I Let Python Pick My March Madness Bracket - Bracket Simulation Tutorial

This tutorial guides viewers through creating a Python program that simulates the March Madness tournament, using team seeds to generate realistic probabilities and predict potential upsets. The program utilizes Python's dataclasses and the random module to build a tournament function that determines the champion.

https://www.youtube.com/watch?v=4TFQD0ok5Ao

coredumpy

coredumpy saves your crash site for post-mortem debugging

https://github.com/gaogaotiantian/coredumpy

How we started our async python migration

Have you ever wanted to try an Async Python migration, but were unsure where to start? Read this post to find out!

https://blog.duolingo.com/async-python-migration/

Dockerpyze

Python applications to Docker, automatically.

https://github.com/nicoloboschi/dockerpyze

nova-act

Amazon Nova Act is a research preview of a new AI model for developers to build agents that take actions in web browsers.

https://github.com/aws/nova-act

Deploy Django 5.2 from Scratch. Complete Guide

This video provides a tutorial on deploying a Django project to Railway using modern deployment practices and covers setting up Python environments, Git, and configuring Django for production. It demonstrates how to use Railway's platform services to host and deploy Django applications with proper configurations, using Nixpacks or custom Dockerfiles.

https://www.youtube.com/watch?v=A4Pn2lEdoLQ

PyTorch and Python Free-Threading

This post examines multi-threaded parallel inference on PyTorch models using the new No-GIL, free-threaded version of Python. Using a simple 124M parameter GPT2 model that we train from scratch, we explore the novel new territory unlocked by free-threaded Python: parallel PyTorch model inference, where multiple threads, unimpeded by the Python GIL, attempt to generate text from a transfo...

https://trent.me/articles/pytorch-and-python-free-threading/

AI Runner

AI Runner is a local-first tool that allows you to run open-source large language models (LLM) and AI image generators (Stable Diffusion) on your own hardware, without the need for a web server or cloud service.

https://github.com/capsize-games/airunner

FastMCP

The fast, Pythonic way to build Model Context Protocol servers.

https://github.com/jlowin/fastmcp

agent-file

An open file format for serializing stateful agents with persistent memory and behavior. Share, checkpoint, and version control agents across compatible frameworks.

https://github.com/letta-ai/agent-file

Emigo

Agentic AI Development in Emacs.

https://github.com/MatthewZMD/emigo

microsoft / RD-Agent

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through our open source R&D automation tool RD-Agent, which lets AI drive data-driven AI.

https://github.com/microsoft/RD-Agent

trustcall

Tenacious tool calling built on LangGraph.

https://github.com/hinthornw/trustcall

Programming a Robotic Hand to Crawl

This video demonstrates using a robotic hand to perform gestures like rock, paper, and scissors, programmed with the help of language models, highlighting the model's ability to understand and execute these gestures even if they aren't explicitly defined in the hand's code. The creator explores how language models can abstract and translate general knowledge into specific robotic actions...

https://www.youtube.com/watch?v=57cPmzwCqd4

Training and Finetuning Reranker Models with Sentence Transformers v4

This post explains how to train and fine-tune reranker models using Sentence Transformers v4, detailing components like datasets, loss functions, and evaluators. It demonstrates that fine tuning models on specific domains outperforms general-purpose models, providing practical examples for improving relevance in information retrieval.

https://huggingface.co/blog/train-reranker

I Really HATE Brittle Python Functions

This video explains principles for writing less brittle Python code by avoiding type constraints, using value constraints inside functions, and carefully considering optional values. It also advocates for raising errors instead of returning None when a function expects to find an object by ID but cannot.

https://www.youtube.com/watch?v=g-Cytq7YDCc