Processing a 250 TB dataset with Coiled, Dask, and Xarray
The authors successfully processed 250TB of geospatial cloud data in 20 minutes using Xarray, Dask, and Coiled, highlighting the challenges and optimizations involved, all while keeping the cost at approximately $25. This achievement demonstrates the feasibility of large-scale data processing, exposes scalability issues, and explores cost-efficient strategies for such tasks.
Litestar is a powerful, flexible yet opinionated ASGI framework, focused on building APIs, and offers high-performance data validation and parsing, dependency injection, first-class ORM integration, authorization primitives, and much more that's needed to get applications up and running.
A set of productivity tools for Python.
Vector Embeddings Tutorial – Create an AI Assistant with GPT-4 & Natural Language Processing
Learn about vector embeddings and how to use them in your machine learning and artificial intelligence projects. Learn how to create an AI assistant with vector embeddings.
When to use classes in Python? When you repeat the same functions
In this article, we look at another heuristic for using classes in Python, with examples from real-world code, and some things to keep in mind.
How We're Building AI Search Engines using LLM Embeddings
Demo and explanation of how to use the Python sentence-transformers library to generate, store, and query LLM embeddings using the Django ORM and pgvector. This video demonstrates a prototype application that enables "AI-powered search" for job descriptions using an unstructured, English-language description of a job seeker.
Add database search with Django and HTMX
We'll create a fast and simple database search using Django and HTMX. It's easy and fast to do with HTMX. There'll be 6 steps.
How the Python Dataframe Interchange Protocol Makes Life Better
In this article, we answer three questions about the Python Dataframe Interchange Protocol: What it is + what problems it solves; how it works; and how extensively it's been adopted.
FlagOpen / FlagEmbedding
This article introduces flake8-logging, a Flake8 plugin that helps you to improve the logging in your Python code. Flake8 is a linter that checks Python code for errors and style violations. flake8-logging extends Flake8 by adding rules for checking logging code.
Deploying Django with Kamal (mrsk)
If you just want to deploy containers on a remote machine, Kamal might be a nice addition to your toolbelt. It automates many common steps when deploying containers to one or more remote machines, without introducing the complexity of something like Kubernetes or having to use a managed service.
How to Use Apple Vision Framework via PyObjC for Text Recognition
THis article discusses how to use the Vision framework via PyObjC, which allows you to use Objective-C frameworks from Python. The Vision framework is a machine learning framework that can be used to perform tasks such as face detection, object detection, and text recognition.
openai / human-eval
Code for the paper "Evaluating Large Language Models Trained on Code"
Generate captions for images with Salesforce BLIP.
Towards a new SymPy: Part 1 - Outline
This first post will outline the structure of the foundations of a computer algebra system (CAS) like SymPy, describe some problems SymPy currently has and what can be done to address them. Then subsequent posts will focus in more detail on particular components and the work that has been done and what should be done in the future.
A Python REPL with a built in AI agent and code generation.
Personalized AI SQL Agent.
Run TUIs and terminals in your browser.
Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
Converts text input or URL into knowledge graph and displays.
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Send SMS from Django application using any SMS service provider just writing a single line of code.
Finetune a LLM to speak like you based on your WhatsApp Conversations.
Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads.
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.