Python Weekly (Issue 603 June 8 2023)

Python Weekly - Issue 603

June 08, 2023

Python Weekly

Welcome to issue 603 of Python Weekly. Let's get straight to the links this week.

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A weekly newsletter featuring the best hand curated news, articles, tutorials, talks, tools and libraries etc for programmers.

News

The ReversingLabs research team has identified a novel attack on PyPI using compiled Python code to evade detection — possibly the first attack to take advantage of PYC file direct execution.

Articles, Tutorials and Talks

In this video, the concept of image dithering is explained, showcasing how dithering algorithms can effectively display images on black and white monitors while preserving reasonable contrast. The video provides coding examples utilizing packages like NumPy, Pillow, and Matplotlib to demonstrate the implementation of these algorithms.

An experimental VMM for KVM written in Python. This is simply an experimental proof of concept which was hacked together enough to be able to boot OVMF, then install Linux on a disk and boot it.

R2pickledec is the first pickle decompiler to support all instructions up to protocol 5 (the current). In this post we will go over what Python pickles are, how they work and how to reverse them with Radare2 and r2pickledec. 

Watch the video to witness the transformation from average to exceptional code in Part 1 of 2 videos.

Python is a popular choice for automating anything and everything, that includes automating system administration tasks or tasks that require running other programs or interacting with operating system. There are however, many ways to achieve this in Python, most of which are arguably bad, though. So, in this article we will look at all the options you have in Python for running other processes - the bad; the good; and most importantly, the right way to do it.

The purpose of this post is to demonstrate that not all data drift impacts model performance. Making drift methods hard to trust since they tend to produce a large number of false alarms. To illustrate this point, we will train an ML model using a real-world dataset, monitor the distribution of the model's features in production, and report any data drift that might occur.

Cosine similarity proved useful in many different areas, such as in machine learning applications, natural language processing, and information retrieval. After reading this article, you will know precisely what cosine similarity is, how to run it with Python using the scikit-learn library (also known as sklearn), and when to use it. You’ll also learn how cosine similarity is related to graph databases, exploring the quickest way to utilize it.

In this LangChain Crash Course you will learn how to build applications powered by large language models. We go over all important features of this framework.

Recordings from PyCon US 2023 in Salt Lake City, UT.

Learn how you can use resample, groupby, and rolling in pandas and supercharge your workflows with Ponder!

Interesting Projects, Tools and Libraries

PyStack is a tool that uses forbidden magic to let you inspect the stack frames of a running Python process or a Python core dump, helping you quickly and easily learn what it's doing (or what it was doing when it crashed) without having to interpret nasty CPython internals.

Evaluate multiple LLMs easily.

A Hierarchical Vision Transformer without the Bells-and-Whistles.

A unified framework for 3D content generation.

Lanarky is an open-source framework to deploy LLM applications in production. It is built on top of FastAPI and comes with batteries included.

String-to-String Algorithms for Natural Language Processing.

one-click deepfake (face swap)

Chat with your documents offline using AI.

All Python versions, kept up-to-date on hourly basis using Nix.

A simple library for creating beautiful interactive prompts.

A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) using on Deep Filtering.

flood is a load testing tool for benchmarking EVM nodes over RPC

New Releases

The Jupyter contributor community is proud to announce JupyterLab 4.0, the next major release of our full-featured development environment. The package is now available on PyPI and conda-forge. You can upgrade by running pip install --upgrade jupyterlab or conda install -c conda-forge jupyterlab.

Upcoming Events and Webinars

International Obfuscated Python Code Competition. 

There will be a talk, Elasticsearch: Vector and Hybrid Search.

There will be a talk, Will PyScript replace Django? - What PyScript is and is not.

There will be following talks

  • Application of machine learning algorithms to predict leaks in pipeline

  • Improving energy efficiency of data centers through machine learning

There will be following talks

  • How open-source brings you high-quality financial market data

  • Async can be easy: scaling structured concurrency with static and dynamic analysis

There will be following talks

  • Data and AI for Good

  • Keeping it cool: Overcoming challenges when packaging ice cream with delta robots

There will be a talk, A Tour of Large Language Models: An Accessible Journey into How They Work.

There will be following talks

  • Adventures in AI

  • Operational Guidance for a Successful Data Science and AI Team

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