Welcome to issue 689 of Python Weekly. I have a special announcement to make below.

Articles, Tutorials and Talks

An Introduction to Typescript for Pythonistas

This video explores the key differences between TypeScript and Python, focusing on TypeScript from the perspective of a Python developer. It covers topics like type systems, interfaces, callables, and the ecosystem, providing insights into how a Pythonista can approach TypeScript.

"The closer to the train station, the worse the kebab" - A "Study"

This article describes an informal "study" testing the idea that kebabs near Paris train stations are worse. Using data from OSMnx and the Google Places extension, it found no link between a kebab shop's distance from a station and its Google review rating.

How I Automated My Podcast Transcript Production With Local AI

The author automated podcast transcription using roboscribe, a Python tool that combines WhisperX for diarized transcription and a local Large Language Model (LLM) for cleaning up the transcript, significantly improving readability. By leveraging local AI models, the author maintains control and optimizes the transcription process on their own hardware, achieving high-quality results in a fraction of the time compared to manual editing.

Generics and Typeclasses in Knuckledragger

This blog post explores implementing typeclasses and overloading in Knuckledragger, a Python-based proof assistant using Z3. It discusses managing abstraction layers between Python and Z3 expressions using SortDispatch and dataclasses.

Intro to Machine Learning featuring Generative AI

The course starts with the fundamentals, covering what machine learning is, how it differs from traditional approaches, and where it’s used. It then dives deeper into the mechanics, exploring different models, algorithms, and training processes. Next, it introduces Generative AI, explaining how it creates new content, before wrapping up with the architecture of AI systems and how to design and deploy them effectively.

The features of Python's help() function

Python has a built-in help function for getting help... but what can do with help?

Low Overhead Allocation Sampling with VMProf in PyPy's GC

This article introduces a new, low-overhead method for memory profiling in PyPy by combining VMProf and the PyPy Garbage Collector. It samples memory allocations by integrating the sampling check into the bump pointer allocator check, reducing overhead and allowing adjustable precision.

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Interesting Projects, Tools, and Libraries

academic-pretraining

$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources.

GPUStack

Manage GPU clusters for running AI models.

VoiceRestore

A cutting-edge speech restoration model designed to significantly enhance the quality of degraded voice recordings.

NotaGen

NotaGen is a symbolic music generation model leveraging Large Language Models (LLMs) through pre-training on 1.6M musical pieces, fine-tuning on classical compositions, and reinforcement learning using a novel CLaMP-DPO method.

olmOCR

A toolkit for training language models to work with PDF documents in the wild.

smallpond

A lightweight data processing framework built on DuckDB and 3FS.

Wan2.1

Open and Advanced Large-Scale Video Generative Models.

Search-R1

An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL.

MLX-Audio

A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon.

PRevent

Prevent merging of malicious code in pull requests.

New Releases

Python in Visual Studio Code – March 2025 Release

This release includes the following announcements:

Upcoming Events and Webinars

San Francisco Python Meetup March 2025

There will be following talks

PuPPy Meetup March 2025

There will be following talks

PyData Manchester Meetup March 2025

There will be following talks

Cleveland PyLadies Meetup March 2025

There will be a workshop on Self-Hosting LLMs.

PyData Ann Arbor Meetup March 2025

There will be a talk, Training Speech Recognition and Generation Models at Scale.

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