Run the same brand-query through ChatGPT, Gemini, Perplexity, Claude, and Grok. Read the citations. The cited URLs will not be the same, the brands featured will not be the same, and in roughly a third of cases one tool will cite your brand confidently while another does not mention it at all. The temptation is to reach for an algorithmic explanation different rerankers, different summarisation st
This is the follow-up to What I Actually Learned Building a Side Project in 5 Days With AI. That post was about AI. This one is about what happens after you ship — when you actually have to run the thing. I lost a freelance client last year because I forgot to send a monthly report. Not because I didn't do the work. I did the work. I just never wrote it down in a place I'd actually look. The repor
A few days ago, I read a fascinating post here by @404Saint about Arkoi, a tool designed to detect SEO poisoning. It struck a chord with me. If attackers can manipulate search engine results to push malware, what’s stopping them from manipulating the Latent Space of LLMs to misrepresent critical Web3 protocols? As the founder of HUTMINI, I’ve been obsessed with a new problem: AI-Era Visibility. We
TL;DR: I shipped image → PDF conversion but spent most of the week on SEO content instead of the planned batch UI and landing page. The numbers say that was the right call. Organic search became the #1 traffic source for the first time. Convertify is a free image converter I'm building solo: Rust + Axum + libvips on the backend, Next.js 16.2 SSG on the frontend, PostgreSQL for landing page content
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