AIR-1 is a compact sensor device designed for monitoring various environmental parameters such as gas levels, particulate matter, temperature, and humidity. It features multiple sensors for detecting gases like CO, alcohol, H2, NO2, NH3, CO2, as well as particulate matter, VOCs, NOx, and more. The device is designed with a focus on accuracy and efficient heat management in a small form factor, making it suitable for indoor air quality monitoring and environmental sensing applications.

The 'awesome-air-quality' repository is a curated list of software libraries, tools, and resources related to air quality data acquisition, analysis, and visualization. It includes libraries in various programming languages such as Python, Java, R, and C#, as well as hardware drivers and software for gas sensors and particulate matter sensors. The repository aims to provide a comprehensive collection of tools for working with air quality data from different sources and for different purposes.

The IBM Analog Hardware Acceleration Kit is an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. It consists of two main components: Pytorch integration and Analog devices simulator. The Pytorch integration provides a series of primitives and features that allow using the toolkit within PyTorch, including analog neural network modules, analog training using torch training workflow, and analog inference using torch inference workflow. The Analog devices simulator is a high-performant (CUDA-capable) C++ simulator that allows for simulating a wide range of analog devices and crossbar configurations by using abstract functional models of material characteristics with adjustable parameters. Along with the two main components, the toolkit includes other functionalities such as a library of device presets, a module for executing high-level use cases, a utility to automatically convert a downloaded model to its equivalent Analog model, and integration with the AIHW Composer platform. The toolkit is currently in beta and under active development, and users are advised to be mindful of potential issues and keep an eye for improvements, new features, and bug fixes in upcoming versions.

Ianvs is a distributed synergy AI benchmarking project incubated in KubeEdge SIG AI. It aims to test the performance of distributed synergy AI solutions following recognized standards, providing end-to-end benchmark toolkits, test environment management tools, test case control tools, and benchmark presentation tools. It also collaborates with other organizations to establish comprehensive benchmarks and related applications. The architecture includes critical components like Test Environment Manager, Test Case Controller, Generation Assistant, Simulation Controller, and Story Manager. Ianvs documentation covers quick start, guides, dataset descriptions, algorithms, user interfaces, stories, and roadmap.

CREDIT is an open software platform for training and deploying AI atmospheric prediction models. It offers fast models with flexible configuration options for input data and neural network architecture. The user-friendly interface enables quick setup and iteration. Developed by the MILES group and NSF National Center for Atmospheric Research, CREDIT combines advanced AI/ML with atmospheric science expertise. It provides a stable release with various models, training, and deployment options, with ongoing development. Detailed documentation is available for installation, training, deployment, config file interpretation, and API usage.

Large Language Models (LLMs) are now embedded in everyday tools like email assistants, chat apps, and productivity software. This project introduces DonkeyRail, a lightweight guardrail that detects and blocks malicious self-replicating prompts known as RAGworm within GenAI-powered applications. The guardrail is fast, accurate, and practical for real-world GenAI systems, preventing activities like spam, phishing campaigns, and data leaks.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

Moon is a monitoring and alerting platform suitable for multiple domains, supporting various application scenarios such as cloud-native, Internet of Things (IoT), and Artificial Intelligence (AI). It simplifies operational work of cloud-native monitoring, boasts strong IoT and AI support capabilities, and meets diverse monitoring needs across industries. Capable of real-time data monitoring, intelligent alerts, and fault response for various fields.

Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It supports the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks. Dioptra provides a REST API and can be controlled via a web interface or Python client for designing, managing, executing, and tracking experiments. It aims to be reproducible, traceable, extensible, interoperable, modular, secure, interactive, shareable, and reusable.

FDE.AI-docs is a repository containing various texts related to FeraDroid Engine (FDE), an All-in-One ultimate optimizer for Android devices. FDE.AI optimizes performance and power consumption by configuring settings based on individual device hardware and software characteristics. It is compatible with a wide range of devices and Android OS versions, applying systemless changes for customization without touching the system partition.

This repository contains the source code for a GEMM & deep learning hardware accelerator system used to validate proposed systolic array hardware architectures implementing efficient matrix multiplication algorithms to increase performance-per-area limits of GEMM & AI accelerators. Achieved results include up to 3× faster CNN inference, >2× higher mults/multiplier/clock cycle, and low area with high clock frequency. The system is specialized for inference of non-sparse DNN models with fixed-point/quantized inputs, fully accelerating all DNN layers in hardware, and highly optimizing GEMM acceleration.

The 'Getting Started with Agents Using Azure AI Foundry' repository provides a solution that deploys a web-based chat application with an AI agent running in Azure Container App. The agent leverages Azure AI services for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution includes built-in monitoring capabilities for easier troubleshooting and optimized performance. Users can deploy AI models, customize the agent, and evaluate its performance. The repository offers flexible deployment options through GitHub Codespaces, VS Code Dev Containers, or local environments.

Knavigator is a project designed to analyze, optimize, and compare scheduling systems, with a focus on AI/ML workloads. It addresses various needs, including testing, troubleshooting, benchmarking, chaos engineering, performance analysis, and optimization. Knavigator interfaces with Kubernetes clusters to manage tasks such as manipulating with Kubernetes objects, evaluating PromQL queries, as well as executing specific operations. It can operate both outside and inside a Kubernetes cluster, leveraging the Kubernetes API for task management. To facilitate large-scale experiments without the overhead of running actual user workloads, Knavigator utilizes KWOK for creating virtual nodes in extensive clusters.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

The Multi-Agent -Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system allows users to focus on what matters by coordinating activities across an organization, enabling GenAI to scale, and is applicable to most industries. It is intended for developing and deploying custom AI solutions for specific customers, providing a foundation to accelerate building out multi-agent systems.

