E aí, gurizada! De uns tempos pra cá, tenho percebido uma mudança significativa na forma como a gente interage com a Inteligência Artificial. Não é mais só uma ferramenta que responde perguntas ou gera imagens; a parada tá ficando séria, com a IA assumindo um papel mais ativo, quase como um colega de trabalho. Foi pensando nisso que gravei um vídeo recentemente, e a repercussão me fez pensar: "Car
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TL;DR You can integrate Azure DevOps with GitHub to get the best of both worlds in Power Platform development. ADO stays as the backbone: work items, sprint planning, test plans, and deploy pipelines all remain on Azure DevOps. Code moves to GitHub: Power App Code Apps or Power Pages SPA live in GitHub repos, unlocking native GitHub Copilot integration and the Copilot Cloud Agent. The two platfo
For years I thought my only options were dual booting or using a clunky virtual machine. Dual boot meant constant reboots, and VirtualBox ate my RAM. Then I discovered Windows Subsystem for Linux 2, and honestly it changed how I work. Now I run a complete Ubuntu desktop right next to my Windows applications. I can code in a native Linux environment, test web servers, and even fire up Linux-only GU
A step-by-step guide for beginners who want a gaming PC and a real enterprise Linux environment on the same machine — with every decision explained in plain English. What Is Dual-Booting and Why Rocky Linux? UEFI, BIOS, and Secure Boot Partitions, File Systems, and GPT The GRUB Bootloader Before You Begin — Checklist Phase 1 — Shrink Your Windows Partition Phase 2 — Download & Flash Rocky Linux Ph
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