Hi all, In a few places I noticed recently that it has become harder for Numba to keep up with NumPy releases. Numba is very popular, so that's not an ideal situation for end users or for packages which have both NumPy and Numba as dependencies. If we can do things to make the life of the Numba team easier, we should consider doing them I'd think. As far as I can remember, we have never had a broad discussion on this. Numba devs have posted issues about individual APIs or behavior, but they're not labelled or prioritized in any way. Rather they just go in our 1000+ open issues backlog. And then other libraries/projects get to rediscover the same issues sometimes - because if it's an issue for Numba, it's likely to be an issue elsewhere too. Here's an example: https://github.com/numpy/numpy/issues/13718. Independently discovered by PyTorch, Warren's ufunclab, and in the Data APIs discussions at least (I suspect JAX/CuPy too, but they didn't link to that issue). Stuart Archibald wrote up a very nice overview of the main pain points Numba devs are seeing at https://github.com/numba/numba/issues/8008. It would be great if others could read those, and discuss the content in that issue in case things are unclear. And then we could circle back and perhaps add a dedicated topic like "Numba compatibility" on our roadmap under https://numpy.org/neps/roadmap.html#interoperability. Not everything will be fixable, but some things should be. And if we'd have to make a few minor technically backwards-incompatible changes (e.g. returning tuple-of-arrays rather than list-of-arrays from some functions), that'd be worth considering. Cheers, Ralf