Probably the primary use of jax is `jax.numpy` which is XLA accelerated and differentiable numpy.

I'll admit that saying "basically numpy on steroids" might have been an overreduction. It is a system for function transformations that is built on XLA and oriented towards science & ML applications.

It's not just me saying stuff like this.

François Chollet (creator of Keras): "[jax is] basically Numpy with gradients. And it can compile to XLA, for strong GPU/TPU acceleration. It's an ideal fit for researchers who want maximum flexibility when implementing new ideas from scratch."

Yes- and that gradient part is a key detail that makes it more than "numpy on steroids". numpy on steroids would be a hardware accelerator that took numpy calls and made them return more quickly, but without the command-and-control and compile-python-to-xla aspects.

Well clearly I meant steroids of the gradient-developing variety.

I think you are being far too pedantic about what a biological compound would analogously do to a software library, especially given that I mention the differentiability property in the same sentence you are taking issue with.

OK, actually as long as it's gradient-developing steroids, I'll allow it.