The 'foundry-samples' repository serves as the main directory for official Azure AI Foundry documentation sample code and examples. It contains notebooks and code snippets for various developer tasks, offering both end-to-end examples and smaller snippets. The repository is open source, encouraging contributions and providing guidance on how to contribute.

This repository contains examples demonstrating how to use the Private AI REST API for identifying and replacing Personally Identifiable Information (PII) in text. The API supports over 50 entity types, such as Credit Card information and Social Security numbers, across 50 languages. Users can access documentation and the API reference on Private AI's website. The examples include common API call scenarios and use cases in both Python and JavaScript, with additional content related to PrivateGPT for secure work with Language Models (LLMs).

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

The mcp-fundamentals repository is a collection of fundamental concepts and examples related to microservices, cloud computing, and DevOps. It covers topics such as containerization, orchestration, CI/CD pipelines, and infrastructure as code. The repository provides hands-on exercises and code samples to help users understand and apply these concepts in real-world scenarios. Whether you are a beginner looking to learn the basics or an experienced professional seeking to refresh your knowledge, mcp-fundamentals has something for everyone.

Trubrics-sdk is a software development kit designed to facilitate the integration of analytics features into applications. It provides a set of tools and functionalities that enable developers to easily incorporate analytics capabilities, such as data collection, analysis, and reporting, into their software products. The SDK streamlines the process of implementing analytics solutions, allowing developers to focus on building and enhancing their applications' functionality and user experience. By leveraging trubrics-sdk, developers can quickly and efficiently integrate robust analytics features, gaining valuable insights into user behavior and application performance.

Generative AI Indepth Basic to Advance is a repository focused on providing tutorials and resources related to generative artificial intelligence. The repository covers a wide range of topics from basic concepts to advanced techniques in the field of generative AI. Users can find detailed explanations, code examples, and practical demonstrations to help them understand and implement generative AI algorithms. The goal of this repository is to help beginners get started with generative AI and to provide valuable insights for more experienced practitioners.

PowerApps-Samples is a repository containing sample code for Power Apps, covering various aspects such as Dataverse, model-driven apps, canvas apps, Power Apps component framework, portals, and AI Builder. It serves as a valuable resource for developers looking to explore and learn about different functionalities within Power Apps ecosystem.

StoryToolkitAI is a film editing tool that utilizes AI to transcribe, index scenes, search through footage, and create stories. It offers features such as automatic transcription, translation, story creation, speaker detection, project file management, and more. The tool works locally on your machine and integrates with DaVinci Resolve Studio 18. It aims to streamline the editing process by leveraging AI capabilities and enhancing user efficiency.

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

This repository serves as a platform for coordinating ecosystem-wide work related to free-threading topics in Python. It aims to track, understand, and provide documentation for common issues across multiple libraries. Specific project-related issues should be reported in the respective project's issue tracker. For detailed documentation on free-threading topics, visit py-free-threading.github.io.

This repository serves as a hub for Microsoft Official Build & Modernize AI Applications reference solutions and content. It provides access to projects demonstrating how to build Generative AI applications using Azure services like Azure OpenAI, Azure Container Apps, Azure Kubernetes, and Azure Cosmos DB. The solutions include Vector Search & AI Assistant, Real-Time Payment and Transaction Processing, and Medical Claims Processing. Additionally, there are workshops like the Intelligent App Workshop for Microsoft Copilot Stack, focusing on infusing intelligence into traditional software systems using foundation models and design thinking.

The NeMo Framework Launcher is a cloud-native tool designed for launching end-to-end NeMo Framework training jobs. It focuses on foundation model training for generative AI models, supporting large language model pretraining with techniques like model parallelism, tensor, pipeline, sequence, distributed optimizer, mixed precision training, and more. The tool scales to thousands of GPUs and can be used for training LLMs on trillions of tokens. It simplifies launching training jobs on cloud service providers or on-prem clusters, generating submission scripts, organizing job results, and supporting various model operations like fine-tuning, evaluation, export, and deployment.

