I’ve been playing around with https://nono.sh/ , which adds a proxy to the sandbox piece to keep credentials out of the agent’s scope. It’s a little worrisome that everyone is playing catch up on this front and many of the builtin solutions aren’t good.
I'm also a long time conda user and have recently switched to pixi (https://pixi.sh/), which gives a very similar experience for conda packages (and uses uv under the hood if you want to mix dependencies from pypi). It's been great and also has a `pixi global` similar to `pipx`, etc the makes it easy to grab general tools like ripgrep, ruff etc and make them widely available, but still managed.
I use numba quite a bit at work and it's fantastic. I recently, however, did a comparison between numba, cython, pythran and rust (ndarray) for a toy problem, and it yielded some interesting results:
Most surprising among them was how fast pythran was with little more effort than is required of numba (still required an aot compilation step with a setup.py, but minimal changes in the code). All of the usual caveats should be applied to a simple benchmark like this.
Another relatively new addition to the python statistical sampler space is Austin[1], that has a lot of similar features to py-spy. I haven't made a direct comparison yet between the two.
I'm actively looking for alternatives since I found this to be a super useful feature in terms of seeing the state of a repository and the relationship between active branches. I've seen https://gitup.co/, but it doesn't have a view that is organized by commit date.
First class multi-dimensional arrays with natural indexing syntax would be a huge draw. You get that in Numpy, Julia, Fortran and it is extremely powerful in terms of expressing the types of operations that are common across many scientific domains.
Conda is 100% free and open source, but is developed by Continuum, which is a company (but one that contributes heavily to many of the key components of the open-source pydata/scipy ecosystem, and whose CEO basically created numpy/scipy for the community at the expense of his academic career). Personally I have had nothing but good interactions with people from Continuum and the software they develop. They (as a company and as the individuals in it) do an incredible amount for the open source community and we all benefit from that. They seem to have worked out a sustainable model for running a company and working in/contributing to open source.
You should also check out https://conda-forge.github.io/, which is building high quality, community curated conda packages for a lot of stuff that isn't available from the main Continuum channel.
While I still think it a bit unfair, the exact quote is "...but to a first approximation, no one is using it". So I think (assuming fijal's numbers of 0.5-1% are roughly correct) it is not completely unreasonable to say that PyPy adoption is very limited to date, which was the point of what he was saying (although I think it could have been said more diplomatically).
I'll echo @dagw's comments. Cython has been rock solid for me for a long time and it is my go to for any sort of external c/c++ library interfacing. That said, I find myself using Numba more and more in the places that I can as Numba has gotten significantly better in the last 6 months or so. It's still not a complete replacement for cython (and I don't think it ever will or intends to be), but for hot spot numerical calculations it's really nice since it's much faster to test things out since it doesn't involve the boiler plate required by Cython, and doesn't require the (often slow) compilation times.
In Numba 0.21.0, on-disk caching of jit'd code was also introduced, which was one of the major sticking points for us to put Numba into production in areas where we needed faster start-up times. Before we could really only use Cython because we required the start-up times available only from AOT compilation.
That all said, Numba has been a bit buggy for me at times, although these get squashed pretty quickly. I've only found a single bug in Cython in all of the years I've been using it, and it's amazing how quickly Robert Bradshaw or Stefan Behnel respond and fix things considering Cython is not their full time job.
I use cython extensively in my work and it's super productive for me as well, but I could see maybe toying with Rust more in the future for some really specialized things.