Plurrrr

I've decided some weeks ago to (try to) adopt the method of David Allen named "Get Things Done" (GTD). A lot of people were giving very positive feedback about this method and there are a lot of related resources online, including several Emacs/org-mode setup. This helped me a lot to design my own setup since I did not read the book, but I found a nice summary:

1. Capture anything that crosses your mind, nothing is too big or small.
2. Clarify what you’ve captured into clear and concrete action steps.
3. Organize and put everything into the right place.
4. Review, update, and revise your lists.
5. Engage Get to work on the important stuff.

Last week two blog posts, titled Rust Is Hard, Or: The Misery of Mainstream Programming and (async) Rust doesn’t have to be hard, sparked a lot of discussions on Hacker News and Reddit.

This topic is close to my heart. After all, at lunatic we are building a Rust runtime with the performance characteristics of async Rust, but without the issues of async Rust. It brings Rust back to its pre-1.0 days when it still used user space threads as a concurrency model.

In this blog post I would like to compare both approaches and show the differences between the programming models and runtime characteristics.

When you’re processing data with Pandas, so-called “vectorized” operations can significantly speed up your code. Or at least, that’s the theory.

In practice, in some situations Pandas vectorized operations can actually make your code slower, or at least no faster. And they can also significantly increase memory usage.

Let’s dig in and see what vectorization means in Pandas, when and why it helps, and when it’s harmful.