Django 5.2, Pydantic 2.11, Python 3.14, Outlier Detection and more
with some more interesting news, articles, packages and projects
News
NVIDIA Finally Adds Native Python Support to CUDA
For years, NVIDIA’s CUDA toolkit lacked native Python support, but that’s finally changed.
Pydantic v2.11 Released!
This release brought significant performance boosts and powerful new features that can make a real difference in your projects. It might be old news, but it's definitely worth knowing.
Articles
Django: what’s new in 5.2
Django 5.2 was released on April 2nd, and it brought some exciting new features. In this article, Adam Johnson shared several highlights with examples - like automatic model imports in the shell, support for composite primary keys, a simplified way to override BoundField, etc.
# Adam Johnson
The final alpha release for Python 3.14 dropped on April 8th, which means no new features will be added from here on out. The official release is set for October - yep, Python 3.14 (pi 😉). If you're curious about what's coming—like PEP 765, PEP 758, PEP 649, performance boosts and more—check out this great breakdown video by Denis Gruzdev.
# Denis Gruzdev
AI agents are making tasks like data scraping much easier. If you're exploring options, the Browser Use package is a solid one to check out. In this video, Tim Ruscica (aka Tech With Tim) showed how to use it and how it simplifies the scraping process. This tool/package is not perfect, but it is definitely a great starting point for experimentation.
# Tim Ruscica (aka Tech With Tim)
Outlier Detection with Python
Outliers can reveal everything from fraud to fascinating patterns in your data. In this episode of Talk Python to Me, Brett Kennedy dove into outlier detection using Python world - covered many things related to it. A great listen if you're curious about spotting the unusual in your datasets.
# Brett Kennedy
Optimizing Python: Understanding Generator Mechanics, Expressions, And Efficiency
Python generators provide a clean and memory-efficient way to handle iteration. They’re especially useful for tasks like processing large datasets, handling infinite sequences or reading big files without loading everything into memory. If you want to dive deeper into how and when to use them, check out this great article by Josh Engroff.
# Josh Engroff
Interesting Packages and Projects to Explore
py-spy - Sampling profiler for Python programs
Robyn - A Super Fast Async Python Web Framework with a Rust runtime.
PyFunctional - Package for creating data pipelines with chain functional programming
airspeed velocity (asv) - A simple Python history benchmarking tool
Mininterface - A minimal interface to a Python application (GUI, TUI, CLI, web)
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