإنجليزي مع بعض في أي موقع، البراوزر بيتلخبط في الاتجاه: جملة زي "مرحبا API بتاعك كويس" — كلمة API بتتعكس وتتقرأ غلط بسبب الـ Unicode Bidi Algorithm. المشكلة دي مش في موقع معين — هي موجودة في كل المواقع. حتى Claude.ai وChatGPT نفسهم بيعانوا منها. بدل ما نضبط dir="rtl" على الـ element بس، عملت BiDi parser حقيقي يقسّم النص: "مرحبا API tools بتاعك" ↓ tokenizer [arabic]
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