L'IA vocale en gestion de chantier : retour d'expérience après 50 projets BTP Le problème : les mains pleines, le temps compté Sur un chantier, le chef de projet ou l'artisan a les mains occupées. Qu'il soit en train de mesurer une façade, de vérifier l'aplomb d'une cloison ou de valider du béton fraîchement coulé, la dernière chose dont il a besoin est de sortir son téléphone pour re
Voice AI for Jobsite Estimating: A Developer Perspective Building estimators spend hours hunched over spreadsheets, struggling with poor handwriting on site photos, and entering the same data twice (once on paper, once in the office). This workflow is broken. Voice AI changes everything—and it's simpler to implement than most developers think. In this article, I'll walk you through the real-worl
Voice AI for Jobsite Estimating: A Developer Perspective The construction industry has historically lagged behind in digital adoption. Yet today, one of the most transformative shifts happening on job sites isn't coming from enterprise software vendors—it's coming from applied AI at the edge. Voice-based estimating is reshaping how builders create quotes, manage materials, and streamline workflo
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
A production-grade embedded system enabling communication across speech, text, Morse, and haptic signals within a single unified pipeline. Official Project Page: https://anandps.in/projects/unified-assistive-communication-system GitHub Repository: https://github.com/anand-ps/unified-assistive-communication-system Problem Assistive communication systems are fragmented. Most tools so