Show HN: Jax-JS, array library in JavaScript targeting WebGPU (ss.ekzhang.com)

A new machine learning library named jax-js has been launched, reimplementing Google's DeepMind JAX framework in pure JavaScript and targeting the web with WebGPU and WebAssembly for numerical computing. This cross-platform library allows developers to perform machine learning directly in the browser, highlighting the potential for real-time model training and execution without the bottlenecks commonly associated with JavaScript. Notably, jax-js can execute a range of operations, including automatic differentiation and JIT compilation, similar to its Python counterpart, providing an easy-to-use interface for web developers. The significance of jax-js lies in its capacity to democratize access to machine learning, enabling front-end developers to create comprehensive ML applications without relying on backend infrastructure. By leveraging the native capabilities of modern browsers, jax-js achieves near-native execution speeds for complex numerical operations, such as those in neural network training, while maintaining a familiar API for those experienced with JAX. This project opens new avenues for deploying ML models directly in web environments, paving the way for innovative applications, including real-time data processing and interactive experiences that were previously challenging to achieve in JavaScript.