I’m going to Idaho for the month of October! The Bay Area feels disconnected in its COVID response from the rest of the country - I’m excited to find out if that’s true.

This month, I set some huge gym PRs & ran my last time trial of the track season. With the offseason upon me, I’m ready to start a strength & explosiveness block in the weight room.

• This month, I’ve been really focused on work - we have a big launch coming very soon, so I’ve spent more hours than normal coordinating & building things for that.
• To close my track “season”, I was able to run time trials in the 100/200/400 with lifetime PRs in all of them!

• The biggest piece of news for the month: I moved out of SF into my parent’s house in Dublin! With coronavirus & related precautions, urban areas no longer have network advantages over suburbs. While Emily and I think about where we want to move long-term (perhaps Denver or New York after coronavirus fades away), we’re saving a bundle.
  • We’re really grateful to have this option: both that our jobs allow us to work remotely during coronavirus and that my parents are willing & able to house both fo us while we ride out the storm.

I’ve been working with fixed-width containers in React recently, so I’ve been able to focus on typesetting. One problem I’ve had is noticing widows in paragraph text as I change copy and styling. (widow is apparently an overloaded term in typesetting — here, I mean a single word that overflows into a new line, not a single line overflowing into a new page.) I ended up building a React component that automates widow detection - let’s dig in!

This post is cross-posted from Ladder’s engineering blog. Shout out Ladder for forcing me to write something!

At Ladder, we use Datomic as our primary data store and Google BigQuery as our data warehouse. We’ve iterated on how we send data from Datomic to BigQuery - starting from a nightly job dumping every entity and ending with a streaming solution with just seconds of latency. In this post, we’ll walk through bucketing Datomic entities into BigQuery tables, transforming entities into rows, and using one of Datomic’s superpowers to stream changed entities into BigQuery.