Longer-form thoughts that don't fit in a talk or a tweet.
Applying machine learning inside a scientific instrument changes the job in ways that aren't obvious from the outside.
A closer look at what's really encoded in a learned embedding space, and why the geometry matters more than the numbers.
Why a background in visual communication turned out to be better preparation for machine learning than I expected.