Python Weekly (Issue 640 February 29 2024)

Python Weekly - Issue 640

February 29, 2024

Python Weekly

Welcome to issue 640 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

Learn about generative models and different frameworks, investigating the production of text and visual material produced by artificial intelligence.

The article discusses the learnings from six months of working on a CodeGen pair programmer, GPT Pilot, aiming to have human developers understand the codebase and provide detailed explanations of added code to facilitate collaboration between human developers and AI in coding tasks

This video explains why dependency injection is a game-changer for your coding projects. Creating loosely coupled code is key to making the code more flexible and more maintainable. This is all possible through the implicit use of dependencies.

Step-by-step guide to deploying Mistral Large to Azure.

python-requests has been around for a long time. I've been a maintainer for many years and I share some retrospective thoughts on the project.

A post about Mamba, a recent neural architecture that can be roughly thought of as a modern recurrent neural network (RNN). The model works really well and is a legitimate competitor with the ubiquitous Transformer architecture. It has gotten a lot of attention.

The post discusses challenges and perceptions of transitioning from R to Pandas in Python, exploring reasons why Pandas might feel clunky to users accustomed to R. The author provides insights and tips to ease the transition and improve the Pandas experience.

How to address limitations of naive RAG pipelines by implementing targeted advanced RAG techniques in Python.

In this Python tutorial, we'll build a typing assistant with Mistral 7B and Ollama that's running locally. You'll also learn how to implement a hotkey listener and keyboard controller with Python. Follow along in this step-by-step coding tutorial.

This article covers how to get and manage cookies and custom headers, avoid TLS fingerprinting, recognize important HTTP headers to send in requests, and how to implement exponential-backoff HTTP request retrying.

In under 5 minutes and with only 100 lines of Python code, Rohan Rao, senior solutions architect at NVIDIA, demos how large language models (LLMs) can be developed and deployed for AI chatbot applications—without needing your own GPU infrastructure. 

The article critiques the current state of Python's dependency management, emphasizing the need for better defaults and user experience. Drawing parallels with Golang's successful design, the author advocates for a shift in mentality within the Pypa ecosystem to improve the default behavior of tools like Pip and suggests exploring alternatives if necessary.

Interesting Projects, Tools and Libraries

ingestr is a CLI tool to copy data between any databases with a single command seamlessly. 

An open, modular framework for zero-shot, language conditioned pick-and-drop tasks in arbitrary homes.

DNA foundation modeling from molecular to genome scale.

Auto Prompt is a prompt optimization framework designed to enhance and perfect your prompts for real-world use cases.

The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.

The official PyTorch implementation of Google's Gemma models.

Inspect and refine PATH environment variable on both Windows and Linux.

Mountaineer is a batteries-included web framework for Python and React.

Lightweight text-based SQL parameter binds.

hotpdf is a fast PDF parsing library to extract text and find text within PDF documents built on top of pdfminer.six

Generate Synthetic Data Using OpenAI or MistralAI.

New Releases

JupyterLab 4.1 and Notebook 7.1 are now available! These releases include several new features, bug fixes, and enhancements for extension developers. This release is compatible with extensions supporting JupyterLab 4.0 and Notebook 7.0.

This release includes the following announcements:

  • New Add Imports Code Action heuristics setting

  • Automatically start your browser when debugging Django or Flask apps

  • Shell integration for the Python REPL

  • Language support for locally running Jupyter servers

Upcoming Events and Webinars

There will be following talks

  • Building the single-source-of-truth: developing a system for large-scale entity resolution

  • The future of AI: Opensource LLMs

There will be a talk, Eclipse Insights: How AI is Transforming Solar Astronomy.

There will be following talks

  • Python for (Board) Game Design

  • Python for Designers

There will be discussion and learning about the field of Prompt Engineering.

There will be following talks

  • Going beyond Parquet's default settings – be surprised what you can get - Uwe Korn 

  • Profiling and Optimising Model Prediction Services -Paolo Rechia

There will be a talk, Developing Valuable ML Products.

There will be a talk, Unleashing the Power of Generative AI in Business Applications.

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