Originally published at Perl Weekly 771 Hi there, I put the 'Testing in Perl' course on hold for now. Instead of that we are going to explore the use of some of the mocking libraries we saw during the course. In the next session we'll pick one of the Perl modules used for mocking and we'll look for modules that use it. We'll try to understand how it is being used and we'll try to contribute someth
In a fast-paced logistics hub, a Blue Screen of Death (BSOD) isn't just a technical glitch; it’s a killer vibe for the whole workflow. I recently had a user come to me with the ultimate jump-scare: their laptop crashed, rebooted, and hit them with: "No Bootable Device — Please download and install an operating system." 💀 To most people, that screen means "your files are gone." But as a Value Arch
Building a News Aggregator Without an Engagement Algorithm I have been building a project called WeSearch: https://wesearch.press It is a free news aggregator that pulls from hundreds of sources, keeps discovery mostly chronological, adds source/bias context where available, preserves permanent daily archives, and allows anonymous discussion on stories. The project started from a simple frustrat
AI Fabrics, Quantum-Safe Tunnels, and Cloud Policy April was a good reminder that networking is not standing still. The big themes were not abstract. They showed up in very practical places: data centers trying to keep up with AI workloads cloud networks becoming more private and policy-driven routing security getting more attention VPNs and firewalls preparing for post-quantum cryptography wire
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