BeamerQt

PyQt-based application to create Beamer-LaTeX Presentations.

https://github.com/acroper/BeamerQt

Flash Attention derived and coded from first principles with Triton (Python)

This video provides an in-depth, step-by-step explanation of Flash Attention, covering its derivation, implementation, and underlying concepts. The presenter explains CUDA, Triton, and autograd from scratch, then derives and codes both the forward and backward passes of Flash Attention.

https://www.youtube.com/watch?v=zy8ChVd_oTM

chonkie

The no-nonsense RAG chunking library that's lightweight, lightning-fast, and ready to CHONK your texts.

https://github.com/bhavnicksm/chonkie

pipe-operator

Elixir's pipe operator in Python.

https://github.com/Jordan-Kowal/pipe-operator

NanoDjango - single-file Django apps | uv integration

NanoDjango is a cool package that lets you build small scripts using all the power of Django, and also supports django-ninja for APIs. We'll dive into NanoDjango in this video, and will use uv and inline script metadata for dependency management.

https://www.youtube.com/watch?v=0-iuJgfQMOw

Cosmos-Tokenizer

A suite of image and video neural tokenizers.

https://github.com/NVIDIA/Cosmos-Tokenizer

The Practical Guide to Scaling Django

Most Django scaling guides focus on theoretical maximums. But real scaling isn’t about handling hypothetical millions of users - it’s about systematically eliminating bottlenecks as you grow. Here’s how to do it right, based on patterns that work in production.

https://slimsaas.com/blog/django-scaling-performance

Python dependency management is a dumpster fire

This article is all about fire safety techniques and tools. It's about how you should think about dependency management, which tools you should consider for different scenarios, and what trade offs you'll have to make. Finally, it exposes the complexity and lingering problems in the ecosystem.

https://nielscautaerts.xyz/python-dependency-management-is-a-dumpster-fire.html

Protenix

A trainable PyTorch reproduction of AlphaFold 3.

https://github.com/bytedance/Protenix

Understanding Multimodal LLMs

An introduction to the main techniques and latest models.

https://substack.com/@rasbt/p-151078631

Tutorial: How to rate limit Python async API requests

With an example that performs 100 simultaneous requests to the Etherscan API

https://elnaril.hashnode.dev/how-to-rate-limit-python-async-requests-to-etherscan-and-other-apis

Proposal for a Django project template

The author's take on what could be a project template for Django advanced usage, with modern tooling (for Python and UI dependencies, as well as configuration/environment management), but not too opinionated.

https://david.guillot.me/en/posts/tech/proposal-for-a-django-project-template/

alphafold3

AlphaFold 3 inference pipeline.

https://github.com/google-deepmind/alphafold3

TransformerEngine

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

https://github.com/NVIDIA/TransformerEngine

Everything I've learned so far about running local LLMs

A post about running large language models (LLMs) locally on a computer. It discusses what LLMs are and how to set them up to run on your own machine. The article also covers some of the limitations of LLMs, but highlights their potential for tasks like proofreading and creative writing.

https://nullprogram.com/blog/2024/11/10