Streamlit Widgets

An Overview of Commonly Used Widgets in Streamlit.

https://www.pythonguis.com/tutorials/streamlit-widgets/

Building AI Agents in Python with Pydantic AI

In this article, you will learn how to build production-ready AI agents in Python using Pydantic AI, with structured outputs, custom tools, and dependency injection.

https://machinelearningmastery.com/building-ai-agents-in-python-with-pydantic-ai/

HTTP GET requests with the Python standard library

Alex explains how he replaced third-party HTTP clients for his simple scripts with a tiny wrapper around Python’s standard library, keeping only certifi for trust roots. The post walks through building a cleaner GET API on top of urllib.request, showing that for small local use cases the stdlib is enough even if the raw interface is clunky.

https://alexwlchan.net/2026/python-http-with-the-stdlib/

warproxxx / poly_data

Polymarket Data Retriever that fetches, processes, and structures Polymarket data including markets, order events and trades.

https://github.com/warproxxx/poly_data

pyptx

A Python DSL to write Nvidia PTX for Hopper and Blackwell in JAX and PyTorch.

https://github.com/patrick-toulme/pyptx

Learn concurrency - a deep dive into multithreading with Python

This article explains concurrency in Python including topics like multithreading, multiprocessing, race conditions, and synchronization mechanisms such as locks. We’ll then take a deep dive into switching off GIL to enable real multithreading in Python, highlighting the differences, the benefits and the gotchas with clear code examples.

https://blog.geekuni.com/2026/04/python-concurrency.html

microsoft / agent-governance-toolkit

AI Agent Governance Toolkit — Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents. Covers 10/10 OWASP Agentic Top 10.

https://github.com/microsoft/agent-governance-toolkit

A Practical Guide to Agentic Coding

Agentic coding tools like GitHub Copilot can significantly boost developer productivity when integrated thoughtfully into workflows across IDEs, terminals, and cloud environments. The key is balancing automation with control, ensuring code quality while leveraging AI to build richer workflows, including integrations with external data sources like M365.

https://www.youtube.com/watch?v=QrfsX-sW6QI

Don’t Use Boolean Flags in Python, Use Policies Instead

The Policy Pattern replaces large conditional blocks by breaking rules into small, composable components that can be combined into flexible pipelines. This approach makes code easier to extend, test, and manage, especially when dealing with feature flags and configuration changes.

https://www.youtube.com/watch?v=wYeDGkdMi3g

google-deepmind / gemma

Gemma open-weight LLM library, from Google DeepMind

https://github.com/google-deepmind/gemma

What's new in pip 26.1

pip 26.1 adds support for dependency cooldowns, experimental support for reading/installing from standard lockfiles (pylock.toml), fixes several long-standing limitations of the 2020 resolver, and drops support for Python 3.9.

https://ichard26.github.io/blog/2026/04/whats-new-in-pip-26.1/

ml-intern

An open-source ML engineer that reads papers, trains models, and ships ML models.

https://github.com/huggingface/ml-intern

honker

SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub, and scheduler

https://github.com/russellromney/honker

Easily Stream LLM Responses with Django-Bolt and PydanticAI

A guide showing how easy it is to start using django-bolt and PydanticAI agents together.

https://www.caktusgroup.com/blog/2026/04/27/django-bolt-easy-pydanticai-streaming/

Choosing a Python Logging Library in 2026

Compare Pythons standard logging module structlog and Loguru with real benchmarks OpenTelemetry integration paths and frameworkspecific guidance for Django FastAPI and Flask.

https://www.dash0.com/guides/python-logging-libraries

Databases Were Not Designed For This

The post defines defensive databases as systems designed to protect data from buggy, noisy, or autonomous applications through safeguards such as idempotency, auditability, soft deletes, controlled writes, and strict permissions. As AI agents and distributed services generate more unpredictable traffic, data stores must actively preserve integrity rather than assuming every client behave...

https://arpitbhayani.me/blogs/defensive-databases