Projects
Here are some tools and games I’ve made that you might find useful or fun. I sometimes forget to update this page for years so it might be out of date.
- Mess with DNS (2021)
- dns-lookup (2021)
- nginx playground (2021)
- debugging mysteries (2021)
- rbspy: a Ruby profiler (2018)
- Computers are fast (2016)
- Pandas Cookbook (2014)
Mess with DNS
https://messwithdns.net is place for you to experiment with DNS! I built this because I love learning about DNS by experimenting, and I wanted to make an easy safe place to let other people experiment too.
There’s also a blog post about the project. I built this with Marie Claire LeBlanc Flanagan.
dns-lookup
https://dns-lookup.jvns.ca is a simple web-based DNS lookup tool! I built this because I thought that dig’s
output to be unnecessarily unfriendly. I actually use it all the time to make
basic DNS queries.
There’s also a blog post.
nginx playground
https://nginx-playground.wizardzines.com/ is a site where you can test nginx configs. I built this because testing nginx configs is SUPER annoying and I thought it might be possible to quickly build an interface to make it faster. (it worked!)
There’s also a blog post.
debugging mysteries
https://mysteries.wizardzines.com/ is a set of choose-your-own-adventure style debugging mysteries. There’s also a blog post about how it works..
rbspy
rbspy is a sampling profiler for Ruby! I took a 3-month sabbatical from work (Jan-March 2018) to build it. It’s still actively maintained, I handed over the maintainership to Adam Jensen in 2021. Some links:
- series of blog posts about the project
- the documentation
- the project on github
Computers are fast
A game to teach you that computers are fast: https://computers-are-fast.github.io/. To me the fun thing about this game is that even though I built it, I still get the questions wrong all the time (how many times can a C program loop in a second??) I did this project with Kamal Marhubi.
Pandas Cookbook
https://github.com/jvns/pandas-cookbook is an tutorial on how to use Pandas, the amazing Python data analysis library. I made this on a 12-hour train ride in 2014 and it’s helped a surprisingly large number of people learn pandas, judging from the number of stars/forks :)