PythonHub

OAuth Authentication with Flask in 2023 A long time ago I wrote a tutorial on how to add logins with a social network to your Flask ... http://blog.miguelgrinberg.com/post/oauth-authentication-with-flask-in-2023

Django Model Fields With Attributes https://jacobian.org/til/django-model-fields-with-attributes/

Building a Toy Programming Language in Python I thought it would be fun to go outside of my comfort zone of web development topics and write ... http://blog.miguelgrinberg.com/post/building-a-toy-programming-language-in-python

What are your favorite extensions for VSCODE that make coding in Python easier? https://www.reddit.com/r/learnpython/comments/14lafpb/what_are_your_favorite_extensions_for_vscode_that/

generative-models Generative Models by Stability AI. https://github.com/Stability-AI/generative-models

Automating Python code quality The article emphasizes the importance of code quality in Python software development, discussing various aspects such as style consistency, code readability, testing, and documentation. It provides practical tips and best practices to improve code quality and maintainability, ultimately enhancing the overall software development process. https://blog.fidelramos.net/software/python-code-quality

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