Python Weekly (Issue 720 October 30 2025)

Author

Rahul Chaudhary
October 30, 2025

Welcome to issue 720 of Python Weekly. Let's get straight to the links this week.

The Simplest Way To Create and Launch AI Agents

Imagine if ChatGPT, Zapier, and Webflow all had a baby. That's Lindy.

With Lindy, you can build AI agents and apps in minutes simply by describing what you want in plain English.

From inbound lead qualification to AI-powered customer support and full-blown apps, Lindy has hundreds of agents that are ready to work for you 24/7/365.

Stop doing repetitive tasks manually. Let Lindy automate workflows, save time, and grow your business.


News
The PSF has withdrawn a $1.5 million proposal to US government grant program

The Python Software Foundation (PSF) withdrew its $1.5 million proposal to the U.S. National Science Foundation's grant program due to grant terms that required the PSF to affirm it would not promote diversity, equity, and inclusion (DEI) or ideology, which conflicted with its mission and posed financial risks. Although the proposal aimed to enhance security for Python and PyPI through automation tools, PSF prioritized its values and community trust over funding, deciding unanimously to withdraw the application.

The State of Django 2025 report from PyCharm and the Django Software Foundation highlights Django's maturity with strong community support, rising use of HTMX and Alpine.js for simpler server-rendered interactivity, and growing AI tool adoption among developers. The survey reveals Django developers are experienced professionals who favor PostgreSQL, widely use type hints, regularly update to the latest Django versions, and rely on popular third-party packages like Django REST Framework, with pytest as the preferred testing tool.


Articles, Tutorials and Talks

Learn how to use Pydantic to validate and structure data in Python using type hints, custom validators, and nested models. Pydantic simplifies data handling in web apps, pipelines, and AI tools by replacing messy manual validation with clean, reliable models.

The blog post recounts how a silent PyTorch MPS backend bug caused training loss plateaus by failing to update model weights on non-contiguous tensors, leading the author through a deep debugging journey that revealed PyTorch internals and the complexity of device-specific GPU kernel implementations. This experience taught more about PyTorch's workings than years of usage, emphasizing the challenges of hardware-specific bugs in AI training frameworks.

The wrapt library's version 2.0.0 changed its object proxy class hierarchy, breaking checks that detect if an object is already wrapped, causing repeated wrapping and performance issues. The best practice is to use custom wrapper types and traverse wrapper chains via the __wrapped__ attribute to reliably detect wrapping, emphasizing careful version pinning and cautious monkey patching in production.

The article explains that Django added async support mainly to handle I/O-bound workloads more efficiently by allowing the server to process multiple requests concurrently without blocking. However, the async features add significant complexity and have seen limited adoption because most Django applications benefit more from offloading heavy tasks to background workers rather than rewriting code to be async. Despite the impressive engineering effort, for most developers Async Django is an overcomplicated solution to a problem that rarely arises.

The PyTorch Native Agentic Stack is a scalable, PyTorch-integrated framework designed for building and deploying autonomous AI agents across thousands of GPUs. It simplifies complex distributed reinforcement learning workflows by orchestrating large-scale models, providing abstractions for services, fault tolerance, and efficient state management to accelerate AI research and deployment.

The article introduces probabilit, a Python package designed for Monte Carlo modeling that simplifies uncertainty calculations by allowing users to model equations with probability distributions and sample from them. It demonstrates key features like Latin Hypercube Sampling for efficient sampling and inducing correlations between variables, highlighting its suitability for prototyping uncertainty but noting it is not ideal for complex simulations or performance-critical applications.

Develop Specialized AI Agents with New NVIDIA Nemotron Vision, RAG, and Guardrail Models

The NVIDIA Nemotron family provides open, efficient AI models for specialized agentic AI, including advanced reasoning, vision-language understanding, retrieval, and safety guardrails. These models are optimized for various deployment needs, enabling developers to build accurate, scalable, and safe AI agents across diverse applications.

The FlashPack package dramatically speeds up PyTorch model loading by flattening all weights into a single contiguous stream, memory-mapping the file, and overlapping disk, CPU, and GPU operations with CUDA streams. This approach yields 3-6× faster loading compared to traditional methods like load_state_dict(), reducing GPU idle time and improving overall performance, especially on systems without specialized hardware.

