PythonHub

Integrating Stripe Into A One-Product Django Python Shop In the first part of this series, we created a Django online shop with htmx. In this second part, we'll handle orders using Stripe. https://blog.appsignal.com/2024/09/04/integrating-stripe-into-a-one-product-django-python-shop.html

Using GPT-4o for web scraping The article discusses using GPT-4 with OpenAI's structured outputs feature to create an AI-assisted web scraper, exploring its capabilities in parsing complex tables and generating XPaths. While the author found GPT-4 effective at extracting data from various HTML tables, they also noted challenges with merged rows, high API costs, and the need for further refinements to improve accuracy... https://blancas.io/blog/ai-web-scraper/

Lesser known parts of Python standard library – Trickster Dev https://www.trickster.dev/post/lesser-known-parts-of-python-standard-library/

Why hash tables are faster? https://www.reddit.com/r/learnpython/comments/1fcj0ci/why_hash_tables_are_faster/

smartcut Cut video files with minimal recoding. https://github.com/skeskinen/smartcut

pipefunc Lightweight function pipeline (DAG) creation in pure Python for scientific workflows. https://github.com/pipefunc/pipefunc

How to Create a Pre-Commit Hook A step-by-step guide to developing your own pre-commit hook. https://stefaniemolin.com/articles/devx/pre-commit/hook-creation-guide/

Mini-Omni Mini-Omni is an open-source multimodel large language model that can hear, talk while thinking. Featuring real-time end-to-end speech input and streaming audio output conversational capabilities. https://github.com/gpt-omni/mini-omni

Classifying all of the pdfs on the internet The article describes an attempt to classify a massive dataset of 8.4 million PDFs from Common Crawl using various machine learning techniques. The author experiments with different approaches, including deep learning models and traditional machine learning methods like XGBoost, ultimately achieving the best performance with an XGBoost model trained on embeddings, reaching 85.26% accurac... https://snats.xyz/pages/articles/classifying_a_bunch_of_pdfs.html

Lessons learnt building a real-time audio application in Python https://www.vangemert.dev/#/blog/lessons-learnt-backlooper

Tinystatus: A tiny status page generated by a Python script https://github.com/harsxv/tinystatus

Multimodal Data Analysis with LLMs and Python – Tutorial The tutorial teaches how to analyze multimodal data using Large Language Models (LLMs) and Python, covering text classification, image-based question answering, audio transcription, and creating a natural language query interface for SQL databases. https://www.youtube.com/watch?v=3-4qAkFRpAk

uvtrick A fun party trick to run Python code from another venv into this one. https://github.com/koaning/uvtrick

Used Python to create public-domain US maps that can serve as desktop backgrounds https://www.reddit.com/r/Python/comments/1f29mo0/used_python_to_create_publicdomain_us_maps_that/

supertree supertree is a Python package designed to visualize decision trees in an interactive and user-friendly way within Jupyter Notebooks, Jupyter Lab, Google Colab, and any other notebooks that support HTML rendering. https://github.com/mljar/supertree

Building LLMs from the Ground Up This tutorial guides coders through the fundamentals of large language models (LLMs), explaining how they work and how to build them from scratch in PyTorch. It covers coding a small GPT-like model, its data pipeline, architecture, pretraining, and fine-tuning using open-source libraries. https://www.youtube.com/watch?v=quh7z1q7-uc

Taming the beast that is the Django ORM - An introduction The Django ORM, how it compares to raw SQL and gotchas that you should be aware of when using it https://www.davidhang.com/blog/2024-09-01-taming-the-django-orm/

kotaemon An open-source RAG-based tool for chatting with your documents. https://github.com/Cinnamon/kotaemon

Maximizing Python Code Efficiency: Strategies to Overcome Common Performance Hurdles This article talks about performance issues caused by nested loops and memory allocation issues. It provides strategies to overcome these issues while improving efficiency. https://towardsdatascience.com/maximizing-python-code-efficiency-strategies-to-overcome-common-performance-hurdles-c6292610d785

Why I Still Use Python Virtual Environments in Docker The article argues for using Python virtual environments in Docker containers, citing benefits like predictability, standardization, and easier debugging. The author contends that virtual environments provide a consistent, well-understood structure for Python applications, making communication and deployment across teams more straightforward, while also simplifying Python's import behavior. https://hynek.me/articles/docker-virtualenv/