Latest Posts

In the last article, we were able to successfully deploy a Google Cloud Storage (GCS) bucket using Terraform. It was a little bit anticlimactic, since the only real indication that we had done anything at all was the output of gsutil ls showing that the bucket was created. In this article, we’ll get closer to our goal of hosting a static site on GCS by getting our implementation to the point where there’s actually a URL we can visit and see some static site content.

I was recently challenged at work to come up with a long-term personal development goal for the year. I suck at doing that stuff. But as our team is moving increasingly towards cloud computing, I do know that eventually I will hit a ceiling if I don’t learn something about building complex systems in the Cloud. This article is my first stab at that on my own dime, and with my own time.

I’m proud to announce that I’ve made my first official contribution to the Python Package Index (PyPI) with the release of a package called yaml-extras. This package is the culmination of what I’ve discussed in the previous three blog posts dedicated to loading the contents of YAML files into one another in a modular way. See the other three articles linked below:

What happens when we want PyYAML to dynamically load an entire directory of YAML files into the contents of a single YAML document? At the moment, this is painfully manual, where importing all the contents of a directory require separate !import nodes for each document:

Where we last left off, I was just able to import a whole YAML file’s contents into another YAML file using a custom Loader and Constructor in PyYAML for the !import tag. But there’s a missing piece. What happens if I want to merge multiple YAML files during import? First, let’s talk about the YAML merge key, “<<”, and how it works.

Since I’ve started working at Intuitive full-time, I’ve been interacting with YAML files in some capacity pretty much every day. Some of that is because I do a lot of dbt development as a data engineer, and dbt uses YAML files to define models, tests and other properties about the data model. And there’s pre-commit, Gitlab CI, as well as other custom Intuitive tools that use YAML files to define configurations.

I’ve spent the past two weeks just starting to get a handle on how to use the popular low-level, memory-safe programming language Rust. In general, it gives me C++ vibes when I’m programming in it. That said, I think that it could have good potential as a machine learning development environment, so long as it is supplied with the appropriate external libraries (or “crates,” to use Rust’s terminology.)