Popular GitHub Repositories for Apple Podcasts

Create and run high-performance macOS and Linux VMs on Apple Silicon, with built-in support for AI agents.

Run Stable Diffusion on Mac natively

Firebase SDK for Apple App Development

BibiGPT v1 · one-Click AI Summary for Audio/Video & Chat with Learning Content: Bilibili | YouTube | Tweet丨TikTok丨Dropbox丨Google Drive丨Local files | Websites丨Podcasts | Meetings | Lectures, etc. 音视频内容 AI 一键总结 & 对话：哔哩哔哩丨YouTube丨推特丨小红书丨抖音丨快手丨百度网盘丨阿里云盘丨网页丨播客丨会议丨本地文件等 (原 BiliGPT 省流神器 & AI课代表)

On-device Speech Recognition for Apple Silicon

Gradio WebUI for creators and developers, featuring key TTS (Edge-TTS, kokoro) and zero-shot Voice Cloning (E2E, F5-TTS, CosyVoice), with Whisper audio processing, RVC voice changer, YouTube download, UVR5 vocal isolation, and multilingual translation.

A course of learning LLM inference serving on Apple Silicon for systems engineers.

A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon.

Everything we actually know about the Apple Neural Engine (ANE)

Query your Apple Health data with natural language ? ?

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

The world's smartest system-wide grammar assistant; a better version of the Apple Intelligence Writing Tools. Works on Windows, Linux, & macOS, with the free Gemini API, local LLMs, & more.

MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.

A MLX port of FLUX based on the Huggingface Diffusers implementation.

An adblocker for live radio streams and podcasts. Machine learning meets Shazam.

SHARK Studio -- Web UI for SHARK+IREE High Performance Machine Learning Distribution

Neural Network built in Apple Playground using Swift

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.

TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"