Goodbye 2025! This has not been an easy year for fairly obvious reasons, and I’m not feeling as reflective at the end of the year as I sometimes am, but I’ll try to provide at least some of my usual year-end reporting.

The core of my toolset is pretty stable from 2023 and 2024: still in the Apple ecosystem for endpoints, and a lot of the major infrastructural pieces and small utilities are still in play. I’ll therefore focus this review on the interesting changes.

Ph.D. admissions committees, faculty search committees, etc. need to review dozens to hundreds of applications for a limited number of positions, and need to do so in a way that gives each applicant a fair review and keeps committee workloads manageable.

The way we’ve done this in a couple of committees I’ve been on is to implement two-stage review: each application is first read by two committee members, and if at least one of them thinks it merits further consideration, it moves to the next stage (full committee review, involving potential advisers, etc.). This requires us to assign those initial reviewers, however.

Effectively computing non-personalized recommendations can be annoyingly subtle. If we do naïve things like sorting by average rating, we get a top-N list dominated by items that one user really liked. Sorting by overall popularity doesn’t have this problem; as soon as we want contextual related-product recommendations, however (e.g. “people who bought this also bought”), and don’t want those recommendations to be dominated by the most popular items overall, the problem comes roaring back.

In my end-of-year blogging, I usually provide an update on my toolbox, and a review of the year.

So what’s happened? A quick (and likely incomplete) summary…

It’s that time of year again! Since I did a relatively full reset last year, for this year’s State of the Tools I’ll focus on changes and a few highlights. My Zoom studio post discusses my hardware and software for video recording in more detail.