Python Hub Weekly Digest for 2025-11-30
This week in Python, popular topics included an introduction to Graph Neural Networks (GNNs), a guide on using UUIDv7 with Python, Django, and PostgreSQL, and an explanation of the floodfill algorithm in Python. The Hachi project, an end-to-end search engine, and RLinf, a flexible infrastructure for post-training foundation models, were also highlighted. Interesting articles covered topics like the learning curve of Python, AI image generation with Nano Banana, and the role of heartbeats in distributed systems. Notable projects included portable_python, Ax, and Heretic. Have a great week and happy coding!
đź’– Most Popular
GNN From Scratch
The article provides an introduction to Graph Neural Networks (GNNs), explaining how graphs are represented for machine learning and introducing the mathematical intuition behind GNNs. It covers key concepts such as nodes, edges, and the message-passing mechanism, helping readers understand how GNNs learn from graph-structured data.
How to use UUIDv7 in Python, Django and PostgreSQL
Learn how to use UUIDv7 today with stable releases of Python 3.14, Django 5.2 and PostgreSQL 18. A step by step guide showing how to generate UUIDv7 in Python, store them in Django models, use PostgreSQL native functions and build time ordered primary keys without writing SQL.
Transform Data From Structured Source in PostgreSQL
Floodfill algorithm in Python
Learn how to implement and use the floodfill algorithm in Python.
RLinf
RLinf is a flexible and scalable open-source infrastructure designed for post-training foundation models (LLMs, VLMs, VLAs) via reinforcement learning.
đź“– Articles
The Qtile Window Manager: A Python-Powered Tiling Experience
Image Search App with ColPali and FastAPI
Hachi: An (Image) Search engine
The Hachi project is an end-to-end, fast, self-hosted semantic and metadata search engine designed to enable comprehensive search across all types of media by extracting independent information from distributed personal data. It prioritizes minimal external dependencies, hackability, and the integration of machine learning models to fuse deterministic and semantic attributes, aiming for ...
Learning Python Feels Easy. Until It Isn’t.
The video explains the real learning curve of Python from beginner basics to writing professional, testable code. It covers mastering Python fundamentals, writing Pythonic code, understanding types and abstractions, designing better software, and incorporating testing for maintainable and scalable Python development.
Nano Banana can be prompt engineered for extremely nuanced AI image generation
The article highlights Nano Banana’s capability for exceptionally nuanced AI image generation, leveraging a large 32,768-token context window to enable highly detailed and controlled prompts. These features allow for precise adjustments and creative experimentation, pushing the boundaries of prompt engineering in AI art.
Lazy Skills: A Token-Efficient Approach to Dynamic Agent Capabilities
The article presents a method for AI agents to progressively load capabilities on-demand through a three-level system—metadata discovery, detailed documentation loading, and executable tool registration. This approach significantly reduces token usage in large language model contexts, enhances extensibility, and improves efficiency by isolating skills in subprocesses and using keyword-ba...
Heartbeats in Distributed Systems
Heartbeats in distributed systems are periodic signals sent by nodes to indicate they are alive and functioning, enabling monitoring systems to detect failures or unresponsiveness promptly. These heartbeats help maintain system health, support fault detection, load balancing, and consistency by allowing systems to respond timely to node failures or network partitions.
Python Hub Weekly Digest for 2025-11-23
⚙️ Projects
portable_python
Self-contained Python distribution for Linux.
Ax
Ax is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.
Heretic
Fully automatic censorship removal for language models.
Depth-Anything-3
Recovering the Visual Space from Any Views.
tiny-diffusion
A character-level language diffusion model trained on Tiny Shakespeare.
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.