The story goes that one day in the winter of 1928, Joe Kennedy stopped to have his shoes shined and when the boy finished, he offered him a stock tip: “Buy Hindenburg”. Kennedy sold off his stocks right after after concluding: “You know that it is a time to sell when shoeshine boys give you stock tips. This bull market is over”. This was just before the Great Depression.

Those who do not learn history are doomed to repeat it; the saying goes. Many organisations are still paying the price for vendor lock-in and struggling to get out of “enterprise” platforms such as WebLogic, WebSphere and TIBCO, as they desperately try to break down their monoliths and become cloud native.

I was thinking about Fermat’s Little Theorem and I wondered whether this primality test could be used to generate prime numbers faster than the Sieve of Eratosthenes, which is typically used to market Haskell to imperative programmers.

The T-shirt size model is convenient for pricing software but it may cause problems if used incorrectly.

The ideal number of columns and whether to use tabs or spaces are frequent topics of debate among programmers. In this post I justify my preference for 70 columns and 2 spaces.

There is nothing more frustrating than spending time dealing with expenses since it is an activity that adds zero value to our customers and contributes nothing to our personal development. In short, doing expenses is an utter waste of time. In this post I show how I automated my expenses using Expensify and Greasemonkey.

In this article we explore the Event Sourcing Pattern and its cousins (Retroactive Event, Parallel Models and Materialised View) using a Haskell model to investigate the implications of implementing said patterns and the challenges that arise from their use. In particular, we note that the Retroactive Pattern is difficult to implement in an efficient manner whenever the possibility of inserting an arbitrary number of “missed” events between existing events is desirable.

The Command and Query Responsibility Segregation (CQRS) Pattern is a solution to the problems that are inherent to the Create, Read, Update and Delete (CRUD) approach to data handling. We use Haskell to explore the problem scope and the proposed solution described by the pattern.

The Compute Resource Consolidation Pattern is used to reduce costs by consolidating tasks when computational units are charged uniformly even in spite of low usage.

This is a Docker tutorial written in a Frequently Asked Questions (FAQ) style.