Vespa is a scalable open-source serving engine that enables users to store, compute, and rank big data at user serving time. Pyvespa provides a Python API to Vespa, allowing users to create, modify, deploy, and interact with running Vespa instances. The library's primary purpose is to facilitate faster prototyping and familiarization with Vespa features.

aiNodes Engine is a Python-based AI image/motion picture generator node engine with a live execution chain, python code editor node, and plug-in support. It offers full modularity, colored background drop, and easy node creation with IDE annotations. The project is officially supported by Deforum and incorporates various open-source projects like ComfyUI. It is designed to be flexible, with an Unreal-like execution chain, supporting features such as Deforum, Stable Diffusion, Upscalers, Kandinsky, ControlNet, and more. The engine allows for background separation, human matting/masking, compositing, drag and drop, subgraphs, and graph saving/loading from image metadata. It aims to provide a unique, controllable manner of working with a strict user-declared execution chain.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

Modus is an open-source, serverless framework for building APIs powered by WebAssembly. It simplifies integrating AI models, data, and business logic with sandboxed execution. Modus extracts metadata, compiles code with optimizations, caches compiled modules, prepares invocation plans, generates API schema, and activates endpoints. Users query the endpoint, and Modus loads compiled code into a sandboxed environment, runs code securely, queries data and AI models, and responds via API. It provides a production-ready scalable endpoint for AI-enabled apps, optimized for sub-second response times. Modus supports programming languages like AssemblyScript and Go, and can be hosted on Hypermode or any platform. Developed by Hypermode as an open-source project, Modus welcomes external contributions.

VectorVein is a no-code AI workflow software inspired by LangChain and langflow, aiming to combine the powerful capabilities of large language models and enable users to achieve intelligent and automated daily workflows through simple drag-and-drop actions. Users can create powerful workflows without the need for programming, automating all tasks with ease. The software allows users to define inputs, outputs, and processing methods to create customized workflow processes for various tasks such as translation, mind mapping, summarizing web articles, and automatic categorization of customer reviews.

Robyn is an experimental, semi-automated and open-sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. It uses various machine learning techniques to define media channel efficiency and effectivity, explore adstock rates and saturation curves. Built for granular datasets with many independent variables, especially suitable for digital and direct response advertisers with rich data sources. Aiming to democratize MMM, make it accessible for advertisers of all sizes, and contribute to the measurement landscape.

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

This repository contains core sources related to Robotics & AI. It serves as a submodule in integrated projects, providing a minimal Ubuntu-specific build system and development tests. The code originated around 2004 in Edinburgh and has grown over the years to encompass various functionalities for Robotics, ML, and AI. Users are advised to explore example projects using this bare code for a better understanding of its capabilities.

FlowTestAI is the world’s first GenAI powered OpenSource Integrated Development Environment (IDE) designed for crafting, visualizing, and managing API-first workflows. It operates as a desktop app, interacting with the local file system, ensuring privacy and enabling collaboration via version control systems. The platform offers platform-specific binaries for macOS, with versions for Windows and Linux in development. It also features a CLI for running API workflows from the command line interface, facilitating automation and CI/CD processes.

AI Exploits is a repository that showcases practical attacks against AI/Machine Learning infrastructure, aiming to raise awareness about vulnerabilities in the AI/ML ecosystem. It contains exploits and scanning templates for responsibly disclosed vulnerabilities affecting machine learning tools, including Metasploit modules, Nuclei templates, and CSRF templates. Users can use the provided Docker image to easily run the modules and templates. The repository also provides guidelines for using Metasploit modules, Nuclei templates, and CSRF templates to exploit vulnerabilities in machine learning tools.

This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

TrinityX is an open-source HPC, AI, and cloud platform designed to provide all services required in a modern system, with full customization options. It includes default services like Luna node provisioner, OpenLDAP, SLURM or OpenPBS, Prometheus, Grafana, OpenOndemand, and more. TrinityX also sets up NFS-shared directories, OpenHPC applications, environment modules, HA, and more. Users can install TrinityX on Enterprise Linux, configure network interfaces, set up passwordless authentication, and customize the installation using Ansible playbooks. The platform supports HA, OpenHPC integration, and provides detailed documentation for users to contribute to the project.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.

uni-api is a project that unifies the management of large language model APIs, allowing you to call multiple backend services through a single unified API interface, converting them all to OpenAI format, and supporting load balancing. It supports various backend services such as OpenAI, Anthropic, Gemini, Vertex, Azure, xai, Cohere, Groq, Cloudflare, OpenRouter, and more. The project offers features like no front-end, pure configuration file setup, unified management of multiple backend services, support for multiple standard OpenAI format interfaces, rate limiting, automatic retry, channel cooling, fine-grained model timeout settings, and fine-grained permission control.

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia's Chat with RTX, providing a user-friendly interface for those without a programming background. Pre-packaged with Mistral 7B, Dot ensures accessibility and simplicity right out of the box. Dot allows you to load multiple documents into an LLM and interact with them in a fully local environment. Supported document types include PDF, DOCX, PPTX, XLSX, and Markdown. Users can also engage with Big Dot for inquiries not directly related to their documents, similar to interacting with ChatGPT. Built with Electron JS, Dot encapsulates a comprehensive Python environment that includes all necessary libraries. The application leverages libraries such as FAISS for creating local vector stores, Langchain, llama.cpp & Huggingface for setting up conversation chains, and additional tools for document management and interaction.

Vespa is a scalable open-source serving engine that enables users to store, compute, and rank big data at user serving time. Pyvespa provides a Python API to Vespa, allowing users to create, modify, deploy, and interact with running Vespa instances. The library's primary purpose is to facilitate faster prototyping and familiarization with Vespa features.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.