Frigate Home Assistant Integration provides a rich media browser with thumbnails and navigation, sensor entities for camera FPS, detection FPS, process FPS, skipped FPS, and objects detected, binary sensor entities for object motion, camera entities for live view and object detected snapshot, switch entities for clips, detection, snapshots, and improve contrast, and support for multiple Frigate instances. It offers easy installation via HACS and manual installation options for advanced users. Users need to configure the `mqtt` integration for Frigate to work. Additionally, media browsing and a companion Lovelace card are available for enhanced user experience. Refer to the main Frigate documentation for detailed installation instructions and usage guidance.

MaixCDK (Maix C/CPP Development Kit) is a C/C++ development kit that integrates practical functions such as AI, machine vision, and IoT. It provides easy-to-use encapsulation for quickly building projects in vision, artificial intelligence, IoT, robotics, industrial cameras, and more. It supports hardware-accelerated execution of AI models, common vision algorithms, OpenCV, and interfaces for peripheral operations. MaixCDK offers cross-platform support, easy-to-use API, simple environment setup, online debugging, and a complete ecosystem including MaixPy and MaixVision. Supported devices include Sipeed MaixCAM, Sipeed MaixCAM-Pro, and partial support for Common Linux.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

AdalFlow is a library designed to help developers build and optimize Large Language Model (LLM) task pipelines. It follows a design pattern similar to PyTorch, offering a light, modular, and robust codebase. Named in honor of Ada Lovelace, AdalFlow aims to inspire more women to enter the AI field. The library is tailored for various GenAI applications like chatbots, translation, summarization, code generation, and autonomous agents, as well as classical NLP tasks such as text classification and named entity recognition. AdalFlow emphasizes modularity, robustness, and readability to support users in customizing and iterating code for their specific use cases.

E.D.D.I (Enhanced Dialog Driven Interface) is an enterprise-certified chatbot middleware that offers advanced prompt and conversation management for Conversational AI APIs. Developed in Java using Quarkus, it is lean, RESTful, scalable, and cloud-native. E.D.D.I is highly scalable and designed to efficiently manage conversations in AI-driven applications, with seamless API integration capabilities. Notable features include configurable NLP and Behavior rules, support for multiple chatbots running concurrently, and integration with MongoDB, OAuth 2.0, and HTML/CSS/JavaScript for UI. The project requires Java 21, Maven 3.8.4, and MongoDB >= 5.0 to run. It can be built as a Docker image and deployed using Docker or Kubernetes, with additional support for integration testing and monitoring through Prometheus and Kubernetes endpoints.

Applio is a VITS-based Voice Conversion tool focused on simplicity, quality, and performance. It features a user-friendly interface, cross-platform compatibility, and a range of customization options. Applio is suitable for various tasks such as voice cloning, voice conversion, and audio editing. Its key features include a modular codebase, hop length implementation, translations in over 30 languages, optimized requirements, streamlined installation, hybrid F0 estimation, easy-to-use UI, optimized code and dependencies, plugin system, overtraining detector, model search, enhancements in pretrained models, voice blender, accessibility improvements, new F0 extraction methods, output format selection, hashing system, model download system, TTS enhancements, split audio, Discord presence, Flask integration, and support tab.

Mirascope is an LLM toolkit for lightning-fast, high-quality development. Building with Mirascope feels like writing the Python code you’re already used to writing.

A multifunctional, intelligent, personalized, scalable, easy to build, and fully automated multi platform intelligent live video editing and publishing robot. ChopperBot is a comprehensive AI tool that automatically analyzes and slices the most interesting clips from popular live streaming platforms, generates and publishes content, and manages accounts. It supports plugin DIY development and hot swapping functionality, making it easy to customize and expand. With ChopperBot, users can quickly build their own live video editing platform without the need to install any software, thanks to its visual management interface.

Docling is a tool that bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. It can convert any PDF document to JSON or Markdown format, understand detailed page layout, reading order, recover table structures, extract metadata such as title, authors, references, and language, and optionally apply OCR for scanned PDFs. The tool is designed to be stable, lightning fast, and suitable for macOS and Linux environments.

The `xaitk-saliency` package is an open source Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, designed for analytics and autonomy applications. It provides saliency algorithms for various image understanding tasks such as image classification, image similarity, object detection, and reinforcement learning. The toolkit targets data scientists and developers who aim to incorporate visual saliency explanations into their workflow or product, offering both direct accessibility for experimentation and modular integration into systems and applications through Strategy and Adapter patterns. The package includes documentation, examples, and a demonstration tool for visual saliency generation in a user-interface.

