A common mistake when NumPy’s RNG with PyTorch
This is probably because I never read these kinds of blogposts but this is one of the most flagrantly clickbait titles I've ever seen. Like the article doesn't even suggest ditching numpy in favor of jax or some kind of other hot take (which would at least warrant such a bombastic title) it literally just presents one instance in which you <i>might</i> be making a mistake when using numpy's rng (not even something more unique to numpy). And the PyTorch team is aware of this and hence exposes `worker_init_fn`. So the title should actually be "Using fork without understanding fork? You might be making a mistake."