Python Hub Weekly Digest for 2023-10-22
💖 Most Popular
Python in Visual Studio Code – October 2023 Release
This release includes the following announcements:
RegisterFields in Django
An explanation of a Django model field that returns an instance of a class depending on a key.
Nevergrad: Python toolbox for performing gradient-free optimization
LeptonAI
A Pythonic framework to simplify AI service building.
RealtimeSTT
A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription. Designed for real-time applications like voice assistants.
📖 Articles
aiwaves-cn / agents
An Open-source Framework for Autonomous Language Agents
Python Type Hints: pyastgrep case study
The author shares their experience adding type hints to Python code in their tool pyastgrep. They discuss the challenges and benefits of using static type checking and interactive programming help to catch errors and improve code readability.
tairov / llama2.mojo
Inference Llama 2 in one file of pure 🔥
Python Hub Weekly Digest for 2023-10-15
Build Your First Pytorch Model In Minutes!
In this video we will learn through doing! Build your very first PyTorch model that can classify images of playing cards.
How to Use an LLM in a SaaS Platform
The video will walk you through how large language models are being used in a SaaS platform called Learntail. Learntail is an easy-to-use AI-powered quiz-generating tool.
Gsplat: CUDA accelerated rasterization of gaussians with Python bindings
⚙️ Projects
ziggy-pydust
A toolkit for building Python extensions in Zig.
genai-stack
This GenAI application stack will get you started building your own GenAI application in no time. The demo applications can serve as inspiration or as a starting point.
streaming-llm
Efficient Streaming Language Models with Attention Sinks.
torch2jax
Run PyTorch in JAX.
The Elegance of Modular Data Processing with Python’s Pipeline Approach
Diving into the intricacies of data processing can often feel like navigating an intricate labyrinth. We build these elaborate processes, only to leave them untouched for fear of breaking them. But what if we could improve it? Here's my perspective on crafting a more maintainable, modular data processing workflow in Python which leans into the "pipe and filter" architectural pattern.
LLM-scientific-feedback
Can large language models provide useful feedback on research papers? A large-scale empirical analysis.
swiss_army_llama
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
👾 Reddits
Interesting developers to follow?
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.