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
SOFTWARE ARCHITECTURE & REFACTORING 3 Domain-Centric Architectures Every Software Architect Should Know The first concern of the architect is to make sure that the house is usable; it is not to ensure that the house is made of brick. — Uncle Bob The expression domain is occurring in software bibles for a very long time now and is heavily discussed in the book Domain-Driven
Or: what broke on my first three attempts so you don't have to repeat it I've built two prediction markets from scratch. The first one crashed on testnet. The second one launched but had zero users for two months. The third one? Actually works. Here's what I learned in the process. Ask yourself three boring but critical questions: Binary outcomes (Yes/No) or multiple choices? Who decides the trut
What Should Humans Design When AI Can Write Most of the Code? AI can now write code. Not perfectly. Not always safely. Not without review. But it can write a great deal of code. It can generate functions, create tests, call APIs, build UI components, handle common errors, and produce large amounts of implementation detail at a speed no human developer can match. This changes the meaning of prog
We are currently witnessing a massive shift in AI development. We’ve moved past the "Chatbot" era and into the era of Agentic Systems—AI that doesn’t just suggest text, but actually executes code, moves money, and modifies databases. However, there is a fundamental architectural flaw in how most agents are built today: we are giving "Intelligence" and "Authority" to the same probabilistic model.
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