This report from LWN.net details a significant performance enhancement in the CPython interpreter achieved by replacing computed goto statements with tail calls in the bytecode execution loop The change, part of the Faster CPython project, leverages modern compiler attributes to enable better code generation, yielding benchmarked speedups around 10% and is expected in Python 3.14.
https://lwn.net/Articles/1010905/

This write-up shares a personal journey of using Python to address practical, everyday problems encountered by the author, such as rent splitting and expense tracking. It details three specific projects, providing code examples for automating rent calculations, managing finances with Google Sheets, and creating product price alerts.
https://dev.to/resource_bunk_1077cab07da/i-used-python-to-solve-a-real-life-problem-heres-how-4a4b

Cirron measures performance counters (instructions executed, etc.) and traces system calls a piece of Python or Ruby code executes.

https://github.com/s7nfo/Cirron

This examination by Nelson Elhage investigates the performance of CPython's new tail-calling interpreter, finding that initial reports of 10-15% speedups were significantly inflated due to an unrelated LLVM 19 compiler regression affecting the comparison baseline. When benchmarked against properly optimized older versions or compilers, the actual performance gain attributable to the new interpreter implementation itself is more modest, typically in the 1-5% range.
https://blog.nelhage.com/post/cpython-tail-call/

This post showcases some of the most powerful and potentially hazardous Python noscripts in existence, emphasizing their capabilities to infiltrate systems, deceive humans, and even rewrite themselves. It details specific examples such as sophisticated AI-phishing generators, self-replicating malware, convincingly human AI chatbots, and automated code-writing tools.
https://dev.to/snappytuts/the-most-overpowered-python-noscripts-ever-written-159f

Python interpreter embedded in Elixir

https://github.com/livebook-dev/pythonx

This comprehensive guide explains how Python serves as a powerful tool for Open Source Intelligence (OSINT) practitioners in 2025, covering its advantages in gathering internet-based intelligence. It details Python libraries like BeautifulSoup and Scrapy for web scraping, advanced techniques such as face recognition and deepfake detection using OpenCV, and emphasizes the importance of legal and ethical considerations in OSINT work.
https://dev.to/snappytuts/python-for-osint-stalking-the-internet-like-a-pro-pa0

This tutorial explores the security vulnerabilities associated with Python's pickle module, demonstrating how it can be exploited for remote code execution by crafting malicious serialized objects. It emphasizes the critical warning never to unpickle data from untrusted sources, illustrating the risk with a practical example involving a Flask web application and a reverse shell payload.
https://davidhamann.de/2020/04/05/exploiting-python-pickle/

A collaborative note taking, wiki and documentation platform that scales. Built with Django and React. Opensource alternative to Notion or Outline.

https://github.com/suitenumerique/docs

This piece examines why certain Python libraries, often used for web scraping and automation, can lead to noscripts being blocked or blacklisted by cloud providers and websites due to aggressive activity detection or security policies. It discusses specific libraries like Scrapy and Selenium, explains the reasons for potential bans, and offers strategies such as request throttling, using proxies/VPNs, and randomizing behavior to avoid detection.
https://dev.to/snappytuts/pythons-most-banned-noscripts-getting-you-blacklisted-55n4

This blog entry by Nick Craux offers practical advice for improving the coding experience with the Cursor AI assistant, drawing from personal use and skepticism. It highlights the importance of configuring .cursorrules files, providing specific code context to the AI, and understanding the tool's limitations and strengths for different coding tasks.
https://www.nickcraux.com/blog/cursor-tips

Practice debugging, by intentionally introducing bugs into an existing codebase.

https://github.com/ehmatthes/py-bugger

This walkthrough, noscriptd "Web Scraping with Python: Learn It Fast!", demonstrates how to automate data collection from websites using Python, focusing on the BeautifulSoup and requests libraries. It covers the essential steps from fetching webpage HTML and extracting specific elements like headlines or prices to saving the data and checking robots.txt for legality.
https://dev.to/0x3d_site/web-scraping-with-python-learn-it-fast-4c6b

This essay dissects troubleshooting as a fundamental, domain-agnostic skill, defining it as the systematic process of determining and fixing the cause of unwanted system behavior. It outlines a detailed framework for effective troubleshooting, covering aspects like adopting the right mindset, understanding system flows, isolating problems, gathering information, assessing risks, and the importance of patience and detailed observation.
https://www.autodidacts.io/troubleshooting/

The fast, Pythonic way to build Model Context Protocol servers 🚀

https://github.com/jlowin/fastmcp

"The Python Debugging Playbook" outlines a systematic approach to fixing Python code, framing effective debugging as a learned skill rather than innate talent. This playbook presents a five-step process covering interpreting errors, using breakpoint(), isolating bugs, searching effectively, and structuring requests for help.
https://dev.to/0x3d_site/the-python-debugging-playbook-fix-your-code-4nbb

isd (interactive systemd) – a better way to work with systemd units

https://github.com/isd-project/isd

coredumpy saves your crash site for post-mortem debugging

https://github.com/gaogaotiantian/coredumpy

This comparison evaluates Python and JavaScript for web development, examining their respective strengths and weaknesses through framework matchups like Flask vs. Node.js and Django vs. Express. It provides code samples, performance insights, and guidance to help developers select the appropriate technology stack based on project requirements.
https://dev.to/resource_bunk_1077cab07da/python-vs-javanoscript-which-wins-for-web-dev-1gbk

This Talk Python episode features Brett Kennedy, author of Outlier Detection with Python, exploring how to identify significant anomalies in data using various Python tools and techniques. The discussion covers real-world applications from finance to astronomy, key libraries like PyOD and scikit-learn, handling large datasets, and the importance of interpretability when dealing with outliers.
https://talkpython.fm/episodes/show/497/outlier-detection-with-python

System-wide Python package discovery, validation, and allow-listing

https://github.com/fetter-io/fetter-rs