An opinionated list of Python frameworks, libraries, tools, and resources
We’ve been running a series of experiments using ChatGPT 5.4 integrated into a website chatbot across different environments: 🌐 a main website 🎯 Goal: simulate realistic user behavior and observe how the model responds over time. ⚙️ Test setup The chatbot is designed to (no self promo here, just context): 📌 answer strictly based on website content (RAG-like approach) Over time, we intentionally
Introduction Backup plugins are useful — but they are not a real disaster‑recovery strategy. In this article, I break down the real reasons why backup plugins fail, what actually happens during a server‑level crash, and how to build a recovery plan that works in the real world. Backup Plugins Only Work When WordPress Works A backup plugin depends on: WordPress running PHP running MySQL running the
Riad Hasan has optimized dozens of WordPress sites for clients worldwide. In this guide, he shares the exact techniques he uses to achieve sub-2-second load times and perfect Core Web Vitals scores. Performance isn't just about speed — it impacts SEO, user experience, and conversions. Riad Hasan explains his systematic approach to WordPress optimization. Google's Core Web Vitals are now ranking fa
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
Stop clicking through wp-admin for every small task. WP-CLI is a command-line tool that lets you manage WordPress sites faster — all from your terminal. Why WP-CLI? Problems It Solves Is It Good or Bad? Setup & Installation Must-Know Commands A Quick Real-World Use Case Your First Custom Script What's Next Every WordPress developer knows the pain — update 12 plugins, flush cache, reset a password,
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