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For those not already in-the-know about Tony Horton’s infamous X-treme workout series, P90X was a collection of video workouts released in 2005. They’ve achieved something of a cult following in the years since, and Tony has returned in several reprisals – though none seem to have gained the fame of the original P90X. The workouts require minimal equipment: just a pull-up bar, a yoga mat, and a modest collection of dumbbells.

I spend a lot of time on meta-productivity: thinking about how to get work done better and faster. The majority of that thinking is around technology and specifically the tech tools I use. Maybe thinking about my workflow has returns large enough to offset the time it consumes in my life, but frankly it doesn’t matter too much to me since life optimization is as much a hobby as an objective.

For a while now, I’ve been thinking about how interesting it would be to look at data on my running habits. Depending on the time of year and some other factors, I probably run 1-4 times a week, and I’ve been running semi-regularly since college.

For the vast majority of that time, I’ve recorded my runs in Under Armour’s MapMyRun app. It’s a bit buggy and feels non-native, but it has a good Apple Watch app and collects the information I need fairly reliably.

Over the past few years, I have heard many people repeatedly note the downsides of Twitter, particularly its impact on our society. I don’t agree with all of those criticisms, but some are legitimate and most deserve a serious discussion. This isn’t that discussion, though. Instead, I’d like to spend some time on the many merits of Twitter, so lost in our current debate. Good Writing Or: If I had more time, I would have written you a shorter tweet

My colleague Brad Boehmke and I made a guest appearance on the Talk Python to Me podcast to talk about the challenges of supporting a large data science department that uses both R and Python. You can listen to the episode on any podcast player.

I was recently interviewed for 84.51˚’s podcast on the topic of upskilling the data science function.

I contributed a walkthrough to some basic Linux utilities for data scientists to the 84.51˚ tech blog.

I wrote a piece for the Syntact Project on Gross National Happiness as an alternative to GDP.

My colleague Dominick Ghirardo and I wrote a short piece for our company’s tech blog on approaching automation in data science.

I’ve set up three Raspberry Pis over the last two days. By set up, I mean I burned the disk image to the SD card, got the Pi connected to the network and open to SSH, installed the tools I’m going to want every time, etc. Basically, make it just another identical worker node. I own six Pis total (for now), so I’ve done this before, and I’ll do it again. In fact, two of the installs I did were on Pis I’d owned for a while; I just wanted to wipe them and start fresh.

So after the first two this weekend took me a lot longer than they should have, I realized it was about time I documented my process and made it repeatable.