ReaLHF is a distributed system designed for efficient RLHF training with Large Language Models (LLMs). It introduces a novel approach called parameter reallocation to dynamically redistribute LLM parameters across the cluster, optimizing allocations and parallelism for each computation workload. ReaL minimizes redundant communication while maximizing GPU utilization, achieving significantly higher Proximal Policy Optimization (PPO) training throughput compared to other systems. It supports large-scale training with various parallelism strategies and enables memory-efficient training with parameter and optimizer offloading. The system seamlessly integrates with HuggingFace checkpoints and inference frameworks, allowing for easy launching of local or distributed experiments. ReaLHF offers flexibility through versatile configuration customization and supports various RLHF algorithms, including DPO, PPO, RAFT, and more, while allowing the addition of custom algorithms for high efficiency.

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

AIR-1 is a compact sensor device designed for monitoring various environmental parameters such as gas levels, particulate matter, temperature, and humidity. It features multiple sensors for detecting gases like CO, alcohol, H2, NO2, NH3, CO2, as well as particulate matter, VOCs, NOx, and more. The device is designed with a focus on accuracy and efficient heat management in a small form factor, making it suitable for indoor air quality monitoring and environmental sensing applications.

AirCasting is a platform for gathering, visualizing, and sharing environmental data. It aims to provide a central hub for environmental data, making it easier for people to access and use this information to make informed decisions about their environment.

ESPHome yaml files for AirGradient devices to maintain the research and accuracy of AirGradient sensors, while also gaining the benefits of ESPHome/HomeAssistant for easy to use switches, buttons, configurations, and dashboards. Maintains the ability to also send data to the AirGradient Dashboard, which can also be disabled/removed to keep all data local.

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.

AireLibre is a community response to the need for freely, collaboratively, and decentralized air quality information. It includes projects like Red Descentralizada de Aire Libre (ReDAL), Linka, Linka Firmware, LinkaBot, AQmap, and Android/iOS apps. Users can join the network with a sensor communicating with Linka. Materials and tools are needed to build a sensor. The initiative is decentralized and open for community collaboration. Users can extend or add projects to AireLibre. The license allows for creating personal networks. AireLibre is not for professional/industrial/scientific/military use, and the sensors are not calibrated in Switzerland.

Air Quality Info is a PHP-based page that displays current PM10 and PM2.5 measurements from Sensor.Community-compatible devices. It features a clean interface, stores records in MySQL, renders graphs with ChartJS, supports multiple devices, offers locale support, and functions as a Progressive Web App. The project setup involves creating directory structures, setting permissions, and starting Docker containers. The admin dashboard is accessible at http://aqi.eco.localhost:8080/, while the Air Quality Info pages use a specific naming schema. The project is supported by Nettigo Air Monitor, Sensor.Community, and a forum thread in Polish.

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

AirCasting is a platform for gathering, visualizing, and sharing environmental data. It aims to provide a central hub for environmental data, making it easier for people to access and use this information to make informed decisions about their environment.

AIR-1 is a compact sensor device designed for monitoring various environmental parameters such as gas levels, particulate matter, temperature, and humidity. It features multiple sensors for detecting gases like CO, alcohol, H2, NO2, NH3, CO2, as well as particulate matter, VOCs, NOx, and more. The device is designed with a focus on accuracy and efficient heat management in a small form factor, making it suitable for indoor air quality monitoring and environmental sensing applications.

AireLibre is a community response to the need for freely, collaboratively, and decentralized air quality information. It includes projects like Red Descentralizada de Aire Libre (ReDAL), Linka, Linka Firmware, LinkaBot, AQmap, and Android/iOS apps. Users can join the network with a sensor communicating with Linka. Materials and tools are needed to build a sensor. The initiative is decentralized and open for community collaboration. Users can extend or add projects to AireLibre. The license allows for creating personal networks. AireLibre is not for professional/industrial/scientific/military use, and the sensors are not calibrated in Switzerland.

This repository contains the official code for the paper 'Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning'. It provides tools for point cloud segmentation experiments based on different settings, tree parameters extraction, handling large point clouds through tiling, predicting, and merging workflows. Additionally, it includes commands for training, testing, and evaluating the models, along with the necessary datasets and pretrained models.

Air Quality Info is a PHP-based page that displays current PM10 and PM2.5 measurements from Sensor.Community-compatible devices. It features a clean interface, stores records in MySQL, renders graphs with ChartJS, supports multiple devices, offers locale support, and functions as a Progressive Web App. The project setup involves creating directory structures, setting permissions, and starting Docker containers. The admin dashboard is accessible at http://aqi.eco.localhost:8080/, while the Air Quality Info pages use a specific naming schema. The project is supported by Nettigo Air Monitor, Sensor.Community, and a forum thread in Polish.

The 'awesome-air-quality' repository is a curated list of software libraries, tools, and resources related to air quality data acquisition, analysis, and visualization. It includes libraries in various programming languages such as Python, Java, R, and C#, as well as hardware drivers and software for gas sensors and particulate matter sensors. The repository aims to provide a comprehensive collection of tools for working with air quality data from different sources and for different purposes.

Awesome Green AI is a curated list of resources and tools aimed at reducing the environmental impacts of using and deploying AI. It addresses the carbon footprint of the ICT sector, emphasizing the importance of AI in reducing environmental impacts beyond GHG emissions and electricity consumption. The tools listed cover code-based tools for measuring environmental impacts, monitoring tools for power consumption, optimization tools for energy efficiency, and calculation tools for estimating environmental impacts of algorithms and models. The repository also includes leaderboards, papers, survey papers, and reports related to green AI and environmental sustainability in the AI sector.