If this is useful, a ❤️ helps others find it. Everything I keep looking up when building Tauri v2 apps — in one place. // Define #[tauri::command] fn greet(name: String) -> String { format!("Hello, {}!", name) } // With error handling #[tauri::command] fn read_file(path: String) -> Result { std::fs::read_to_string(path).map_err(|e| e.to_string()) } // Async #[tauri::command] async fn fet
Repo: https://github.com/richer-richard/socratic-council Stack: Tauri 2 (Rust + React/TypeScript), pnpm monorepo, Apache-2.0 Latest release: v2.0.0 If you ask one frontier model a hard question, you get a confident answer. If you ask sixteen, you get an argument. Socratic Council is a desktop app that runs a structured seminar between sixteen LLM agents drawn from eight providers — OpenAI, Anthr
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
All tests run on an 8-year-old MacBook Air. The default: Tauri commands When commands aren't enough: events Targeting specific windows The channel API for streaming data async fn stream_data(on_event: Channel) -> Result<(), AppError> { What I don't use The pattern I follow User action → invoke command → return result Three patterns. That's the whole IPC layer. If this was useful, a ❤️ helps more t
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