Howdy, Space Cowboy
My name is Sean. I'm a software developer from Vancouver, Canada. I focus on systems and embedded programming. I've previously written code which operated in space; and am currently working in motion automation.
I enjoy math, programming, and building things. I'm working on a couple of projects at the moment You can find my writings and project notes on this site.
I'm interested in many topics these days. If you see anything here you find interesting please reach out to me. I love getting email, à la Patrick McKenzie. Consider this a standing invitation.
Do people still send emails? Please email me to let me know.
You can see semi-formal writings, as well as my own notes on my blog. I maintain a list of daily links for tracking things on the internet I find interesting. Write-ups for completed projects can be found under projects. You can read about me personally on my about or now pages. For a list of other information on this site see Miscellanea or the SiteMap
Blog
They're Right About Vim
January 18, 2024
Hello, World!
January 17, 2024
Daily Links
June 15, 2025
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Beej's Guide to C Programming: an extremely comprehensive guide to C programming. I thought I knew C very well, and I still learned a lot reading through this.
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Float Exposed: a tool which visualizes the bit-representation of floating point numbers. Extremely useful for getting an intuitive understanding.
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Terms of Service; Didn't Read: "“Terms of Service; Didn't Read” (short: ToS;DR) is a young project started in June 2012 to help fix the “biggest lie on the web”: almost no one really reads the terms of service we agree to all the time."
Projects
ORCASat
September 2, 2023Optical Reference Calibration Satellite. A 2U CubeSat designed and built by students at the University of Victoria. Launched aboard CRS-26 from Kennedy Space Center to the ISS on Nov. 26, 2022, then deployed to low earth orbit on Dec. 29, 2022. Atmospheric re-entry completed on July 7, 2023.
Satellite Image Classifier
April 13, 2023Trained Convolutional Neural Network to identify locations likely to contain mineral deposits on the Earth’s surface using NASA Landsat images. Training labels for supervised learning were generated programmatically using a corresponding dataset of known mineral deposits. The Landsat dataset is preprocessed by downsampling to a resolution of 512x512