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Python Weekly

Welcome to issue 633 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

Python 3.13 gets a JIT
In late December 2023, CPython core developer Brandt Bucher submitted a little pull-request to the Python 3.13 branch adding a JIT compiler. This change, once accepted would be one of the biggest changes to the CPython Interpreter since the Specializing Adaptive Interpreter added in Python 3.11. In this post, we’re going to have a look at this JIT, what it is, how it works and what the benefits are.

Integrating Rust and Python with a Shared Library

Learn how to build computational and memory intensive functions in Rust and seamlessly integrate them into Python scripts. This video covers creating a shared Python module with Rust using pyo3, compiling the Rust code into a shared object file, importing that file in Python, and calling the high-performance Rust functions from Python.

Best practices to protect your Flask applications
Learn to secure your Flask applications effectively with our expert hands-on tutorial. Enhance security for your projects in just a few steps!

Knuckledragger: Experimenting with a Python Proof Assistant
Something that I’m tinkering with is making a proof assistant in python.

Fun With Scrapy Link Validation on CI
How can you make sure, automatically, that all the links to other internal pages within a site continue to work? In this article, we look at how to use Scrapy, a web scraping tool, and GitHub Actions, a Continuous Integration system, to accomplish this goal.

Using formatters and linters to manage a large codebase
At Kraken Tech we have a large global development team with over 500 developers, the majority of which work on the same monolithic codebase comprising more than 4 million lines of Python code. We release new code over 100 times a day, running hundreds of thousands of tests in the process. So how can we ensure high coding quality in a distributed team, with such frequent changes? And how can we make it easier for new joiners to slot right in and contribute?

A Deep Dive Into Python's functools.wraps Decorator
The article discusses the importance of the functools.wraps decorator in Python. It explains how the decorator helps to preserve metadata when wrapping one object over another, particularly when developing Python decorators. The author emphasizes the significance of this decorator due to the potential loss of valuable metadata from the encapsulated object.

SIMD in Pure Python
This post explores the concept of SWAR (SIMD Within A Register) and its variant SWAB (SIMD Within A Bigint) in the context of Python. It discusses how SWAB, which maximizes the amount of work done per VM instruction, can be beneficial in Python by reducing interpreter overhead and allowing the CPU to spend the majority of its time in fast native code that implements integer operations

Experimenting with Handwriting Recognition for The New York Times Crossword
The article discusses the exploration of handwriting recognition in The New York Times Crossword app during the MakerWeek 2023 hackathon. The project involved implementing on-device machine learning for handwriting recognition on both iOS and Android platforms, with the potential for interactive features like "Scribble-to-Erase" detection and in-app self-training mechanisms.

Retro on Viberary
The article reflects on the author's experience with Viberary, a side project aimed at finding books based on a particular vibe. The project was created to explore machine learning side projects and the intersection of search and recommendations, serving as a production-grade complement to the author's recent deep dive into embeddings.

Deploying AI-powered Django apps to Modal.com
Modal is a great place to deploy Python apps that use AI models and require GPU. Here’s how to use Modal with Django apps.

NumPy 2 is coming: preventing breakage, updating your code
NumPy 2 is coming, and it’s backwards incompatible. Learn how to keep your code from breaking, and how to upgrade.

Pandas Profiling: A Detailed Explanation
Pandas profiling or ydata-profiling as it's now called is a package offered through Python that we'll cover in this article and go over how to use it.

Data Analyst Bootcamp for Beginners (SQL, Tableau, Power BI, Python, Excel, Pandas, Projects, more)

Become a data analyst by following along with this massive course. You will learn the core topics that data analysts need to know. And along the way, you will build plenty of projects to gain hands-on experience.

Interesting Projects, Tools and Libraries

Open Interpreter
A natural language interface for computers.

Harlequin
The SQL IDE for Your Terminal.

crewAI
Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

skfolio
Python library for portfolio optimization built on top of scikit-learn.

aloha
A Low-cost Open-source Hardware System for Bimanual Teleoperation.

selfextend
An implementation of Self-Extend, to expand the context window via grouped attention

langgraph
Building language agents as graphs.

psst
Paper-based Secret Sharing Technique.

van-gonography
Hide any type of files inside a image of your choice.

Upcoming Events and Webinars

DragonPy Meetup January 2024
There will be following talks

Pardon my Python
From P to P – The different kind of p2p
Python and energetics

Bangalore Meetup January 2024
There will be following talks

Micropython - Python for microcontrollers
Time to Ditch requirements.txt and VENV, Start using Poetry now
How I made dunderhell: the ugliest python minifier

PyData Munich Meetup January 2024
There will be following talks

Different approaches for image captioning, the task of predicting captions for a given visual input
LLMs to Business - Scaling LLM applications in business processes.

PyData Southampton Meetup January 2024
There will be following talks

OpenAI's Function Calling: What is it and how can we take advantage of it?
Building a data science solution for an NGO when you don’t know what infrastructure it will run on: a case study predicting tutor supply and demand mismatch

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Python Weekly - Python Weekly - Issue 633

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