PythonHub

Automata The Future is Self-Written. https://github.com/emrgnt-cmplxty/Automata

Caching in Django with Redis A step-by-step guide on implementing caching with Redis in Django. https://fly.io/django-beats/caching-in-django-with-redis/

A Tale of Debugging: The Competitive Programmer Approach Have the computer find the bugs for you. https://albexl.substack.com/p/a-tale-of-debugging-the-competitive

PromtEngineer / localGPT Chat with your documents on your local device using GPT models. No data leaves your device and 100% private. https://github.com/PromtEngineer/localGPT

XingangPan / DragGAN Official Code for DragGAN (SIGGRAPH 2023) https://github.com/XingangPan/DragGAN

When NumPy is too slow What do you do when your NumPy code isn’t fast enough? We’ll discuss the options, from Numba to JAX to manual optimizations. https://pythonspeed.com/articles/numpy-is-slow/

embedchain Framework to easily create LLM powered bots over any dataset. https://github.com/embedchain/embedchain

Building Real-time Machine Learning Foundations at Lyft The article highlights Lyft's efforts in developing real-time machine learning foundations to enhance their platform's performance and user experience. It explores the challenges faced and the strategies employed to build scalable and reliable machine learning systems within the context of a ride-sharing company. https://eng.lyft.com/building-real-time-machine-learning-foundations-at-lyft-6dd99b385a4e

Geospatial Data in your Graph In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j. https://www.youtube.com/watch?v=djMsdSxvd2E

ChristianLempa / videos This is my video documentation. Here you'll find code-snippets, technical documentation, templates, command reference, and whatever is needed for all my YouTube Videos. https://github.com/ChristianLempa/videos

Python Hub Weekly Digest for 2023-07-02 https://pythonhub.dev/digest/2023-07-02/

Why Mac for Python dev? https://www.reddit.com/r/learnpython/comments/14faxsa/why_mac_for_python_dev/

Designing Pythonic library APIs The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand. https://benhoyt.com/writings/python-api-design/

The Annotated S4 This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture. https://srush.github.io/annotated-s4

arguably arguably turns functions into command line interfaces (CLIs). arguably has a tiny API and is extremely easy to integrate. https://github.com/treykeown/arguably

Remote Interactive Debugging of Python Applications Running in Kubernetes In this tutorial we will create a setup for remote debugging of Python applications running in Kubernetes, which will allow you to set breakpoints, step through code, and interactively debug your applications without any change to your code or deployment. https://martinheinz.dev/blog/99

localrf An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. https://github.com/facebookresearch/localrf

Authlib: Python Authentication https://docs.authlib.org/en/latest/index.html

Playing with Streamlit and LLMs. This post describes how to use Streamlit to build a simple interface for interacting with large language models (LLMs). It also includes code examples that show how to use Streamlit to display text, images, and tables, and to interact with LLMs through prompts and queries. https://lethain.com/streamlit-llms/

TruLens Evaluation and Tracking for LLM Experiments. https://github.com/truera/trulens