Catosine's Stars
Textualize/rich
Rich is a Python library for rich text and beautiful formatting in the terminal.
grpc/grpc
The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
mlabonne/llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
reworkd/AgentGPT
🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
numpy/numpy
The fundamental package for scientific computing with Python.
HeyPuter/puter
🌐 The Internet OS! Free, Open-Source, and Self-Hostable.
tiangolo/full-stack-fastapi-template
Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
modularml/mojo
The Mojo Programming Language
ml-explore/mlx
MLX: An array framework for Apple silicon
fullstorydev/grpcurl
Like cURL, but for gRPC: Command-line tool for interacting with gRPC servers
grpc-ecosystem/awesome-grpc
A curated list of useful resources for gRPC
lmcinnes/umap
Uniform Manifold Approximation and Projection
OpenBMB/MiniCPM
MiniCPM3-4B: An edge-side LLM that surpasses GPT-3.5-Turbo.
ml-explore/mlx-examples
Examples in the MLX framework
Floorp-Projects/Floorp
All of source code of version 10 or later of Floorp Browser, the most Advanced and Fastest Firefox derivative 🦊
amazon-science/chronos-forecasting
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
danielgtaylor/python-betterproto
Clean, modern, Python 3.6+ code generator & library for Protobuf 3 and async gRPC
ml-explore/mlx-data
Efficient framework-agnostic data loading
xia-lab/MetaboAnalystR
R package for MetaboAnalyst
MoonshotAI/MoonshotAI-Cookbook
Yet another Cookbook
googleapis/proto-plus-python
Beautiful, idiomatic protocol buffers in Python
GuangTianLi/grpcalchemy
The Python micro framework for building gPRC application.
BiRG/pyopls
A Python 3 implementation of orthogonal projection to latent structures
shuzhao-li-lab/asari
asari, metabolomics data preprocessing
IntelAI/intel-xai-tools
Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.
JAEarly/MILTimeSeriesClassification
Inherently Interpretable Time Series Classification via Multiple Instance Learning (MILLET)
fpaupier/gRPC-multiprocessing
A boilerplate to use multiprocessing for your gRPC server in your Python project
HassounLab/GNN-SOM
Site-of-Metabolsim prediction using Graph Neural Networks.
shuzhao-li-lab/PythonCentricPipelineForMetabolomics
Python pipeline for metabolomics data preprocessing, QC, standardization and annotation
hlorenzo/py_ddspls
Multi (& Mono) Data-Driven Sparse PLS