Author Archives: Mario Lurig

child reading book

Installing Castopod on Shared Hosting

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Castopod is podcast hosting software that allows you to easily serve your podcast(s) from your own server rather than through a secondary service like anchor.fm, blubrry, and libsyn. While I personally use anchor.fm for some podcasts which I wanted to easily monetize, for a new podcast, Story Suggest, I had no desire for it to get monetized by me or anyone else. Furthermore, I also wanted to dip my toe into integration with the fediverse. Don’t worry if you don’t know what that is, it’s not critical to any of this.

Either way, while the installation documentation for castopod is relatively clear, I felt like there were a few quick notes/tips I could add for those installing on a shared host like Dreamhost. While I maintain a VPS at Dreamhost, the functionality and installation is the same. Below I’ve highlighted some clarifying points to help others quickly get setup with Castopod.

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discourse

Fixing Yarn ESOCKETTIMEDOUT Error During Discourse Setup

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While installing Discourse on a free tier instance in a Google Cloud Compute Instance, I was following this discourse install tutorial with only minor adjustments:

  • Used Ubuntu 20.04 LTS minimal
  • Standard Disk (default is Balanced)
  • e2.micro instance

However, when I was waiting for Step 9 to complete (after running ./discourse-setup) and everything being built, it would fail with an ESOCKETTIMEDOUT error related to yarn. The last message that tries to run is [ ! -d 'node_modules' ] || su discourse -c 'yarn install --production && yarn cache clean'.

Here’s what is going wrong. Yarn has a default timeout that is fine if you are using the minimum recommended for Discourse, but with a micro instance, it takes too long. To fix this, you have to manually edit one of the install scripts that runs for the new Docker container Discourse is building.
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PyCharm Logo

Solving “No Supported Authentication Methods” for PyCharm and GitHub on Windows

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Fatal Error: Disconnected: No supported authentication methods available.

I lost so many hours to that little error. Here was the problem:

I followed the steps in this lovely PyCharm tutorial which walks you through creating your ssh key, saving it to a profile, and applying it to your GitHub account. This was successful in getting the terminal working. However, the error noted above was happening when I tried to use PyCharm’s Git integration and pull or push with the GitHub repository.
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Introducing reMarkable 2

Template file for Creating PDFs for the ReMarkable 2 Paper Tablet

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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.

discourse

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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Dropout Rate compared to Accuracy

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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