Rapid is a web-based modern editor for OpenStreetMap. It integrates advanced mapping tools, authoritative geospatial open data, and cutting-edge technology to empower mappers at all levels to get started quickly, making accurate and fresh edits to maps. Rapid is enhanced with authoritative open data sources and AI-generated roads from the Facebook Map With AI service + buildings from Microsoft open buildings dataset to make adding and editing roads, buildings, and more quick and simple. Rapid also includes data integrity checks to ensure that new map edits are consistent and accurate.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

DataRobot AI Accelerators are code-first workflows to speed up model development, deployment, and time to value using the DataRobot API. The accelerators include approaches for specific business challenges, generative AI, ecosystem integration templates, and advanced ML and API usage. Users can clone the repo, import desired accelerators into notebooks, execute them, learn and modify content to solve their own problems.

InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.

Pieces for Developers is a closed-source Obsidian plugin designed to revolutionize coding workflows by incorporating key capabilities and favorite features directly into the Obsidian environment. The plugin, Pieces Copilot for Obsidian, enhances coding and problem-solving experiences by providing insights on code snippets, generating samples, and facilitating navigation through PRs. Users can capture, manage, share, and discover code snippets and developer materials with ease, bringing efficiency and organization to their coding experience.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

doc2plan is a browser-based application that helps users create personalized learning plans by extracting content from documents. It features a Creator for manual or AI-assisted plan construction and a Viewer for interactive plan navigation. Users can extract chapters, key topics, generate quizzes, and track progress. The application includes AI-driven content extraction, quiz generation, progress tracking, plan import/export, assistant management, customizable settings, viewer chat with text-to-speech and speech-to-text support, and integration with various Retrieval-Augmented Generation (RAG) models. It aims to simplify the creation of comprehensive learning modules tailored to individual needs.

Data Formulator is an AI-powered tool developed by Microsoft Research to help data analysts create rich visualizations iteratively. It combines user interface interactions with natural language inputs to simplify the process of describing chart designs while delegating data transformation to AI. Users can utilize features like blended UI and NL inputs, data threads for history navigation, and code inspection to create impressive visualizations. The tool supports local installation for customization and Codespaces for quick setup. Developers can build new data analysis tools on top of Data Formulator, and research papers are available for further reading.

LaVague is an open-source Large Action Model framework that uses advanced AI techniques to compile natural language instructions into browser automation code. It leverages Selenium or Playwright for browser actions. Users can interact with LaVague through an interactive Gradio interface to automate web interactions. The tool requires an OpenAI API key for default examples and offers a Playwright integration guide. Contributors can help by working on outlined tasks, submitting PRs, and engaging with the community on Discord. The project roadmap is available to track progress, but users should exercise caution when executing LLM-generated code using 'exec'.

Hof is a CLI tool that unifies data models, schemas, code generation, and a task engine. It allows users to augment data, config, and schemas with CUE to improve consistency, generate multiple Yaml and JSON files, explore data or config with a TUI, and run workflows with automatic task dependency inference. The tool uses CUE to power the DX and implementation, providing a language for specifying schemas, configuration, and writing declarative code. Hof offers core features like code generation, data model management, task engine, CUE cmds, creators, modules, TUI, and chat for better, scalable results.

ZetaForge is an open-source AI platform designed for rapid development of advanced AI and AGI pipelines. It allows users to assemble reusable, customizable, and containerized Blocks into highly visual AI Pipelines, enabling rapid experimentation and collaboration. With ZetaForge, users can work with AI technologies in any programming language, easily modify and update AI pipelines, dive into the code whenever needed, utilize community-driven blocks and pipelines, and share their own creations. The platform aims to accelerate the development and deployment of advanced AI solutions through its user-friendly interface and community support.

BeamNGpy is an official Python library providing an API to interact with BeamNG.tech, a video game focused on academia and industry. It allows remote control of vehicles, AI-controlled vehicles, dynamic sensor models, access to road network and scenario objects, and multiple clients. The library comes with low-level functions and higher-level interfaces for complex actions. BeamNGpy requires BeamNG.tech for usage and offers compatibility information for different versions. It also provides troubleshooting tips and encourages user contributions.

Blox AI is a platform that allows users to effortlessly create flowcharts and diagrams, collaborate with teams, and receive explanations from the Google Gemini model. It offers rich text editing, versatile visualizations, secure workspaces, and limited files allotment. Users can install it as an app and use it for wireframes, mind maps, and algorithms. The platform is built using Next.Js, Typescript, ShadCN UI, TailwindCSS, Convex, Kinde, EditorJS, and Excalidraw.

Raycast-G4F is a free extension that allows users to leverage powerful AI models such as GPT-4 and Llama-3 within the Raycast app without the need for an API key. The extension offers features like streaming support, diverse commands, chat interaction with AI, web search capabilities, file upload functionality, image generation, and custom AI commands. Users can easily install the extension from the source code and benefit from frequent updates and a user-friendly interface. Raycast-G4F supports various providers and models, each with different capabilities and performance ratings, ensuring a versatile AI experience for users.

CodeProject.AI Server is a standalone, self-hosted, fast, free, and open-source Artificial Intelligence microserver designed for any platform and language. It can be installed locally without the need for off-device or out-of-network data transfer, providing an easy-to-use solution for developers interested in AI programming. The server includes a HTTP REST API server, backend analysis services, and the source code, enabling users to perform various AI tasks locally without relying on external services or cloud computing. Current capabilities include object detection, face detection, scene recognition, sentiment analysis, and more, with ongoing feature expansions planned. The project aims to promote AI development, simplify AI implementation, focus on core use-cases, and leverage the expertise of the developer community.

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

Rapid is a web-based modern editor for OpenStreetMap. It integrates advanced mapping tools, authoritative geospatial open data, and cutting-edge technology to empower mappers at all levels to get started quickly, making accurate and fresh edits to maps. Rapid is enhanced with authoritative open data sources and AI-generated roads from the Facebook Map With AI service + buildings from Microsoft open buildings dataset to make adding and editing roads, buildings, and more quick and simple. Rapid also includes data integrity checks to ensure that new map edits are consistent and accurate.

Rapid is a web-based modern editor for OpenStreetMap. It integrates advanced mapping tools, authoritative geospatial open data, and cutting-edge technology to empower mappers at all levels to get started quickly, making accurate and fresh edits to maps. Rapid is enhanced with authoritative open data sources and AI-generated roads from the Facebook Map With AI service + buildings from Microsoft open buildings dataset to make adding and editing roads, buildings, and more quick and simple. Rapid also includes data integrity checks to ensure that new map edits are consistent and accurate.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.