OSRepos

awesome-c: Curated List of C Frameworks, Libraries, and Tools

awesome-c is a comprehensive, curated list of high-quality C frameworks, libraries, and resources. It serves as an invaluable guide for C developers looking for open-source tools and learning materials. This repository helps navigate the vast ecosystem of C programming by categorizing various utilities, from AI and compression to networking and testing.

Awesome Java: A Curated List of Frameworks, Libraries, and Software

Awesome Java is a highly popular and comprehensive curated list of frameworks, libraries, and software for the Java programming language. With over 48,000 stars, it serves as an invaluable resource for developers seeking high-quality tools across various domains. This repository helps Java developers discover essential components for their projects, from architecture to web frameworks.

awesome-javascript: A Curated Collection of Essential JS Resources

The `awesome-javascript` repository is a comprehensive collection of browser-side JavaScript libraries, resources, and tools. It meticulously categorizes various aspects of JavaScript development, from package managers and frameworks to data visualization and animations. Developers can use this extensive list to discover new tools and enhance their projects efficiently.

lewisjellis-awesome-lua

NVIDIA NeMo Speech: Scalable Generative AI for Speech Models

NVIDIA NeMo Speech is a powerful, scalable generative AI framework designed for researchers and developers focused on Large Language Models, Multimodal, and Speech AI. It provides tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), enabling efficient creation, customization, and deployment of new AI models using existing code and pre-trained checkpoints. This framework supports a wide range of applications, from real-time streaming ASR to high-quality multilingual TTS.

Colibri: Run 744B GLM-5.2 MoE on Consumer Machines with Pure C

Colibri is an innovative project that enables running the massive 744B-parameter GLM-5.2 Mixture-of-Experts (MoE) model on consumer-grade machines with as little as 25GB of RAM. It achieves this remarkable feat through a pure C engine with zero dependencies, streaming model experts from disk on demand. This allows users to interact with a frontier-class LLM without requiring expensive GPU hardware.