Andrew W. Donoho's starred repositories
openai-cookbook
Examples and guides for using the OpenAI API
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
copilot-docs
Documentation for GitHub Copilot
prompt-engineering
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
latexify_py
A library to generate LaTeX expression from Python code.
social-app
The Bluesky Social application for Web, iOS, and Android
grist-core
Grist is the evolution of spreadsheets.
superduperdb
🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.
stat_rethinking_2022
Statistical Rethinking course winter 2022
Doctor-Dignity
Doctor Dignity is an LLM that can pass the US Medical Licensing Exam. It works offline, it's cross-platform, & your health data stays private.
NeMo-Guardrails
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
Segment-and-Track-Anything
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.
pytorch-frame
Tabular Deep Learning Library for PyTorch
xland-minigrid
JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️
dask-pytorch-ddp
dask-pytorch-ddp is a Python package that makes it easy to train PyTorch models on dask clusters using distributed data parallel.