A next‑generation numerical computing library that combines a NumPy‑like API with automatic differentiation and XLA compilation. It enables fast, composable transformations such as vmap/jit and scalable parallel execution on GPUs and TPUs. We use JAX when we need high‑performance research workflows: prototyping new architectures, experimenting with advanced optimization methods, and running compute‑intensive training or simulation tasks with tight control over performance.