Python Hub Weekly Digest for 2025-10-12
This week in Python, the PyCharm Blog discussed why Python remains popular in 2025. A video highlighted the use of marimo, a Python notebook, to enhance productivity in Django projects. Other articles covered the application of SOLID design principles to Python code, strategies for effective context engineering for AI agents, and the complementary roles of DuckDB and Polars in the Modern Data Stack. Notable projects included DeepFabric for creating synthetic training data, Logly, a Rust-powered logging library for Python, and memvid, a video-based AI memory library. Happy coding and have a great week!
đź’– Most Popular
EbookFoundation / free-programming-books
đź“š Freely available programming books
Why Is Python So Popular in 2025? – The PyCharm Blog
I stopped using the Django shell because of marimo
The video explains how marimo, a modern Python notebook, enhances productivity in Django projects by integrating SQL support, coding agents, and interactivity beyond the traditional Django shell. It demonstrates using marimo for database connections, asynchronous Django ORM operations, custom admin interfaces, and reactive, interactive notebooks that combine SQL, Python, and UI elements ...
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Python Hub Weekly Digest for 2025-10-05
đź“– Articles
SOLID: Writing Better Python Without Overengineering
The video explores applying the SOLID design principles to improve Python code quality by refactoring a messy sales report script into both a clean class-based and a lean functional version. It discusses the relevance of SOLID principles in Python, showing how they enhance code modularity, testability, and maintainability while highlighting when they might lead to overengineering.
Effective context engineering for AI agents
Context is a critical but finite resource for AI agents. In this post, we explore strategies for effectively curating and managing the context that powers them.
anthropics / claude-agent-sdk-python
onyx-dot-app / onyx
Open Source AI Platform - AI Chat with advanced features that works with every LLM
bytedance / Dolphin
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
Building an AI agent from scratch in Python
How to implement a single AI agent with an LLM API and no frameworks.
DuckDB vs Polars. Wait. DuckDB and Polars.
The article emphasizes that DuckDB and Polars are not direct competitors but complementary tools in the Modern Data Stack, with each excelling in different contexts: DuckDB is best for SQL-heavy analytics and embedding as a query engine, while Polars suits end-to-end ETL pipelines and DataFrame-centric workflows. The choice depends on your problem context, team comfort, and use case rath...
Simplifying Resource Management in mssql-python through Context Manager
The article introduces context manager support in the mssql-python driver, allowing Python applications to manage SQL Server and Azure SQL resources more safely and efficiently using Python's "with" statement. This feature automates opening and closing of connections and cursors, as well as commit and rollback of transactions, reducing boilerplate code, preventing resource leaks, and ens...
PyOCI – Publish and install private Python packages using OCI/Docker registries
Pyscn – Python code quality analyzer for vibe coders
⚙️ Projects
DeepFabric
Create large-scale synthetic training data for model distillation and evaluation.
Logly
Logly is a Rust-powered, Loguru-like logging library for Python that combines the familiarity of Python’s standard logging API with high-performance logging capabilities.
search_evals
Batteries-included eval framework for search APIs.
holm
Web development framework that brings the Next.js developer experience to Python, built on FastAPI, htmy, and FastHX.
memvid
Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.
đź‘ľ Reddits
I built a full programming language interpreter in Python based on a meme
I made PyPIPlus.com — a faster way to see all dependencies of any Python package
StringWa.rs: Which Libs Make Python Strings 2-10Ă— Faster?
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.