🧠 LLM Summarizer App LLM Summarizer App is a simple and powerful web application that allows users to generate abstractive summaries of long articles using OpenAI's GPT models (like GPT-3.5 or GPT…

Various Docker Compose examples of selfhosted FOSS and proprietary projects.

How to use Tailscale with a custom domain via Cloudflare and reverse proxy on a Synology NAS

[CVPR 2024] Official repository for "Tactile-Augmented Radiance Fields".

Mesh TensorFlow: Model Parallelism Made Easier

VisionOS App + Python Library to stream hand tracking data from Vision Pro, video/audio stream to Vision Pro.

The Modular Platform (includes MAX & Mojo)

Mac-style shortcut keys for Linux & Windows.

OpenRGB integration for Home Assistant

official repo for the paper "EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata"

📺 Home Assistant SamsungTV Smart integration with SmartThings API Support.

Expander card for HomeAssistant

Fast disk usage analyzer with console interface written in Go

Public Repo of documentation and scripts how to use GUFI to generate reports to identify data suitable for archive

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

A General NeRF Acceleration Toolbox in PyTorch.

Implementation of Axial attention - attending to multi-dimensional data efficiently

TEACh is a dataset of human-human interactive dialogues to complete tasks in a simulated household environment.

🖥 Control your display's brightness & volume on your Mac as if it was a native Apple Display. Use Apple Keyboard keys or custom shortcuts. Shows the native macOS OSDs.

Code of the CVPR 2021 Oral paper: A Recurrent Vision-and-Language BERT for Navigation

🚀 State-of-the-art parsers for natural language.

Reinforcement Learning environments based on the 1993 game Doom

Python 3.8+ toolbox for submitting jobs to Slurm

[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

State of AI

Minimal tutorial on packing and unpacking sequences in pytorch

Code for 'Chasing Ghosts: Instruction Following as Bayesian State Tracking' published at NeurIPS 2019