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
I needed to coordinate background scripts running across different machines. The obvious answer was Redis. Everyone uses Redis for this. The tutorials all use Redis. The Stack Overflow answers all say "just use Redis." So I looked at what deploying Redis would actually cost me: A running Redis server I had to maintain A broker to connect workers to it Celery or RQ on top of that Memory-based stora
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
All tests run on an 8-year-old MacBook Air. Most AI integration tutorials assume you're paying for API access. HiyokoLogcat is built entirely on Gemini's free tier — and designed so users bring their own free API key. Here's what's possible, what the limits are, and how to design around them. Gemini 2.5 Flash Preview: 15 requests per minute (RPM) 1,000,000 tokens per day 250 requests per day For a