RootRecord: A Practitioner's Map of the Ecosystem RootRecord builds multi-device software for people who want serious tools without unnecessary lock-in: mobile apps for weather and hazards, a full business office in your pocket, a central account hub, and browser-based Solana utilities for token creation and on-chain operations. A single RootRecord account ties licensing, cloud sync where applic
I'm doing the 100 Days of Solana challenge by MLH, and Week 2 just changed how I think about blockchain data entirely. Week 1 was about identity — generating keypairs, understanding wallets, getting devnet SOL. That part felt familiar, like setting up a dev environment. Week 2 was different. Week 2 was about reading the chain — and that's where the mental model shift actually happened. I expected
Been spending the last ~10 days getting hands-on with Solana as part of a hackathon. Went in expecting things to feel completely different from what I’m used to. It wasn’t as far off as I thought. What I’ve Done So Far Generated a keypair + airdropped devnet SOL Created a wallet and checked balance programmatically Understood SOL vs lamports Connected a browser wallet Read on-chain data (accounts,
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