Generative AI models have shown tremendous usefulness in increasing accessibility and automation of a wide range of tasks. This repository contains the `biochatter` Python package, a generic backend library for the connection of biomedical applications to conversational AI. It aims to provide a common framework for deploying, testing, and evaluating diverse models and auxiliary technologies in the biomedical domain. BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs.

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

Tock is an open conversational AI platform for building bots. It offers a natural language processing open source stack compatible with various tools, a user interface for building stories and analytics, a conversational DSL for different programming languages, built-in connectors for text/voice channels, toolkits for custom web/mobile integration, and the ability to deploy anywhere in the cloud or on-premise with Docker.

AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.

MineStudio is a simple and efficient Minecraft development kit for AI research. It contains tools and APIs for developing Minecraft AI agents, including a customizable simulator, trajectory data structure, policy models, offline and online training pipelines, inference framework, and benchmarking automation. The repository is under development and welcomes contributions and suggestions.

FlashInfer is a library for Language Languages Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving and inference, and delivers state-the-art performance across diverse scenarios.

Frigate Home Assistant Integration provides a rich media browser with thumbnails and navigation, sensor entities for camera FPS, detection FPS, process FPS, skipped FPS, and objects detected, binary sensor entities for object motion, camera entities for live view and object detected snapshot, switch entities for clips, detection, snapshots, and improve contrast, and support for multiple Frigate instances. It offers easy installation via HACS and manual installation options for advanced users. Users need to configure the `mqtt` integration for Frigate to work. Additionally, media browsing and a companion Lovelace card are available for enhanced user experience. Refer to the main Frigate documentation for detailed installation instructions and usage guidance.

AirSonos is a Home Assistant Community Add-on that provides AirPlay capabilities for Sonos (and UPnP) players. It bridges the compatibility gap between Apple devices using AirPlay and Sonos players by creating virtual AirPlay devices for Sonos players in the network. The add-on may also work for other UPnP players like newer Samsung televisions. It is based on the AirConnect project, offering a solution for streaming audio to Sonos devices.

aiohomekit is a Python library that implements the HomeKit protocol for controlling HomeKit accessories using asyncio. It is primarily used with Home Assistant, targeting the same versions of Python and following their code standards. The library is still under development and does not offer API guarantees yet. It aims to match the behavior of real HAP controllers, even when not strictly specified, and works around issues like JSON formatting, boolean encoding, header sensitivity, and TCP packet splitting. aiohomekit is primarily tested with Phillips Hue and Eve Extend bridges via Home Assistant, but is known to work with many more devices. It does not support BLE accessories and is intended for client-side use only.

Frigate Home Assistant Integration provides a rich media browser with thumbnails and navigation, sensor entities for camera FPS, detection FPS, process FPS, skipped FPS, and objects detected, binary sensor entities for object motion, camera entities for live view and object detected snapshot, switch entities for clips, detection, snapshots, and improve contrast, and support for multiple Frigate instances. It offers easy installation via HACS and manual installation options for advanced users. Users need to configure the `mqtt` integration for Frigate to work. Additionally, media browsing and a companion Lovelace card are available for enhanced user experience. Refer to the main Frigate documentation for detailed installation instructions and usage guidance.

This repository contains a custom component for Home Assistant that integrates various Xiaomi Mi Air Purifier and Xiaomi Mi Air Humidifier models. It provides detailed support for different devices, including power control, preset modes, child lock, LED control, favorite level adjustment, and various attributes monitoring. The custom component offers a more extensive range of supported devices compared to the official Home Assistant component, with additional features and device compatibility. Users can easily set up and configure their Xiaomi air purifiers and humidifiers within Home Assistant for enhanced control and monitoring.

This custom component for Home Assistant adds support for controlling Midea air conditioner and dehumidifier appliances via the local area network. It provides integration for various Midea appliances, allowing users to control settings such as humidity levels, fan speed, and more through Home Assistant. The component supports multiple protocols and entities for different appliance models, offering a comprehensive solution for managing Midea appliances on the local network.

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.

Microsoft Copilot for Security is a generative AI-powered assistant for daily operations in security and IT that empowers teams to protect at the speed and scale of AI.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.