The trend in Cloud Computing is this idea of Microservices, Containers, Serverless Functions, and all sorts of fancy words for “if you pay us, you can almost forget that your code is running on someone else’s computer.” But what if you don’t forget?
Recently, I’ve been working on some very cursed calendar maths. Specifically, trying to write some efficient code to work with some of the common operations you’d want to do with dates. I know that loads of code exists already to do this, but I wanted to, in a sense, discover these algorithms for myself, so I really understand how these computations work. There are loads of links to papers and explanations of why these algorithms work scattered among the existing implementations, but I figured, why can’t I just start from the rules and figure it out?
Before I fully unload the details, though, there are a few things that you need to know about calendars.
Content warning: I’ll be making a few jokes about various calendar terms, since a lot of people like to pretend that the “international calendar” isn’t the Christian calendar, forced upon the world by imperialism. There will be a few terms I intentionally misidentify because I find it funny. The remainder of what I say, however, will be correct.
Over the course of the past few years, one genre of game has had me particularly impressed, and to be entirely honest, it started earlier than you’d think. Unfortunately, it doesn’t really have a name, and I want to change that.
Frog Fractions is a game that is starred by a frog, and has very little to do with fractions.
Preface: If you haven’t read the excellent essay Falsehoods Programmers Believe About Names by Patrick McKenzie, or haven’t read it recently, I strongly suggest that you do so before reading this article. This article will brutally undermine the original’s efficiency by overanalysing its points, and in my opinion, it’s good to have your own thoughts about it before you hear mine.
Preface: this isn't really a post about how we use machine learning to predict the weather, since we don't predict weather in a way that most people would call “machine learning.” I use predicting the weather as an example of how I think machine learning “should be done”, and as a result, some of the things I say about weather prediction are not cited and may be inaccurate. The point is to show that the statistical models which we call “machine learning” are improperly used in a way that doesn't further understanding of what they're modelling, which leads to a load of unintended consequences and biases.
Content warning: this post contains bits in second person. Although the word “you” is used, it's used purely for humour and is in no way referencing the reader. I've thought a lot about different topics for blog posts, and I've finally decided to start with shitposting. I hope you enjoy.
You may have heard something along the lines of “if there are infinite universes, there's one where (unrealistic expectation)”. I can with complete confidence say that's not true. In fact, mathematicians, whose literal job is to be pedantically rude, have a very rude way of putting this: it's not true, but it's almost true.