VisionCraft

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

Sarvadnya is a repository focused on interfacing custom data using Large Language Models (LLMs) through Proof-of-Concepts (PoCs) like Retrieval Augmented Generation (RAG) and Fine-Tuning. It aims to enable domain adaptation for LLMs to answer on user-specific corpora. The repository also covers topics such as Indic-languages models, 3D World Simulations, Knowledge Graphs Generation, Signal Processing, Drones, UAV Image Processing, and Floor Plan Segmentation. It provides insights into building chatbots of various modalities, preparing videos, and creating content for different platforms like Medium, LinkedIn, and YouTube. The tech stacks involved range from enterprise solutions like Google Doc AI and Microsoft Azure Language AI Services to open-source tools like Langchain and HuggingFace.

Recognize is a smart media tagging tool for Nextcloud that automatically categorizes photos and music by recognizing faces, animals, landscapes, food, vehicles, buildings, landmarks, monuments, music genres, and human actions in videos. It uses pre-trained models for object detection, landmark recognition, face comparison, music genre classification, and video classification. The tool ensures privacy by processing images locally without sending data to cloud providers. However, it cannot process end-to-end encrypted files. Recognize is rated positively for ethical AI practices in terms of open-source software, freely available models, and training data transparency, except for music genre recognition due to limited access to training data.

Morphik is an AI-native toolset designed to help developers integrate context into their AI applications by providing tools to store, represent, and search unstructured data. It offers features such as multimodal search, fast metadata extraction, and integrations with existing tools. Morphik aims to address the challenges of traditional AI approaches that struggle with visually rich documents and provide a more comprehensive solution for understanding and processing complex data.

Sourcegraph’s AI code assistant, Cody for Visual Studio, enhances developer productivity by providing a natural and intuitive way to work. It offers features like chat, auto-edit, prompts, and works with various IDEs. Cody focuses on team productivity, offering whole codebase context and shared prompts for consistency. Users can choose from different LLM models like Claude, Gemini Pro, and OpenAI's GPT. Engineered for enterprise use, Cody supports flexible deployment and enterprise security. Suitable for any programming language, Cody excels with Python, Go, JavaScript, and TypeScript code.

Awesome Generative AI is a curated list of modern Generative Artificial Intelligence projects and services. Generative AI technology creates original content like images, sounds, and texts using machine learning algorithms trained on large data sets. It can produce unique and realistic outputs such as photorealistic images, digital art, music, and writing. The repo covers a wide range of applications in art, entertainment, marketing, academia, and computer science.

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.

Symposium2023 is a project aimed at enabling Delphi users to incorporate AI technology into their applications. It provides generalized interfaces to different AI models, making them easily accessible. The project showcases AI's versatility in tasks like language translation, human-like conversations, image generation, data analysis, and more. Users can experiment with different AI models, change providers easily, and avoid vendor lock-in. The project supports various AI features like vision support and function calling, utilizing providers like Google, Microsoft Azure, Amazon, OpenAI, and more. It includes example programs demonstrating tasks such as text-to-speech, language translation, face detection, weather querying, audio transcription, voice recognition, image generation, invoice processing, and API testing. The project also hints at potential future research areas like using embeddings for data search and integrating Python AI libraries with Delphi.

This repository is a curated collection of powerful AI tools that accelerate and enhance game development. It provides tools for asset, texture, image, code generation, animation video mocap, voice generation, speech recognition, conversational models, game design, search engine, AI NPC, Python libraries, and C# libraries. These tools streamline the creation process, save time, automate tasks, and unlock creative possibilities for game developers, whether indie or part of a studio. The repository aims to speed up development and enable the creation of immersive games by leveraging cutting-edge AI technologies.

Comflowyspace is an open-source AI image and video generation tool that aims to provide a more user-friendly and accessible experience than existing tools like SDWebUI and ComfyUI. It simplifies the installation, usage, and workflow management of AI image and video generation, making it easier for users to create and explore AI-generated content. Comflowyspace offers features such as one-click installation, workflow management, multi-tab functionality, workflow templates, and an improved user interface. It also provides tutorials and documentation to lower the learning curve for users. The tool is designed to make AI image and video generation more accessible and enjoyable for a wider range of users.

Supervisely is a computer vision platform that provides a range of tools and services for developing and deploying computer vision solutions. It includes a data labeling platform, a model training platform, and a marketplace for computer vision apps. Supervisely is used by a variety of organizations, including Fortune 500 companies, research institutions, and government agencies.

Project Miyagi showcases Microsoft's Copilot Stack in an envisioning workshop aimed at designing, developing, and deploying enterprise-grade intelligent apps. By exploring both generative and traditional ML use cases, Miyagi offers an experiential approach to developing AI-infused product experiences that enhance productivity and enable hyper-personalization. Additionally, the workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.

RapidRAG is a project focused on Knowledge QA with LLM, combining Questions & Answers based on local knowledge base with a large language model. The project aims to provide a flexible and deployment-friendly solution for building a knowledge question answering system. It is modularized, allowing easy replacement of parts and simple code understanding. The tool supports various document formats and can utilize CPU for most parts, with the large language model interface requiring separate deployment.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

Meilisearch is a lightning-fast search engine that seamlessly integrates into apps, websites, and workflows. It offers features like hybrid search, search-as-you-type, typo tolerance, filtering, sorting, synonym support, geosearch, extensive language support, security management, multi-tenancy, RESTful API, AI-readiness, easy installation, deployment, and maintenance.

GLaDOS Personality Core is a project dedicated to building a real-life version of GLaDOS, an aware, interactive, and embodied AI system. The project aims to train GLaDOS voice generator, create a 'Personality Core,' develop medium- and long-term memory, provide vision capabilities, design 3D-printable parts, and build an animatronics system. The software architecture focuses on low-latency voice interactions and minimal dependencies. The hardware system includes servo- and stepper-motors, 3D printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions involve setting up a local LLM server, installing drivers, and running GLaDOS on different operating systems.

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

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

The Deep Java Library (DJL) is a framework-agnostic Java API for deep learning. It provides a unified interface to popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. DJL makes it easy to develop deep learning applications in Java, and it can be used for a variety of tasks, including image classification, object detection, natural language processing, and speech recognition.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

Nitro is a high-efficiency C++ inference engine for edge computing, powering Jan. It is lightweight and embeddable, ideal for product integration. The binary of nitro after zipped is only ~3mb in size with none to minimal dependencies (if you use a GPU need CUDA for example) make it desirable for any edge/server deployment.

The second edition of "PyTorch Practical Tutorial" was completed after 5 years, 4 years, and 2 years. On the basis of the essence of the first edition, rich and detailed deep learning application cases and reasoning deployment frameworks have been added, so that this book can more systematically cover the knowledge involved in deep learning engineers. As the development of artificial intelligence technology continues to emerge, the second edition of "PyTorch Practical Tutorial" is not the end, but the beginning, opening up new technologies, new fields, and new chapters. I hope to continue learning and making progress in artificial intelligence technology with you in the future.

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.