Nicolas Fränkel

This is the 7th post in the Exercises in Programming Style focus series. The post of this week is special, as it’s about Object-Oriented Programming. It’s quite popular nowadays to dismiss OOP. There’s a lot of confusion around it. Some people conflate OOP with accessors (i.e. getters and setters), or shared mutable state (or even both). This is not true, as we will see in this post.

This is the 6th post in the Exercises in Programming Style focus series. Last week saw us using higher-order functions by passing them as the parameter to another function, and dynamically calling them. The parameter passing is quite nice, but it’s not easy to follow the flow of the program. This week we are going to keep those functions, but make use of them in a different way, with function composition.

This is the 5th post in the Exercises in Programming Style focus series. This week, the chapter is named 'Kick forward'. The style’s constraint is not to call a function directly, but to pass it to the next function as a parameter, to be called later. I’m not sure whether the concept of higher-order functions has its roots in Functional Programming. However, the concept itself is easy to grasp. A higher-order function can be used as a function parameter - or as a return value - of ano

This is the 4th post in the Exercises in Programming Style focus series. This week’s post will be back to fundamentals, as the constraint is to use recursion. Recursion in computer science is a method of solving a problem where the solution depends on solutions to smaller instances of the same problem (as opposed to iteration). The approach can be applied to many types of problems, and recursion is one of the central ideas of computer science.

This is the 3rd post in the Exercises in Programming Style focus series. In previous posts, we had a simple problem to handle - find and sort the 25 most frequent words from a file. Then, we had to comply with different sets of constraints regarding the code: first, no variable but a single untyped array; then, only a stack and a heap. This week, the constraint is to achieve the goal with the shortest code possible. For that, the usage of Kotlin’s stdlib is more than welcome.

This is the 2nd post in the Exercises in Programming Style focus series. Last week, we had our first taste of Exercises in Programming Style. Remember, the goal is to write a simple program, but to comply with some constraints. The previous constraint was that there was only a single variable available, an array. With a statically-typed language such as Kotlin, it required a lot of casting variables to their correct types before using them. This week, the constraint is as radical, but different.

This is the 1st post in the Exercises in Programming Style focus series. Recently, my colleague Bertrand lent me a copy of the book Exercises in Programming Style by Cristina Videira Lopes. Among all the books that sit on my reading pile, I decided to put it on top, and started reading right away. The concept behind the book is pretty simple, but very interesting: there is a problem to solve with code - search for the 25 most common words in a text file. But here’s a twist. There’s a

I manage my Jekyll blog in a Git repo. My publication process uses 2 branches: master contains all production content, and feature/newposts has the new blog posts, ready to get published, one commit per post. To publish an existing post: I check the to-be-published post in the feature/newposts branch. Then, get the associated commit. And cherry pick it in the master branch. Finally, I push.

I started working with Git some years ago, and to be honest, it was not easy to pick up. It’s a huge beast, and there are several ways that can help to tame it. On the web, most articles focus on creating command shortcuts. In general, this goes like: instead of writing git pull --rebase, let’s create a shortcut to type gpr.

This is the 4th post in the From Imperative to Functional Programming focus series. This week, I’ll first implement the Dijkstra algorithm, then migrate the code to a more functional-friendly design. Dijkstra’s algorithm allows to find the shortest path in any graph, weighted or not, directed or not. The only requirement is that weights must be positive.