random:seed

Money is one of those areas where small omissions can generate technical debt faster than almost any other slice of an application domain. Not because “finance is hard,” but because average code likes to pretend it’s simple. Bankster is an attempt to do this thing properly.

Last night I received an e-mail in which one of my visitors asked me a few questions after reading the post about parsing phone numbers in Clojure. The question was seemingly simple and concerned a technical detail of software construction, but in reality the reader touched on a very important subject that I had been pondering years ago, and I decided to summarise it here.

Starting today, podcasts distributed under the random:stream section have a new home. This short tale — with some technical details and a surprise ending — explains why and how.

Every couple of years, there’s an increase in devices that enable individuals to measure the bioelectrical activity of their brains at home. These devices exploit the occurrence of fluctuating brain wave frequencies, which vary according to the user’s psychophysical state. Driven by a pandemic-induced fondness for my residence, I decided to purchase one of these gadgets. After several weeks, I sought to offer a subjective review of the chosen model and conduct an experiment using raw data.

Data types allow us to classify values according to various characteristics and to establish relations between the resulting classes. This helps the programmer define operations performed on data of different kinds, and it helps language mechanisms manage memory and detect certain kinds of errors. In Clojure, we deal with several interrelated type systems that we can extend, and by leveraging their polymorphic mechanisms, we are able to abstract data management and build unified information exchange interfaces.

Data structures, alongside algorithms expressed as operations, are a fundamental component of every computer program. When building applications, we choose structures that will be best suited for processing data within our adopted model.