I’ve been running my Kubernetes cluster on Raspberry Pis for about a year now. Overall the cluster is stable and needs little attention and by now many useful and important services are now running on it. With more reliance on these services I need to ensure that the cluster doesn’t fail from preventable errors. One of the common failure modes for Raspberry Pis are sdcards. They are not very fast, they are of limited size, and worst, they tend to fail.

Was writing a few posts for my food blog and needed to type some some “special” characters like é. When on a Mac that’s really simple because the keymap supports typing compound keys. But I’m on Linux and switching between keyboard layouts is annoying. So I figured vim must have a way for typing these characters, and behold, I learned about Vim’s digraph support. The documentation is quite good but I’ll cherry pick some combos because otherwise I’ll forget.

I’ve spent some time over the holidays building a Kubernetes cluster running on raspberry pis. One issue I ran into was that not all docker images I wanted to run were available for arm/linux. Luckily there’s a useful tool called buildx that extends Docker to build containers for different platforms and architectures using quemu and binfmt. ARM has a blog post that details the steps needed to build images.

The steps are:

I spent a good hour trying to get a custom completion script working while porting my app Harbormaster to Cobra. It would and would not work. I retried writing the file, restarting the shell and it would just not offer any completions. Eventually I stumbled upon a innocent looking post on Github that held the solution: remove the .zcompdump file which holds the cached completions. So I ran rm ~/.zcompdump && compinit and everything works as expected.

I needed to crunch some data quickly and decided awk was the right tool to do so. But every time I use awk, I have to go read the manual, so I decided it’s time for a cheat sheet.

Structure of an awk script

# Comments begin with a pound sign
BEGIN {
  # Instructions run before the main loop
  FS = ";" # Set a Field Separator
}

# Each line of input is applied against all the following
# regular expressions and runs the instructions in the
# block:

/^$/ { print "An empty line" }

END {
  # Instructions run after the main loop
}

Invoke awk with a script like so:

I was browsing the Pragmatic Bookshelf and this book caught my eye: writing a ray tracer from ground up with a test driven approach? This sounds like a fantastic challenge to me. I always was interested in ray tracers but always thought it too complicated a topic to do it myself. However, test driven development has helped me work on some complex and terrible code bases, so this feels reassuring to me.

I recently switched back from MacVim to terminal (alacritty) Vim because I’m trying to step up my tmux game after reading the excellent tmux 2 book from the Pragmatic Bookshelf. But anyways, I noticed incredibly slow syntax highlighting for larger ruby files, so slow that editing code was almost impossible. After lots of searching and debugging I found it was the regular expression engine Vim uses by default in combination with the Ruby syntax highlighter.

I recently had the need to quickly visualize some data and none of the systems I usually work with had the data. Initially I dumped the data in Google Sheets and created a chart there, but that was slow and didn’t really scale well. The data had to be cleaned, brought into the right format, columns had to be selected and charts created. At this point I faintly recalled reading about gnuplot which, despite its name, has no affiliation with the GNU project.

I recently received my copy of Working Effectivly With Legacy Code and have been busy reading it. The book, as a product of its time, has examples of not only Java, but also C++, probably to show concepts and techniques that apply to languages that behave differently in terms of linking and building. But regardless of its examples not really applying to what I work with, it was full of useful vocabulary and techniques to work with not only legacy systems, but really, any kind of system.

Google recently announced availability of 1.0 preview of Flutter, a new framework for building mobile apps, written from scratch with performance and beautiful UIs in mind. I’ve always been interested in building apps for mobile devices in general and Android in particular thanks to the number of Android devices I own, but was always discouraged by the complexity of building Android apps. I have one on the Android store that got too unwieldy to work on and I eventually had to cease development due to time constraints. Now with flutter closing in on its first release build I got interested, not only because it is a new beginning to building mobile apps – and we all know that developers love newly built things – but also because it uses Dart, Googles attempt to create a language to replace JavaScript that, sadly, went not far.