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

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