Python Hub Weekly Digest for 2025-10-19
This week in Python, the final version of Python 3.14.0 was released, introducing major new features and improvements. A video tutorial on designing a scalable Python project using FastAPI was shared, along with a guide on enhancing Python logs with Structlog. Articles discussed the performance of Python 3.14, the potential of reactive programming in Python, and the key updates in Python 3.14. The new AI-first Python web framework, Air, was introduced, and improvements in NumPy's type-completeness score were highlighted. Have a good week and happy coding!
💖 Most Popular
Python 3.14.0 (final) is here!
Python 3.14 introduces major new features including official support for free-threaded Python, deferred evaluation of annotations, template string literals, multiple interpreters in the standard library, and a new compression module for Zstandard. It also brings improved error messages, syntax highlighting in the REPL and debugger, an experimental JIT compiler for macOS and Windows, and ...
Anatomy of a Scalable Python Project (FastAPI)
The video explains how to design a scalable Python project using FastAPI, covering key aspects like a balanced folder structure, centralized configuration with pydantic, logging, dependency injection, and isolated testing with an in-memory database. The video also demonstrates Docker-based environment consistency, clean separation of business logic and API routing, and practical tips for...
How to Level Up Your Python Logs with Structlog
This Structlog guide covers configuration context JSON output error handling and OpenTelemetry integration to make your logs a useful signal for Observability.
uv cheatsheet with most common/useful commands
📖 Articles
Python 3.14 Is Here. How Fast Is It?
Python 3.14 delivers notable performance improvements, including up to 27% speedup in some benchmarks compared to Python 3.13, and enhanced free-threaded execution for better multithreading. The new release also adds features like syntax highlighting in the REPL and improved concurrency support.
Why Reactive Programming Hasn't Taken Off in Python (And How Signals Can Change That)
Reactive programming offers significant benefits for Python applications - it reduces bugs, simplifies complexity, and improves maintainability. Yet most Python developers avoid it. The problem isn't reactive programming itself, it's how we've been doing it. Python's reaktiv makes reactive programming as simple as spreadsheet formulas.
7 Key Python 3.14 Updates To Make Your Coding Easier, Faster, and Better
Python 3.14 has officially arrived (October 2025), and with it come some thoughtful, powerful improvements.
fastapi / fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
Python 3.14's best new features
Python 3.14 introduces practical, user-friendly features such as syntax highlighting in the REPL and debugger, improved error messages with helpful suggestions, and pathlib enhancements like copy and move methods. It also adds template string literals (t-strings) for advanced string processing, deferred evaluation of annotations, new UUID versions sortable by creation time, and better co...
Functions: a complete reference
This article serves as a complete reference for all the non-trivial things you should know about Python functions.
T-Strings: Do They Actually Make SQL Better in Python 3.14?
The video explains that t-strings are a new string format in Python 3.14 designed to help with SQL query sanitization by separating query parts and parameters for better readability and safety. While t-strings can improve managing complex queries with many parameters, for simple queries they add unnecessary complexity compared to traditional parameterized queries, making them useful main...
Pdoc – Generate API documentation for Python projects
Python, Go, Rust, TypeScript and AI with Armin Ronacher
The video features Armin Ronacher, creator of Flask and co-founder of a new startup, discussing the strengths and trade-offs of Python, Go, Rust, and TypeScript for different use cases, especially startups. He highlights how AI tools are transforming engineering work, the challenges of language migrations like Python 2 to 3, and the evolving importance of programming languages in an AI-d...
Introducing Air: A Pioneering AI-First Python Web Framework
The Air framework is a pioneering AI-first Python web framework built on FastAPI and Starlette, designed to optimize developer and AI agent productivity with features like intuitive Air Tags for HTML generation, seamless Jinja template integration, and built-in HTMX support. Inspired by frameworks like Django, Flask, and Pyramid, Air emphasizes modularity, modern developer experience, an...
Training a Deep Learning Model for Echogram Semantic Segmentation
In this tutorial we build a deep-learning pipeline for echogram segmentation using open-source tools. Echograms are two-dimensional plots of acoustic echo intensity versus time and depth recorded using sonar instruments, in our case echosounders.
Bringing NumPy's type-completeness score to nearly 90%
NumPy's type-completeness score was improved from 33% to nearly 90% through efforts by Quansight Labs and Meta's Pyrefly team, including a key one-line fix for type annotations and fully typing the MaskedArray class. These improvements enhance developer experience and enable safer downstream library code by providing better static typing coverage in NumPy's public API.
Django: one ORM to rule all databases
Comparing the Django ORM support across official database backends, so you don’t have to learn it the hard way.
PyPI+
Discover and explore 500,000+ Python packages with interactive dependency tree visualization.
TIL: Python's splitlines does more than just newlines
Python Hub Weekly Digest for 2025-10-12
MicroPythonOS – An Android-Like OS for Microcontrollers
⚙️ Projects
Tunix
A JAX-native LLM Post-Training Library.
LlamaFarm
Deploy any AI model, agents, database, RAG, and pipeline locally in minutes.
FuncToWeb
Transform any Python function into a web interface automatically.
“The G in GPU is for Graphics damnit!”: Adventures in Triton Kernels, Profiling, Parallelism and More
The blog explains how Triton, a Python-based DSL for CUDA programming now developed by OpenAI, enables writing custom GPU kernels with better performance and ease than direct CUDA. Through a case study simulating Physarum slime mold, the post showcases profiling, optimization, and how Triton can vastly outperform PyTorch by fusing operations and reducing memory overhead.
devpush
Like Vercel, but open source and for all languages.
agent-framework
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
gemimg
Lightweight wrapper for generating and editing images from Gemini 2.5 Flash Image/Nano Banana.
SINQ
A novel, fast and high-quality quantization method designed to make any Large Language Model smaller while preserving accuracy.
ProofOfThought
LLM-based reasoning using Z3 theorem proving.
👾 Reddits
Best practices for using Python & uv inside Docker
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.