In the previous post, I described the Seasons project: a time-lapse of hundreds of pictures taken from nearly the same viewpoint over the years. The hardest challenge wasn’t taking the pictures or assembling them, but aligning them. You might have noticed the nearly part about viewpoint in the above paragraph. Indeed, it’s an approximation. I’m a human being, not a tripod. The position changes ever so slightly, and so does the exact angle.

I live close to nature. I regularly go for a run in the countryside. Over several years, during my runs, I’ve taken pictures from the same position, always roughly the same angle. I had a vague idea in the back of my mind, as an 'artistic' project. One day, I’d turn those photos into a time-lapse video, one that would show the passage of seasons across a single place. Spoiler, here’s the work in progress: However, I knew that this project would take ages.

I live close to nature. I regularly go for a run in the countryside. Over several years, during my runs, I’ve taken pictures from the same position, always roughly the same angle. I had a vague idea in the back of my mind, as an 'artistic' project. One day, I’d turn those photos into a time-lapse video, one that would show the passage of seasons across a single place. Spoiler, here’s the work in progress: However, I knew that this project would take ages.

Pattern matching is a major feature in software development. While pattern matching applies in several locations, its current usage is limited to switch case blocks. I want to compare the power of pattern matching across a couple of programming languages I’m familiar with in this post. I assume that every reader is familiar with the switch case syntax inherited from C.

GitHub offers a way to customize one’s profile by allowing one to create a README in a specific repository, named as your profile, e.g., nfrankel/nfrankel. A couple of years ago, I automated the update of my GitHub profile with up-to-date info: my latest blog posts, my upcoming talks, and the last recorded YouTube talk. I took the time to document how to do it on this blog.

I got plenty of feedback on my post about Calling Rust from Python: Hacker News/r/python/r/rust Many comments mentioned pyo3, and I should use it instead of cooking my own. Thanks to the authors, I checked: in this post, I explain what it is and how I migrated my code. What is pyo3? Rust bindings for Python, including tools for creating native Python extension modules. Running and interacting with Python code from a Rust binary is also supported.

Let’s continue our exploration of Python’s magic methods in this second part of the series. This part will focus on numbers and containers, i.e., collections. You can read the first part here. Container-related methods Python provides the usual containers, e.g., lists, sets, and dictionaries. You can use the following methods when you want to implement your own. Common methods Containers have a size.

Python provides specially-named methods: methods that the runtime interprets in a certain way, but that you need to know about. The documentation is pretty exhaustive about them, but it needs examples for beginners. The goal of this two-part series is to list these methods and provide these examples, so that I can remember them. Part 1Part 2

Java was the first language I used professionally and is the scale by which I measure other languages I learned afterward. It’s an OOP statically-typed language. Hence, Python feels a bit weird because of its dynamic typing approach. For example, Object offers methods equals(), hashCode(), and toString(). Because all other classes inherit from Object, directly or indirectly, all objects have these methods by definition.

I recently watched GOTO conferences' talk Calling Functions Across Languages by Richard Feldman. I’m afraid I have to disagree with using the term 'language' in this context. It’s a no-brainer to call Java from Kotlin or Scala or to call Java from Kotlin. Hence, in the rest, I’ll use 'stack'.

For over 20 years, I’ve developed code for the JVM, first in Java, then in Kotlin. However, the JVM is not a silver bullet, e.g., in scripts: Virtual machines incur additional memory requirementsIn many cases, the script doesn’t run long enough to gain any benefit performance-wise. The bytecode is interpreted and never compiles to native code. For these reasons, I now write my scripts in Python.