a tumblelog
Sat 25 Jul 2020

San Francisco

In a reply on Hacker News user JustARandomGuy mentioned that I had misspelled San Francisco in the CSS file for this blog; I had written it as San Fransisco 🙄.

Part of the CSS file with San Francisco misspelled
Part of the CSS file with San Francisco misspelled.

A quick find-grep from within Emacs showed that I had made the same mistake in all Sass source files, files ending with the scss extension, for the tumblelog project. This project is the SSG (static site generator) that generates this blog.

I also noticed that those files had a copyright 2019, to which I added 2020. And all those SCSS files still had the old "same terms as Perl itself" license, which I removed as the entire project is now available under the MIT license.

After those fixes I bumped the version number of the project to 4.0.5 and pushed the new version to GitHub.

Sexing Tarantulas Using Molts

In the afternoon, after I had made some fixes to tumblelog, I finally wrote a short guide on how to sex a tarantula using its cast-off exoskeleton.

The molt of a juvenile female Brachypelma smithi
The molt of a juvenile female Brachypelma smithi.

I used the molt of a Brachypelma smithi that molted the 25th of June, 2020. The exuviae used in the tutorial is show in the above photo. To get an impression of the size of the molt, each square on the paper is 5mm by 5mm and the black line near the bottom is 25mm or about 1 inch.

Explaining RNNs without neural networks

Vanilla recurrent neural networks (RNNs) form the basis of more sophisticated models, such as LSTMs and GRUs. There are lots of great articles, books, and videos that describe the functionality, mathematics, and behavior of RNNs so, don't worry, this isn't yet another rehash. (See below for a list of resources.) My goal is to present an explanation that avoids the neural network metaphor, stripping it down to its essence—a series of vector transformations that result in embeddings for variable-length input vectors.

Source: Explaining RNNs without neural networks, an article by Terence Parr.

Setting Up Haskell Development Environment: The Basics

This post differs from most posts around setting up a Haskell development environment in the sense that it does not directly jump into Cabal or Stack. Instead, it first provides some background information that makes it possible to understand the basics of the development environment in Haskell, the different moving parts, and how those come together in turning Haskell source code into an executable which can then be run.

Source: Setting Up Haskell Development Environment: The Basics.