I’ve been learning Golang recently, and while Go’s ultra-simple nature is often refreshing it can be a bit limiting too. One Typescript feature that I found myself missing is enums. While there are a lot of people asking about this, there aren’t a lot of Go-beginner friendly explanations of how to do this out there, so I decided to dig in. After a bit of research I’m happy to report that Go can get pretty darn close!

As mentioned previously, I’m a big fan of Jupyter Notebook/Lab. That said, there is one thing that’s always irked me about working with Jupyter notebooks: Jupyter’s .ipynb format doesn’t play well with version control software like git. Jupyter notebooks use a JSON-based format that allows for storing both the input and the output of each cell.

I’ve been working on a project lately that does a lot of calculations based off of input data that’s being read in from Excel spreadsheets. For this kind of thing Jupyter Notebook/Lab and pandas are my tools of choice, but for this particular project I needed more speed than usual. I’ve known of Cython for quite some time, but never actually had the chance to play with it.

Moving a Linux installation from one machine to another is actually relatively easy to do, but there aren’t many articles online that walk through the whole process. Unlike some other operating systems (I’m looking at you Windows) Linux is by default fairly uncoupled from the hardware it is running on. That said, there are still a few gotchas that need to be watched out for, especially when it comes time to configure the bootloader. This post takes you through the whole process and assumes minimal Linux experience: if you’re comfortable with basic shell commands you should be able to follow along.

This is the first in a series of articles about ZFS, and is part of what I hope becomes an ongoing series here: the zero-assumptions write up. This article will be written assuming you know nothing about ZFS.

I’m a big fan of Jupyter Notebook, and an even bigger fan of the more recent Jupyter Lab (which just reached 1.0!), and for a recent project I wanted to use it as the “frontend” for some python number crunching code. The audience isn’t necessarily technical, so the challenge was to try and make Jupyter act a bit less like a programmer’s tool and a bit more like a traditional webapp.