Sat 12 Nov 2022

The evolution of scalable CSS

In this post, we’ll develop a deeper understanding of CSS by diving into the underlying issues that make it difficult to scale.

We’ll understand the evolution of the various CSS best practices that have emerged and changed over time.

By the end, we’ll have a good grasp on past approaches to scaling CSS on large projects, and how popular tools like Tailwind and a range of others address these issues in counter-intuitive ways.

Source: The evolution of scalable CSS.

How Regexes Work

This isn't an article about how to use regexes; you've probably seen plenty of those already. It's about how you would write a regex package from scratch, in a language like C that doesn't already have regexes.

I'll demonstrate a new module,, which implements regexes from nothing, in Perl. This will give you an idea of how regex matching is possible, although the details differ rather substantially from what Perl actually does.

Source: How Regexes Work, an article by Mark-Jason Dominus.

Adding types to a large Python codebase

Python is one of the world’s beloved programming languages. Dynamic typing makes it easy to learn and fast to build with. Its popularity has led to its use in some of the most advanced AI systems and web applications on the planet.

However, traditional dynamically typed Python code struggles at scale. Specifically:

  • Refactoring is painful. It is challenging to refactor any large dynamically typed codebase. This is because dynamically typed code can’t be processed with the sort of static analysis and refactoring tools widely available in statically typed languages (e.g. Java, C#). These tools support automatic symbol renaming, reference lookup, edit-time type-checking etc.
  • It’s hard to ensure correctness. Dynamically typed Python code is difficult to check for correctness. The best available method is to write comprehensive automated tests, but writing and maintaining a large test suite is expensive. Tests are also often too slow to provide feedback while editing in the same way as static analysis tools, allowing errors to compound for longer.

Source: Adding types to a large Python codebase, an article by Sean Mackesey.