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

Introducing DjangoVer

The article introduces DjangoVer, a versioning system for Django-related packages that aligns the package version with the latest supported Django feature release. It provides clarity on compatibility, signaling maintenance and compatibility status through the version number while addressing limitations of traditional versioning systems like Semantic Versioning.

https://www.b-list.org/weblog/2024/nov/18/djangover/

Is async django ready for prime time?

Explore async Django's readiness for production use, its benefits, challenges, and how AI workloads can leverage its capabilities effectively.

https://jonathanadly.com/is-async-django-ready-for-prime-time

EasyAnimate

An End-to-End Solution for High-Resolution and Long Video Generation Based on Transformer Diffusion.

https://github.com/aigc-apps/EasyAnimate

Is Python Really That Slow?

The post explores Python's perceived slowness, highlighting that it stems from its interpreted nature and focus on developer productivity rather than raw performance. By leveraging tools like C extensions, async programming, or just-in-time compilers, developers can often overcome performance concerns effectively.

https://blog.miguelgrinberg.com/post/is-python-really-that-slow