The reComputer Jetson Orin Beginner Guide is a comprehensive resource designed to help developers explore and harness the powerful AI computing capabilities of the NVIDIA Jetson Orin platform. The guide covers a wide range of topics, from basic tools and getting started to advanced applications in computer vision, generative AI, robotics, and more. With step-by-step tutorials and hands-on projects, users can learn to master NVIDIA's core technologies and popular AI frameworks, enabling them to innovate in AI and robotics. The guide is suitable for beginners looking to dive into AI development and build cutting-edge projects with Jetson Orin.

LiteRT is Google's open-source high-performance runtime for on-device AI, previously known as TensorFlow Lite. The repository is currently not intended for open-source development, but aims to evolve to allow direct building and contributions. LiteRT supports Python versions 3.9, 3.10, 3.11 on Linux and MacOS. It ensures compatibility with existing .tflite file extension and format, offering conversion tools and continued active development under the name LiteRT.

This repository contains a curated list of awesome AI tools that can be used for various machine learning and artificial intelligence projects. It includes tools for data preprocessing, model training, evaluation, and deployment. The list is regularly updated with new tools and resources to help developers and data scientists in their AI projects.

Gen-AI-Experiments is a structured collection of Jupyter notebooks and AI experiments designed to guide users through various AI tools, frameworks, and models. It offers valuable resources for both beginners and experienced practitioners, covering topics such as AI agents, model testing, RAG systems, real-world applications, and open-source tools. The repository includes folders with curated libraries, AI agents, experiments, LLM testing, open-source libraries, RAG experiments, and educhain experiments, each focusing on different aspects of AI development and application.

Generative AI for Beginners .NET is a hands-on course designed for .NET developers to learn how to build Generative AI applications. The repository focuses on real-world applications and live coding, providing fully functional code samples and integration with tools like GitHub Codespaces and GitHub Models. Lessons cover topics such as generative models, text generation, multimodal capabilities, and responsible use of Generative AI in .NET apps. The course aims to simplify the journey of implementing Generative AI into .NET projects, offering practical guidance and references for deeper theoretical understanding.

Policy Synth is a TypeScript class library that empowers better decision-making for governments and companies by integrating collective and artificial intelligence. It streamlines processes through multi-scale AI agent logic flows, robust APIs, and cutting-edge real-time AI-driven web applications. The tool supports organizations in generating, refining, and implementing smarter, data-informed strategies, fostering collaboration with AI to tackle complex challenges effectively.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

Kaizen is an open-source project that helps teams ensure quality in their software delivery by providing a suite of tools for code review, test generation, and end-to-end testing. It integrates with your existing code repositories and workflows, allowing you to streamline your software development process. Kaizen generates comprehensive end-to-end tests, provides UI testing and review, and automates code review with insightful feedback. The file structure includes components for API server, logic, actors, generators, LLM integrations, documentation, and sample code. Getting started involves installing the Kaizen package, generating tests for websites, and executing tests. The tool also runs an API server for GitHub App actions. Contributions are welcome under the AGPL License.

JLB AI Agent is an innovative solution built on the Solana blockchain that harnesses the power of artificial intelligence to automate complex tasks and optimize decision-making in the DeFi space. It aims to provide real-time analytics, efficient operations, and seamless integration for both newcomers and experienced crypto enthusiasts. The tool offers features like blockchain agent chat terminal, real-time streaming implementation, trading infrastructure, NFT management, AI integration, and more, empowering users with autonomous technology where AI meets the dynamic landscape of blockchain.

JLB AI Agent is an innovative solution on the Solana blockchain that leverages artificial intelligence to automate complex tasks and enhance decision-making in the DeFi space. It offers real-time analytics, efficient operations, and seamless integration for both newcomers and experienced crypto enthusiasts. With features like autonomous trading, NFT management, DeFi insights, and comprehensive ecosystem integration, JLB empowers users with cutting-edge technology to navigate the dynamic landscape of blockchain.

This repository is dedicated to learning about Low-Code Full-Stack Agentic AI Development. It provides material for building modern AI-powered applications using a low-code full-stack approach. The main tools covered are UXPilot for UI/UX mockups, Lovable.dev for frontend applications, n8n for AI agents and workflows, Supabase for backend data storage, authentication, and vector search, and Model Context Protocol (MCP) for integration. The focus is on prompt and context engineering as the foundation for working with AI systems, enabling users to design, develop, and deploy AI-driven full-stack applications faster, smarter, and more reliably.