Learn why code optimization is important and how efficient Python code improves speed, scalability, and the user experience.

Helion is a Python-embedded high-level DSL that compiles to optimized Triton kernels, blending the simplicity of PyTorch syntax with powerful autotuning to deliver high-performance, portable machine learning kernels across hardware architectures. It automates complex tasks like tensor indexing, memory management, and hardware-specific tuning, enabling developers to write efficient kernels with minimal effort while achieving performance matching or exceeding hand-optimized code.

The author recounts building the Carbn climate-action app solo, making major architectural decisions despite limited experience. The article explores both the initial MVP backend and a costly overhaul after funding, sharing lessons from these startup "war stories".

The Best Way to Share Code Between Python Apps

The video explains how to use UV workspaces to manage multiple Python applications with shared code and dependencies in a single repository. It demonstrates structuring a project with CLI and FastAPI apps, extracting shared logic into internal packages, and managing dependencies efficiently to avoid duplication and conflicting environments. The approach simplifies development and scaling of multi-app Python projects.

Inside SPy, part 1: Motivations and Goals

This is the first of a series of posts in which I will try to give a deep explanation of SPy, including motivations, goals, rules of the language, differences with Python and implementation details. This post focuses primarily on the problem space: why Python is fundamentally hard to optimize, what trade-offs existing solutions require, and where current approaches fall short. Subsequent posts in this series will explore the solutions in depth. For now, let's start with the essential question: what is SPy?

Here are all the videos for the conference, brought to you by the EuroPython 2025 Team and the EuroPython Society.


Interesting Projects, Tools, and Libraries

Five AIs battle for NASDAQ 100 supremacy. Zero human input. Pure competition.

Beautiful Python async. It is for running high latency async tasks like web requests and database queries concurrently in the same way as asyncio, but with a drastically improved user experience.

NVIDIA Isaac Sim™ is an open-source application on NVIDIA Omniverse for developing, simulating, and testing AI-driven robots in realistic virtual environments.

ValueCell is a community-driven, multi-agent platform for financial applications.

Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.

The absolute trainer to light up AI agents.

Personal Finance Data Interface for Power Users (supporting backends like Monarch Money)

Rich-powered CLI that breaks each HTTP request into DNS, connect, TLS, wait, and transfer phases with waterfall timelines, compact summaries, or metrics-only output.

Create and Run Linux Workflows without Limits. Short for "nine" / "yaml" / "no-code" / "automation".

Yet another static analysis tool for Python codebases written in Python that detects dead code + common security flaws created by ai. Faster and better than the rest :) also, who let the dawgs out?

PyTorch-native post-training at scale.

A realtime celery task monitoring and management system.


New Releases

The pip 25.3 release is the final major pip update of 2025, featuring the removal of non-PEP 517 package build support and non-PEP 660 editable installs, meaning pip no longer calls legacy setup.py commands. It adds the new --build-constraint option for specifying build-time constraints separately, improves caching and metadata handling, supports editable requirements as Direct URLs, and is now built with flit-core instead of setuptools, along with various bug fixes and quality of life improvements.


Upcoming Events and Webinars
PyData Pittsburgh Meetup November 2025

There will be a talk, Bodo DataFrames: A Fast and Scalable HPC-Based Drop-In Replacement for Pandas.

There will be following talks

  • Using Hugging Face's smolagents framework to create a Fantasy Premier League AI Agent

  • No More Boilerplate: Data-Driven Testing Made Easy

  • Mise and easy: managing environments the fun way

  • USearch: The Engine Behind Most New Search & RAG Pipelines

The November edition of IndyPy explores how Python brings structure to chaos and intelligence to your workflows. From powerful data validation with Pydantic to building real-world AI agents that interact with internal systems, you'll discover tools and patterns that help you wrangle messy inputs, streamline complex logic, and unlock the full potential of Python in modern development.

There will be a talk, Python -> English -> Code: Viewing LLMs as Information Compression.

There will be a talk, Effective Experimental Design and Data Analysis in Biosciences.


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