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memory · code learning · WebAssembly · mini project
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Use my saved coding want-to-learn memory to suggest a practical first mini-project for this weekend. You may request and use that saved memory. After using the saved memory, do not search the web, do not use app skills, do not create documents or files, do not create embeds, and do not ask a follow-up question.
Based on your interest in learning [Rust](wiki:Rust_(programming_language)), [WebAssembly](wiki:WebAssembly), and Asynchronous programming patterns, I suggest building a **Web-Based Real-Time Image Filter**. This project is an ideal entry point because it provides immediate visual feedback and highlights why WebAssembly is valuable. ### The Project: Rust-Powered Image Processor The goal is to create a simple web page where you can upload an image and apply filters (like grayscale, sepia, or brightness) using a Rust backend compiled to Wasm. **Why this fits your goals:** * **WebAssembly:** You will use `wasm-pack` and `wasm-bindgen` to bridge the gap between [JavaScript](wiki:JavaScript) and Rust, learning how to pass large buffers of memory (pixel data) between the two. * **Rust Fundamentals:** You'll practice using [Iterators](wiki:Iterator) and handling raw byte arrays efficiently. * **Async Patterns:** You can extend the project by using [Future](wiki:Futures_and_promises) patterns in Rust to handle file loading or to offload the processing to a [Web worker](wiki:Web_worker), ensuring the UI remains responsive during heavy computation. **Suggested Weekend Roadmap:** 1. **Setup:** Initialize a project with `wasm-pack` and set up a basic HTML/JS frontend with a `<canvas>` element. 2. **The Bridge:** Write a Rust function that takes a `Vec<u8>` (representing RGBA pixels), iterates through them to calculate grayscale values, and returns the modified vector. 3. **Optimization:** Once the basic filter works, explore using Rayon or internal async tasks to see if you can parallelize the pixel processing for very large images. This project directly applies what you've shared about your learning path by combining high-performance computation with modern web integration.