Template file for Creating PDFs for the ReMarkable 2 Paper Tablet

I recently received the reMarkable 2 paper tablet and it is everything it promised. I’m excited to use it for work and personal adventures, but for work I wanted to create a document template that would allow me to always make content that is pixel perfect for the tablet. This way there is no scaling necessary and I can even leave in margins for the menu options to stay visible.

So, here it is: reMarkable 2 OTT Template

This was designed for OpenOffice or LibreOffice, but since it is an open format, I’m sure it can be opened by Microsoft and other document programs. Once you open the file, you can save it as a template using the File menu. After that, it’s simply a matter of selecting File > New > Template and selecting reMarkable 2 from the available templates! When you’re done, Export to PDF and transfer to your reMarkarable using their convenient applications.

Adventures in Podcasting

Installing Discourse with Amazon EC2 t2.micro Instance and SparkPost

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This is more notes and reference than an in-depth tutorial, but after spending a few hours trying different things, here’s how to get it all set up. Remember, just as Discourse recommends, a t2.micro instance only has 1GB of memory, so if you intend to grow things to an Internet-wide audience, you should use a t2.small instance instead.
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Testing Dropout Rates for Machine Learning with FastAI

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As I continue my adventures in machine learning through the FastAI courses, I wanted to explore the concept of dropout rate. If you would like to see the Jupyter Notebook used for these tests, including full annotations about what/why, check out my machine learning github project. Specifically the Testing Dropout Rates (small images).ipynb.

Really quickly, dropout rate is a method in Convolutional Neural Networks (CNNs) of removing neurons (e.g. in the first layer of an image this would be individual pixels) to prevent overfitting (i.e. doing notably better on the training set than on the validation set) and thus increase the general applicability of the model. In other words, block a percentage of the material to force it to not become to overdependent on repeating patterns that lead it astray.

These tests were setup to isolate dropout rate as much as possible. Also, while this test was using ResNet50, results may differ using a different model. Okay, enough jibber-jabber, let’s jump right to the conclusions, shall we?
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Machine Learning Install on Windows with Fast.ai

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When getting started exploring machine learning, you will likely come across the free lessons at Fast.ai. These lessons require a few gigabytes worth of programs and algorithms as well as access to a powerful GPU from Nvidia (e.g. GTX 1060). The first lesson even walks you through setting up a cloud server for just that purpose, but what if your PC already has a powerful Nividia graphics card? What if you use Windows?

No problem. This quick guide walks you through the process of setting up a local environment for machine learning, starting with the Fast.ai tutorial series. It’s designed for Windows PCs with an Nvidia graphics card. Alright, let’s get started with a few quick downloads.

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Replacing a Fanatic Windsurfing U Shaped Pin

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After recently acquiring some older windsurfing gear, I was introduced to a lot of old parts and connector systems. Namely, rather than the modern connection systems, a 1990’s era Fanatic board use a U shaped pin to secure the windsurfing board’s female end with the mast’s male end. A stainless steel pin is then slid through existing holes to trap the mast from pulling out. The downside to this system, at least for me, was that the pin was lost while not in use as it’s not permanently attached to either the board or the mast extension. So I built a DIY pin.