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

This feature dives into actionable techniques for improving Python code quality, addressing common issues like nested loops, long functions, and unclear conditionals. It provides practical refactoring examples using list comprehensions, modular functions, ternary operators, type hints, and the Loguru library for better logging.
https://dev.to/resource_bunk_1077cab07da/your-python-code-is-ugly-heres-how-to-fix-it-40dm

This recounting by Max Bernstein details a real-world performance optimization saga from developing a custom Python runtime, where a seemingly innocuous string function bottlenecked Django performance. The analysis reveals how a naive Python implementation of str.rpartition led to excessive, costly UTF-8 indexing operations, emphasizing the need to look beyond surface-level profiler results to find the true cause of slowdowns.
https://bernsteinbear.com/blog/silly-perf/

Use Typer (type hints) to define the interface for your Django management commands.

https://github.com/django-commons/django-typer

This demonstration walks through creating a functional Python compiler using Python, explaining the core steps from source code to execution. It breaks down tokenization, Abstract Syntax Tree (AST) generation, bytecode compilation, and interpretation with a simple virtual machine.
https://dev.to/resource_bunk_1077cab07da/how-i-built-a-python-compiler-yes-really-1dgp

Python wrapper for Wikipedia

https://github.com/martin-majlis/Wikipedia-API

This Pybites exploration delves into Python generators, explaining how the yield statement enables memory-efficient, on-demand value production crucial for large datasets. It details the mechanics of generator functions, the conciseness of generator expressions, and the advantages of their lazy evaluation and state preservation capabilities.
https://pybit.es/articles/generator-mechanics-expressions-and-efficiency/

Phil Eaton’s recent blogpost explores how to embed Python within Rust for the purpose of running tests, offering a hands-on guide to setting up a Rust-based test runner that can execute Python noscripts and even expose Rust functions to Python code. This practical walkthrough highlights the flexibility of combining Rust’s performance with Python’s noscripting capabilities for more dynamic and parallel test scenarios.

https://www.enterprisedb.com/blog/embedding-python-rust-tests

A Self-adaptation Framework🐙 that adapts LLMs for unseen tasks in real-time!

https://github.com/SakanaAI/self-adaptive-llms

Installing Python dependencies with pip can be frustratingly slow, especially in CI pipelines or when building Docker images. This guide discusses how to speed up the process by focusing on using pre-built wheels instead of source distributions, leveraging package caching, disabling bytecode compilation when appropriate, and considering faster alternatives like uv, a Rust-based package installer that parallelizes downloads and skips bytecode compilation by default.

https://pythonspeed.com/articles/faster-pip-installs

OpenTelemetry is an open source, vendor-neutral observability framework that provides standardized APIs, libraries, and tools to collect telemetry data-such as metrics, logs, and traces-from your application, allowing you to monitor different systems and platforms seamlessly and future-proof your monitoring setup. To add OpenTelemetry to a Django application, you can use either automatic or manual instrumentation: automatic instrumentation involves installing packages like opentelemetry-instrumentation-django and configuring environment variables and your Django settings to export data to an observability backend (such as Elastic), while manual instrumentation gives you finer control by adding tracing and metrics code directly to your views and models, enabling custom monitoring of application behavior and performance.

https://allthingsopen.org/articles/what-is-opentelemetry-add-django-application

David Guillot’s experiment investigates how web push notifications, when combined with Progressive Web Apps (PWAs), can offer a compelling alternative to traditional mobile apps for user engagement. By leveraging Django and packages like django-webpush and django-pwa, the article demonstrates a practical approach to implementing reliable, native-feeling notifications on both desktop and mobile-highlighting UX design considerations, technical constraints (such as iOS requiring PWA installation for notifications), and offering a live demo to gather community feedback on usability and device impact.

https://david.guillot.me/en/posts/tech/web-push-notifications-an-experiment/