Welcome to the 62n issue of Python Monthly!
If it’s your first time here, welcome, I like you already. If you want the full back story on this monthly newsletter, head here.
The quick version: I curate and share the most important Python articles, news, resources, podcasts, and videos.
Think the Pareto Principle (80/20 rule) meeting the Python world. I give you the 20% that will get you 80% of the results.
If you're a long time reader, welcome back old friend.
Alright, let's not waste any valuable time and jump right into this month's updates.
To start off this month's newsletter, let's dive into a nice resource that lists out some of the modern best practices when using Python. Enjoy!
Need something to do this weekend? Why not use this fun Python library and build something: Deepface - A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python.
A lot has happened in the world of Large Language Models over the course of 2024. Here are the major breakthroughs in the field in the past year and some of the key things you need to be aware of. Later in this newsletter we will discuss the big news coming out this month in 2025 (including DeepSeek) in the Big Tech News section!
By the way, here is a guide on which AI tool to use in 2025.
unittest
Trick ♟This is a neat little trick if you're writing tests with unittest
. Sometimes, tests need temporary files or directories and you can do it fairly easily. Follow this guide.
Which backend library/framework should you use? This article compares two of the most popular choices and gives you a breakdown of the pros and cons.
Which one wins? Find out here.
Regular expressions, commonly known as regex, are a tool for text processing and pattern matching. In Python, the re
module offers a robust implementation of regex, allowing developers to handle complex text manipulation efficiently.
In this article, you'll learn how to use this module so you can better understand how to use regex in Python.
Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program. But don’t worry — Python offers multiple strategies to efficiently process such files without exhausting memory or performance.
Whether you’re working with server logs, massive datasets, or large text files, this guide will walk you through the best practices and techniques for managing large files in Python.
This is super interesting: If code can indeed be improved simply through iterative prompting such as asking the LLM to “make the code better” — even though it’s very silly — it would be a massive productivity increase. And if that’s the case, what happens if you iterate on the code too much? What’s the equivalent of code going cosmic?
There’s only one way to find out... read this article.
Working in large established codebases is one of the hardest things to learn as a software engineer. You can’t practice it beforehand, and personal projects can never teach you how to do it, because they’re necessarily small and from-scratch.
So what are the best practices? Here they are.
A fun little read for you: The 7 Most Influential Papers in Computer Science History. This is a good history lesson in the computer science field.
It clearly makes sense for OpenAI to at least think about its own infrastructure - at the moment it’s dependent on a somewhat fuzzy partnership with Microsoft, which is spending $80bn in the 12 months to June on datacenter capex, but building it to run AI on Azure, not just for OpenAI.
If OpenAI wants to be its own trillion dollar company (remember when it was a non-profit?) that can’t continue indefinitely. It wasn’t clear in the initial announcement, but on Wednesday the FT reported that this project would be for the exclusive use of OpenAI.
Nvidia had a busy month. First off they revealed Project Digits - a ‘personal AI supercomputer’ which looks pretty existing. Their stock went up again at the beginning of the month, but then DeepSeek came along and Nvidia set a new record: Nvidia’s $589 Billion DeepSeek Rout Is Largest in Market History.
ByteDance released an AI powered IDE: Trae.
OpenAI released Operator an agent that can go to the web to perform tasks for you. Using its own browser, it can look at a webpage and interact with it by typing, clicking, and scrolling. It is currently a research preview. They also just released o3-mini to combat DeepSeek's free model.
Google is open sourcing the popular Pebble smartwatch. Hackers are excited about this one.
If you have a child, this is going to be your new favourite thing: Bring childrend's books to life.
Enjoy pretending you're a wizard.
Trust me on this one... this is one of the coolest stories you will ever read.
For all you space nerds out there: Space Sim.
Click, be entertained, and stimulated. This is really funny.
Someone created tetris inside a PDF. Here is a good discussion on how it works.
Drop a raindrop anywhere in the world, and see where it ends up.
A simple short story for this month's best resource, but one that I hope inspires you to get 2025 started on the right foot. I hope you enjoy it as much as I did: An Unreasonable Amount of Time.
API Parrot: reverse engineer the HTTP APIs of any website, making life easier for developers looking to automate, integrate or scrape websites without public APIs.
The first agentic IDE: Windsurf... will be interesting to see how people find this new IDE. Let me know if you like it!
Here is how to acquire a new superpower today (I've tried it and it works!)
Add pets to your VS code... but I still like my Sublime Text, and so does this person.
Here is how to stop wasting time on useless websites.
See you next month everyone... also share this with your friends... pretty please! ❤️
By the way, I teach people how to code and get hired in the most efficient way possible as an Instructor at the Zero To Mastery Academy. You can see a few of our courses below or see all ZTM courses here.