GitHub has announced the companies chosen to form the next cohort for GitHub Accelerator. Find out about this year's participating projects, all of which focus on AI.

The GitHub Accelerator scheme was introduced with the aim of building more careers and companies in open source. GitHub says the program is designed to provide financial support, mentorship, networking, training, and visibility to help participants take the next step in their open source journey—whether that's securing funding, launching a product, or turning an idea into an invention.

The winning companies get to take part in a 10-week peer-based cohort program that will include 5-10 hours of instruction, guided workshops, and homework. They will also have mentors from the community and GitHub personnel in the fields of open source, AI, and security as well as areas like fundraising and business. The participants will be able to show off their projects through GitHub channels, events and showcases including a Demo Day experience.

In financial terms, GitHub says the projects will receive a variety of support totaling nearly $400,000 in value: $40,000 in total non-dilutive sponsorship funding via GitHub Sponsors, up to $350,000 in Microsoft and technology benefits through the Microsoft for Startups Founders Hub including Azure credits to access leading AI models through Azure where eligible, access to credits and resources from Open AI, free Copilot and other GitHub products, and connection to GitHub Fund and M12, Microsoft's Venture Fund.

Last year's cohort included companies with a wide range of open source projects, but this year GitHub decided to focus closely on AI.

Here are brief details of this year's participants:

unsloth AI is working on ways to reduce the cost of fine-tuning models. GitHub says unsloth fine-tunes open source models two to five fimes faster with 70% less memory than its competitors.

Giskard is described as a testing platform for AI models that brings transparency, and accountability. It's an open source library for testing and evaluating large language models (LLMs) that is designed for data scientists and developers.

A-Frame is a framework aimed at making AR/VR and 3D content development accessible to anyone in web browsers. The developers are focusing on the integration of AI workflows like 3D Gaussian Splatting and generative AI for images and environments.

Nav2 is a robotics navigation solution that is already used in production worldwide and is the most deployed autonomous mobile robotics (AMR) navigation solution. Nav2 provides a way to deploy robotics technologies so that users can focus on building their product applications.

OpenWebUI is a user interface for AI and LLMs that is powered by a web interface that can run LLMs locally making LLMs and AI more secure and private. The Accelerator project looks to grow both its community of contributors, as well as the project's reach and impact.

LLMware.ai is a project that aims to simplify the way enterprises make RAG models, securely and sensitively. LLMWare provides a comprehensive set of tools that can be used to rapidly build industrial-grade, knowledge-based enterprise LLM applications.

LangDrive consists of plug and play APIs for LLM training. It  serves as a simple framework to train and deploy production-grade fine-tuned language models all via an API and configuration files. The aim is to improve the maintainability of codebases by abstracting the finetuning process and reducing the number of lines for finetuning from hundreds of lines to just 10 lines.

HackingBuddyGPT is made up of autonomous agents and copilots for security teams. The project acts as an autonomous hacking partner with human-in-the-loop infrastructure, a web and API testing platform, and an active directory security management platform.

Web-Check makes it easier for developers to get a complete view of a website, infrastructure, and server. The software offers AI-powered security insights based on open data from any website or server.

marimo is described as a next-generation Python notebook for AI and machine learning, that aims to provide a reproducible, maintainable, and productionizable notebook for AI/ML developers. It can be deployed as an interactive web app, executed as a script, and versioned with Git.

Talkd.ai provides a way to optimize LLMs with easy RAG deployment and management. The Unified API will enable the LLM to always have and manage context to preprocess the input and generate the prompt from memory or context. Its goal is to facilitate and disseminate the use of the RAG technique in LLMs.

The GitHub accelerator is underway now.

More Information

GitHub Accelerator

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