Python Weekly (Issue 692 March 27 2025)

Author

Rahul Chaudhary
March 27, 2025

Welcome to issue 692 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

This tutorial guides viewers through building an analytics API from scratch using Python, FastAPI, SQLModel, and TimescaleDB to manage and analyze time-series data. It covers setting up a production-ready microservice, optimizing for time-based queries, and deploying the API to Railway with private networking.

This Patreon post explores how to create Python decorators that work seamlessly with both synchronous and asynchronous functions using generators. It provides a solution to avoid code duplication and prevent issues when transitioning functions between synchronous and asynchronous execution, enhancing code maintainability.

This is a brief explanation and a cookbook for using numpy.einsum, which lets us use Einstein notation to evaluate operations on multi-dimensional arrays. The focus here is mostly on einsum's explicit mode (with -> and output dimensions explicitly specified in the subscript string) and use cases common in ML papers, though I'll also briefly touch upon other patterns.

This video demonstrates how to build an AI agent using the Browser Use framework and Python that can control a browser to perform tasks like buying items on Amazon or booking tickets. The tutorial covers setup, selecting an LLM, utilizing your own browser context, and parsing information.

A walkthrough of building a RAG system using Anthropic Claude and PostgreSQL on Amazon Bedrock to make your AI app responses more accurate and context-aware.

This post recounts a time when a Python runtime project, aimed at being fast, was surprisingly bottlenecked by string indexing. The author's initial, naive implementation of str.rpartition led to excessive O(N) string indexing, highlighting the importance of understanding UTF-8 encoding and avoiding inefficient string operations.

This video is a beginner's guide to setting up pytest from scratch for Python projects, emphasizing a simple and minimalistic approach. It covers installing pytest, creating a test directory, configuring VS Code, writing basic tests, and using filtering/discovery options, concluding with practical tips for clean and maintainable tests.

Introduction to the JAX library for high-performance numerical computing in Python.

A step by step reference to building accessible web forms in Django.

Unvibe is a Python test runner that leverages LLMs to generate correct code implementations from unit tests, using a Monte Carlo Tree Search to maximize passed assertions. It iteratively refines code by feeding back test errors to the LLM, aiming to automate code generation within existing projects.

This post describes debugging a peculiar intermittent test failure in a UI testing pipeline that occurred shortly after midnight UTC. The root cause was a time discrepancy between the test runner and the browser, leading to inconsistent data requests, which was resolved by synchronizing time using an internal NTP server.


Interesting Projects, Tools, and Libraries

NVIDIA PhysicsNeMo is an open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art SciML methods for AI4science and engineering.

Lihil is a performant and productive Python web framework designed for enterprise development, emphasizing speed, strong typing, and built-in solutions for common problems. It boasts features like dependency injection, OpenAPI docs, data validation, and a message system, while maintaining ASGI compatibility and aiming for 100% test coverage.

Run LLM Agents on Ryzen AI PCs in Minutes.

StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and textual inputs to produce high-quality SVG code with remarkable precision.

Build effective agents using Model Context Protocol and simple workflow patterns.

Official PyTorch implementation of TokenSet.

FastOpenAPI is a library for generating and integrating OpenAPI schemas using Pydantic v2 and various frameworks (Falcon, Flask, Quart, Sanic, Starlette, Tornado).

Train your AI self, amplify you, bridge the world.

Differentiable geometric optics in PyTorch. Design optical systems with optimization.

Roblox Foundation Model for 3D Intelligence.

Run Claude Code on OpenAI models.

A zero-configuration tool for automatically exposing FastAPI endpoints as Model Context Protocol (MCP) tools.

Run a cloud exit assessment on your infrastructure to gain insights into the challenges and constraints of a potential cloud exit.


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