Magic Cloud is a software development automation platform based on AI, Low-Code, and No-Code. It allows dynamic code creation and orchestration using Hyperlambda, generative AI, and meta programming. The platform includes features like CRUD generation, No-Code AI, Hyperlambda programming language, AI agents creation, and various components for software development. Magic is suitable for backend development, AI-related tasks, and creating AI chatbots. It offers high-level programming capabilities, productivity gains, and reduced technical debt.

YAO is an open-source application engine written in Golang, suitable for developing business systems, website/APP API, admin panel, and self-built low-code platforms. It adopts a flow-based programming model to implement functions by writing YAO DSL or using JavaScript. Yao allows developers to create web services by processes, creating a database model, writing API services, and describing dashboard interfaces just by JSON for web & hardware, and 10x productivity. It is based on the flow-based programming idea, developed in Go language, and supports multiple ways to expand the data stream processor. Yao has a built-in data management system, making it suitable for quickly making various management backgrounds, CRM, ERP, and other internal enterprise systems. It is highly versatile, efficient, and performs better than PHP, JAVA, and other languages.

VectorCode is a code repository indexing tool that helps users write better prompts for coding LLMs by providing information about the code repository being worked on. It includes a neovim plugin and supports multiple embedding engines. The tool enhances completion results by providing project context and improves understanding of close-source or cutting edge projects.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

This repository serves as the website for the Introduction to Data-Centric AI class. It contains lab assignments and resources for the course. Users can contribute by opening issues or submitting pull requests. The website can be built locally using Docker and Jekyll. The design is based on Missing Semester. All contents, including source code, lecture notes, and videos, are licensed under CC BY-NC-SA 4.0.

Aidoku Sources is a repository containing public sources that can be directly installed through the Aidoku application. Users can add this source list to the Aidoku app to access additional content. Contributions to the repository are welcome, and it is licensed under either the Apache License, version 2.0, or the MIT license.

The 'foundry-samples' repository serves as the main directory for official Azure AI Foundry documentation sample code and examples. It contains notebooks and code snippets for various developer tasks, offering both end-to-end examples and smaller snippets. The repository is open source, encouraging contributions and providing guidance on how to contribute.

LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.

This repository contains examples showcasing various uses of Spring AI Alibaba, from basic to advanced, and best practices for AI projects. It welcomes contributions related to Spring AI Alibaba usage examples, API usage, Spring AI usage examples, and best practices for AI projects. The project structure is designed to modularize functions for easy access and use.

Brain4J is a lightweight, performant, and open-source machine learning framework for Java. Designed with portability and speed in mind, it is optimized for high performance and ideal for those looking to implement machine learning solutions in pure Java. The framework provides tools and functionalities to facilitate the development of machine learning models within Java applications, offering ease of use and efficiency.

Coding-with-ai is a curated collection of techniques and best practices for utilizing AI coding tools to achieve transformative results in coding projects. It bridges the gap between AI coding demos and daily coding reality by providing insights into specific patterns like memory files, test-driven regeneration, and parallel AI sessions. The repository offers guidance on setting up memory files, writing detailed specs, drafting solutions before using assistants, getting multiple options, choosing stable libraries, and triggering careful planning. It also covers UI prototyping, coding practices, debugging strategies, testing methodologies, and cross-stage techniques for efficient coding with AI tools.

The UvA Deep Learning Tutorials repository contains a series of Jupyter notebooks designed to help understand theoretical concepts from lectures by providing corresponding implementations. The notebooks cover topics such as optimization techniques, transformers, graph neural networks, and more. They aim to teach details of the PyTorch framework, including PyTorch Lightning, with alternative translations to JAX+Flax. The tutorials are integrated as official tutorials of PyTorch Lightning and are relevant for graded assignments and exams.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.