There is a trick that is spreading through social media. If you block the claude user on GitHub, then each time you visit a GitHub repository that has commits by this user you get a banner at the top alerting you of the user's participation. It's an easy way to spot projects that have started to rely on coding agents, in this case on Claude Code specifically.

Imagine the surprise when you see that CPython, one of the most popular open-source projects in the world, is now receiving contributions from claude:

This is a short blog post to announce that I'm migrating the site in which I host my paid courses to a new platform at https://learn.miguelgrinberg.com. If you have purchased a course or ebook directly from me, this article tells you how to transfer your account to the new site.

Date and time management libraries in many programming languages are famously bad. Python's datetime module comes to mind as one of the best (worst?) examples, and so does JavaScript's Date class. It feels like these libraries could not have been made worse on purpose, or so I thought until today, when I needed to implement some date calculations in a backup rotation script written in bash.

So, if you wanted to learn how to perform date and time arithmetic in your bash scripts, you've come to the right place. Just don't blame me for the nightmares.

In this article I want to share a technique that I recently learned to display an interactive map on a website. For this, you will need just a few lines of HTML and JavaScript. This solution does not require you to sign up for any accounts or services anywhere, it is completely free and open source, and can be integrated with any front or back end web framework.

Give the demo below a try and if you like it, then keep on reading to learn how you can add a map like this one to your website in just 3 quick steps!

Like I did last year, I reserved some time during my holiday break to prepare an independent report of the Flask ecosystem in 2025.

A couple of months ago, I received a request from a random Internet user to add CSRF protection to my little web framework Microdot, and I thought it was a fantastic idea.

When I set off to do this work in early November I expected I was going to have to deal with anti-CSRF tokens, double-submit cookies and hidden form fields, pretty much the traditional elements that we have used to build a defense against CSRF for years. And I did start along this tedious route. But then I bumped into a new way some people are dealing with CSRF attacks that is way simpler, which I describe below.

You may have seen the recent reports of a malware that stole API keys, tokens and other secrets from a large number of developers. From where were these secrets stolen from? You guessed it, they were mostly stolen from environment variables.

We use environment variables to configure information that processes need to run, but this type of storage was not designed for security, so using the environment for secrets always comes with risk. Given how serious this recent attack was, I thought it would be good to write a short article describing how I manage my secrets as part of my open source work.

In November of 2024 I wrote a blog post titled "Is Python Really That Slow?", in which I tested several versions of Python and noted the steady progress the language has been making in terms of performance.

Today is the 8th of October 2025, just a day after the official release of Python 3.14. Let's rerun the benchmarks to find out how fast the new version of Python is!

In the Q&A session following my EuroPython 2025 presentation about the Microdot web framework, a member of the audience asked me what the performance of MicroPython running on a microcontroller is. This took me a bit by surprise, because I really had no way to quantify it, I just knew it was, in vague terms, not great. I never questioned the low performance, because it was never a problem for me.

My answer to the question was that microcontrollers cannot replace a computer, and that these devices are only useful for small, focused tasks that are not demanding in any way. But after returning from the conference I kept thinking about this question, which piqued my curiosity. So I decided to build a better mental image of the performance these little machines have.

In this blog post I want to share some results that compare Python code running on a few microcontroller boards that I have collected through my experiments with hardware, along with my laptop and a Raspberry Pi 4 to help put things into perspective.

People keep asking me If I use Generative AI tools for coding and what I think of them, so this is my effort to put my thoughts in writing, so that I can send people here instead of having to repeat myself every time I get the question.

From the title you already know that this isn't a pro-AI blog post. But it isn't an anti-AI post either, at least I don't think it is. There are already plenty of articles by AI promoters and AI critics, so I don't feel there is a need for me to write one more of those. While I'm definitely not neutral on the subject, in this article I'm just going to share my personal experience with these tools, from a strictly technical point of view.