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News & links about Python programming.
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Django 6.0 Is Here! CSP Nonces, Background Tasks, Partials & More

The video tutorial covers the new features introduced in Django 6.0 alpha, including built-in Content Security Policy (CSP) nonce support, simpler background task management, and reusable template partials for cleaner code. It provides practical examples and explanations for implementing these features, highlighting improvements in security, asynchronous task handling, and template desig...

https://www.youtube.com/watch?v=doAMlgrTGbE
How I used Cursor AI to migrate a Bash test suite to Python

The migration of a large Bash container test suite to Python using the Cursor AI code editor saved about 1.5 months of development time, with Cursor handling script conversion, function replacement, and automated PyTest suite generation. Although the migration was not entirely smooth and required some manual fixes, the resulting Python test suite passed tests successfully, demonstrating ...

https://developers.redhat.com/articles/2025/09/23/how-i-used-cursor-ai-migrate-bash-test-suite-python#our_real_world_results
Compiling Python to Run Anywhere

The article discusses an innovative approach to compiling Python code into cross-platform, ahead-of-time optimized machine code executables without modifying the original Python source. It details building a custom symbolic tracer, propagating types for lowering to C++, leveraging AI to generate C++ operators, and empirically optimizing performance across multiple hardware targets to ena...

https://blog.codingconfessions.com/p/compiling-python-to-run-anywhere
How Well Do New Python Type Checkers Conform? A Deep Dive into Ty, Pyrefly, and Zuban

The Python type checking landscape in 2025 includes three new Rust-based tools: Astral's ty, Meta's pyrefly, and Zuban. Ty emphasizes gradual adoption with fewer false positives, pyrefly focuses on aggressive inference to catch more issues early, and Zuban aims for seamless mypy compatibility; while conformance tests reveal differences, all show promise for real-world Python development.

https://sinon.github.io/future-python-type-checkers/
Cloud-Native Pipelines for Scientific Data Processing with Prefect and Dask

This article explains how to build scalable, cloud-native scientific data processing pipelines using Prefect for workflow orchestration and Dask for parallel computation. It covers cloud-optimized formats (like Zarr), integration with tools like xarray and echopype, and demonstrates end-to-end ETL pipelines that load, process, and store multidimensional data directly in the cloud.

https://oceanstream.io/cloud-native-data-processing-pipelines-with-prefect-and-dask/
LLM-Deflate: Extracting LLMs Into Datasets

LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...

https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets