This video tutorial explores the use of Flet, a Python framework inspired by Flutter, to build cross-platform applications. It guides viewers through creating a simple quiz app, demonstrating how to structure the UI, add interactive elements like buttons, and handle user input, making it accessible for developers without extensive front-end experience.

https://www.youtube.com/watch?v=4aiNStwq8oU

a Python API for your car

https://github.com/commaai/opendbc

This piece shares a personal journey of leveraging Python to tackle everyday challenges, transforming frustrating tasks into manageable projects. By automating rent calculations, tracking expenses with Google Sheets, and setting up price alerts for favorite products, the author demonstrates how Python can simplify life and free up time for more meaningful pursuits.

https://dev.to/resource_bunk_1077cab07da/i-used-python-to-solve-a-real-life-problem-heres-how-4a4b

This podcast episode features Dawn Wages, a Django developer and Python Software Foundation chair, discussing why many Python and Django developers prefer PostgreSQL. The conversation covers topics such as the Djangonaut Space mentoring program and the upcoming PyCon 2025, highlighting the strong affinity between Python, Django, and PostgreSQL.

https://www.youtube.com/watch?v=leir3xJ4Klo

This blog post discusses the importance of robust testing for Django admin sites, highlighting the challenges posed by Django's admin DSL. The author shares a parametric test approach to ensure that all admin changelist views can be rendered successfully, using pytest to automate the process and catch potential issues early.

https://jmduke.com/posts/post/django-admin-changelist-test/

This guide showcases Python as a powerful tool for open-source intelligence (OSINT), enabling users to scrape websites, track identities, and even detect deepfakes. With its simplicity, rich library ecosystem, and strong community support, Python is ideal for OSINT practitioners, whether they are journalists, law enforcement officers, or cybersecurity professionals, helping them uncover hidden information on the internet.

https://dev.to/snappytuts/python-for-osint-stalking-the-internet-like-a-pro-pa0

STUMPY is a powerful and scalable Python library for modern time series analysis

https://github.com/TDAmeritrade/stumpy

This article by Ned Batchelder discusses a significant enhancement in measuring branch coverage with coverage.py, leveraging Python's new sys.monitoring feature introduced in Python 3.12. This improvement allows for more efficient tracking of code execution by disabling repeated event notifications once an event has been recorded, making it faster than the traditional sys.settrace method used previously.

https://nedbatchelder.com/blog/202503/faster_branch_coverage_measurement.html

This analysis explores Satellogic's satellite constellation and its open data feed, known as Satellogic EarthView. The dataset contains millions of high-resolution images captured by Satellogic's Aleph-1 constellation, offering insights into global land use and environmental changes. By leveraging tools like Python, DuckDB, and QGIS, users can analyze and visualize these images to uncover valuable geospatial information.

https://tech.marksblogg.com/satellogic-open-data-feed.html

REST implementation of Django authentication system.

https://github.com/sunscrapers/djoser

This resource provides a comprehensive guide to Python interview questions, covering a wide range of topics from basic syntax and data structures to advanced concepts like decorators and metaclasses. It includes explanations and examples for each question, helping developers prepare for technical interviews and improve their understanding of Python's capabilities and applications.

https://www.datacamp.com/blog/top-python-interview-questions-and-answers

This article explains the different ways you can use Python's built-in help() function to get documentation for various elements like functions, modules, and other objects[1]. It covers the six primary uses of help(), including looking up information on symbols, keywords, and topics, as well as navigating the interactive help utility[1].
https://www.pythonmorsels.com/help-features/

This blogpost delves into ten unconventional Python noscripting methods, covering techniques such as self-destructing code, stealthy screen capture, and anti-debugging measures[1]. It offers practical code examples and explanations for these advanced noscripts, showcasing Python's versatility beyond typical applications[1].
https://dev.to/snappytuts/pythons-10-insane-underground-noscripts-you-didnt-know-exist-10hd

Create and modify Word documents with Python

https://github.com/python-openxml/python-docx

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