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Starting with IPython 7.16.1 (released in June 2020), you should be able to recreate the sdist (.tar.gz) and wheel (.whl), and get byte for byte identical result to the wheels published on PyPI. This is a critical step toward being able to trust your computing platforms, and a key component to improve efficiency of build and packaging platforms. It also potentially impacts fast conda environment creation for users. The following goes into some reasons for why you should care.

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The problem of storing and manipulating large amounts of data is a challenge in many scientific computing and industry applications. One of the standard data models for this is HDF5, an open technology that implements a hierarchical structure (similar to a file-system structure) for storing large amounts of possibly heterogeneous data within a single file. Data in an HDF5 file is organized into groups and datasets; you can think about these as the folders and files in your local file system, respectively. You can also optionally store metadata associated with each item in a file, which makes this a self-describing and powerful data storage model.

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Open source is notorious for lack of design presence, enough so that my search to prove this fact has turned up nearly nothing. There’s many ways that such a gap in community might manifest, but one that I never anticipated was working with developers that had never interacted with a designer before.

A quick note for context: I’m writing this as a UX/UI designer working with open source projects for a little over a year. Because there are so many ways design processes can happen (enough to warrant its own blog post), this post is not intended to discuss design process deeply. My goal here is to pass on some of what I’ve learned that helps me design in this unusual space in hopes that it can help someone else. This post might seem most relevant for designers, but I think this experience could be helpful for developers as well.

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I joined Quansight at the beginning of April, splitting my time between PyTorch (as part of a larger Quansight team) and contributing to Quansight Labs supported community-driven projects in the Python scientific and data science software stack, primarily to NumPy. I have found my next home; the people, the projects, and the atmosphere are an all around win-win for me and (I hope) for the projects to which I contribute.

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I recently moved, and my new place has a relatively small footprint. (Yes, I moved during the COVID-19 pandemic. And yes, it was crazy.) I quickly realized that was going to need a wireless router of some sort, or more formally, a wireless access point (WAP). Using my Ubuntu laptop's "wireless hotspot" capability was a nice temporary solution, but it had a few serious drawbacks.

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I joined the Spyder team almost two years ago, and I never thought I was going to end up working on docs. Six months ago I started a project with CAM Gerlach and Carlos Cordoba to improve Spyder’s documentation. At first, I didn’t actually understand how important docs are for software, especially for open source projects. However, during all this time I’ve learned how documentation has a huge impact on the open-source community and I’ve been thankful to have been able to do this. But, from the beginning, I asked myself “why am I the ‘right person’ for this?”

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Python environments provide sandboxes in which packages can be added. Conda helps us deal with the requirements and dependencies of those packages. Occasionally we find ourselves working in a constrained remote machine which can make development challenging. Suppose we wanted to take our exact dev environment on the remote machine and recreate it on our local machine. While conda relieves the package dependency challenge, it can be hard to reproduce the exact same environment.

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After more than three years in development and more than 5000 commits from 60 authors around the world, Spyder 4 finally saw the light on December 5, 2019! I decided to wait until now to write a blogpost about it because shortly after the initial release, we found several critical performance issues and some regressions with respect to Spyder 3, most of which are fixed now in version 4.1.2, released on April 3rd 2020.

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