Cherry Studio is a desktop client that supports multiple LLM providers on Windows, Mac, and Linux. It offers diverse LLM provider support, AI assistants & conversations, document & data processing, practical tools integration, and enhanced user experience. The tool includes features like support for major LLM cloud services, AI web service integration, local model support, pre-configured AI assistants, document processing for text, images, and more, global search functionality, topic management system, AI-powered translation, and cross-platform support with ready-to-use features and themes for a better user experience.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

The AI-Gateway repository explores the AI Gateway pattern through a series of experimental labs, focusing on Azure API Management for handling AI services APIs. The labs provide step-by-step instructions using Jupyter notebooks with Python scripts, Bicep files, and APIM policies. The goal is to accelerate experimentation of advanced use cases and pave the way for further innovation in the rapidly evolving field of AI. The repository also includes a Mock Server to mimic the behavior of the OpenAI API for testing and development purposes.

Magic is an open-source all-in-one AI productivity platform designed to help enterprises quickly build and deploy AI applications, aiming for a 100x increase in productivity. It consists of various AI products and infrastructure tools, such as Super Magic, Magic IM, Magic Flow, and more. Super Magic is a general-purpose AI Agent for complex task scenarios, while Magic Flow is a visual AI workflow orchestration system. Magic IM is an enterprise-grade AI Agent conversation system for internal knowledge management. Teamshare OS is a collaborative office platform integrating AI capabilities. The platform provides cloud services, enterprise solutions, and a self-hosted community edition for users to leverage its features.

Awesome Quant AI is a curated list of resources focusing on quantitative investment and trading strategies using artificial intelligence and machine learning in finance. It covers key challenges in quantitative finance, AI/ML technical fit, predictive modeling, sequential decision-making, synthetic data generation, contextual reasoning, mathematical foundations, design approach, quantitative trading strategies, tools and platforms, learning resources, books, research papers, community, and conferences. The repository aims to provide a comprehensive resource for those interested in the intersection of AI, machine learning, and quantitative finance, with a focus on extracting alpha while managing risk in financial systems.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

The Spring AI project provides a Spring-friendly API and abstractions for developing AI applications. It offers a portable client API for interacting with generative AI models, enabling developers to easily swap out implementations and access various models like OpenAI, Azure OpenAI, and HuggingFace. Spring AI also supports prompt engineering, providing classes and interfaces for creating and parsing prompts, as well as incorporating proprietary data into generative AI without retraining the model. This is achieved through Retrieval Augmented Generation (RAG), which involves extracting, transforming, and loading data into a vector database for use by AI models. Spring AI's VectorStore abstraction allows for seamless transitions between different vector database implementations.

RAGStack is an out-of-the-box solution simplifying Retrieval Augmented Generation (RAG) in GenAI apps. RAGStack includes the best open-source for implementing RAG, giving developers a comprehensive Gen AI Stack leveraging LangChain, CassIO, and more. RAGStack leverages the LangChain ecosystem and is fully compatible with LangSmith for monitoring your AI deployments.

Breadboard is a library for prototyping generative AI applications. It is inspired by the hardware maker community and their boundless creativity. Breadboard makes it easy to wire prototypes and share, remix, reuse, and compose them. The library emphasizes ease and flexibility of wiring, as well as modularity and composability.

Cloudflare-ai-web is a lightweight and easy-to-use tool that allows you to quickly deploy a multi-modal AI platform using Cloudflare Workers AI. It supports serverless deployment, password protection, and local storage of chat logs. With a size of only ~638 kB gzip, it is a great option for building AI-powered applications without the need for a dedicated server.

AppBuilder SDK is a one-stop development tool for AI native applications, providing basic cloud resources, AI capability engine, Qianfan large model, and related capability components to improve the development efficiency of AI native applications.

This repository contains community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models. Everyone is welcome to contribute, and we value everybody's contribution! There are several ways you can contribute to the Open-Source AI Cookbook: Submit an idea for a desired example/guide via GitHub Issues. Contribute a new notebook with a practical example. Improve existing examples by fixing issues/typos. Before contributing, check currently open issues and pull requests to avoid working on something that someone else is already working on.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.