Best AI tools for< Environmental Advocate >

PsyScribe is an AI-powered platform that serves as your personal therapist and mental health support system. By leveraging advanced artificial intelligence algorithms, PsyScribe provides users with a confidential and accessible space to express their thoughts and emotions, receive personalized insights, and access mental health resources. Whether you're seeking guidance, coping strategies, or simply a listening ear, PsyScribe is designed to support your emotional well-being effectively and conveniently.

Copilot is an AI-powered bike light and camera designed to enhance safety for cyclists. It constantly monitors the road behind the cyclist using artificial intelligence to detect vehicles approaching or overtaking. The device provides audible and visual alerts to the cyclist, helping to prevent accidents. Copilot aims to improve situational awareness and make cycling safer in urban environments.

MindPeace AI is an AI-powered platform that serves as a personal mental wellbeing companion, offering therapeutic techniques to guide users towards self-reflection, relaxation, and emotional balance. It provides tools, exercises, and gentle reminders in a convenient, stigma-free environment. The platform is designed to complement traditional therapy by providing on-demand support, but it does not replace human therapists. MindPeace AI prioritizes privacy and safety, ensuring that conversations are private and personal information is never shared with third parties.

SnapMeasureAI is an AI-powered application that provides 99% accurate body measurements without the need to visit a tailor. It uses advanced AI technology to accurately measure the body from just a few photos or a short video, with an average error of 0.70 cm (0.27 in). SnapMeasureAI is designed to help users find their perfect fit quickly and easily, reducing the need for multiple tailor visits and minimizing the hassle of clothing returns. The application is trained on over 100 million body combinations, 400,000 backgrounds, and 90,000 poses, ensuring reliable performance in diverse scenarios. SnapMeasureAI aims to address the significant problem of annual retail returns by offering precise measurements and helping users make confident shopping decisions.

viAct.ai is an AI-powered construction management software and app that utilizes computer vision and video analytics to enhance workplace safety in industries such as construction, oil & gas, mining, manufacturing, and more. The platform offers scenario-based AI vision technology to simplify monitoring processes, automate tasks, and reduce safety risks. With features like PPE detection, environmental monitoring, danger zone alerts, fleet management, and work at height safety, viAct.ai aims to empower jobsites with automated solutions powered by AI video analytics.

Climate Change AI is a global non-profit organization that focuses on catalyzing impactful work at the intersection of climate change and machine learning. They provide resources, reports, events, and grants to support the use of machine learning in addressing climate change challenges.

Global Plastic Watch (GPW) is a digital platform that maps the world's plastic pollution in near real-time using a unique combination of satellite imagery and artificial intelligence. It provides a comprehensive view of the global plastic waste crisis, including the location and size of plastic waste sites, the types of plastic waste, and the impact of plastic pollution on the environment and human health.

EcoSnap is an AI tool designed to help users recycle plastic more effectively. By simply taking a picture of a plastic code, users can learn how to recycle the item properly. The tool aims to promote environmental sustainability by providing accurate recycling information based on artificial intelligence technology. EcoSnap is user-friendly and accessible, making it convenient for individuals looking to contribute to a greener planet.

Satlas is an AI-powered platform that provides geospatial data generated by AI models. The platform offers insights into changes in marine infrastructure, renewable energy infrastructure, and tree cover on a monthly basis. Users can explore maps showcasing developments such as wind farms, solar farms, deforestation, and more. Satlas employs advanced AI architectures and training algorithms in computer vision to enhance low-resolution satellite imagery and produce high-resolution images globally. The platform's geospatial datasets are freely available for offline analysis, along with AI models and training labels. Developed by the Allen Institute for AI, Satlas aims to advance computer vision technology for better understanding and monitoring of Earth's changes.

Green Cubes is an AI application that provides precise volume, complexity, and biodiversity indication of terrestrial areas at scale for Digital Reality and Sponsorship. It shapes the digital twin of nature, offering transparency and trust through Measure, Report, and Verification (MVR) using data collection, AI computation, and 3D visualization. Green Cubes enables corporations to sponsor nature impact with confidence and transparency, contributing to the preservation of biodiversity.

FlyPix is an AI-enabled geospatial solutions platform that leverages advanced AI technology to transform object detection, localization, tracking, and monitoring in the field of geospatial technology. The platform offers a wide range of capabilities, including AI-driven object analysis, change and anomaly detection, dynamic tracking, and custom use cases tailored to meet unique industry needs. FlyPix aims to provide unparalleled precision and efficiency in operations by converting complex imagery into actionable, geo-referenced insights.

Picterra is a geospatial AI platform that offers reliable solutions for sustainability, compliance, monitoring, and verification. It provides an all-in-one plot monitoring system, professional services, and interactive tours. Users can build custom AI models to detect objects, changes, or patterns using various geospatial imagery data. Picterra aims to revolutionize geospatial analysis with its category-leading AI technology, enabling users to solve challenges swiftly, collaborate more effectively, and scale further.

Flora Incognita is an interactive plant species identification app that combines AI-supported plant identification with citizen science. Users can identify over 30,000 plant species, save observations, access extensive plant fact sheets, and contribute to scientific research. The app is free of charge, ad-free, and works offline, making it ideal for educational purposes and nature conservation initiatives.

Trazable LifeCycle is a sustainability software designed to measure, improve, and report the sustainability of companies. It simplifies the process of measuring and reporting environmental impact by providing tools to create process maps, add environmental impact data, and generate key sustainability indicators. The software is tailored for the food industry, offering over 50 million industry-specific data points to aid in decision-making and compliance with sustainability regulations. Trazable LifeCycle ensures data validity by using constantly updated and validated datasets, allowing users to measure both product and organizational carbon footprints.

Cybertiks is an AI-powered application that offers remote monitoring and analysis of agriculture fields using satellite imagery. The application provides valuable insights such as soil nutrients and texture, tailored AI models for accurate metrics, historical data analysis, and visualization of results on a map. Cybertiks integrates various sources of information, offers tailored solutions for clients, and uses quantum-enabled artificial intelligence for material detection and classification. The application is designed to meet the needs of diverse industries worldwide by harnessing the power of satellite imagery and AI technology.

Telborg is an AI-powered platform that provides daily climate news sourced from verified entities such as companies, governments, international institutions, think tanks, and universities. It offers insights on carbon removal technologies, climate finance, renewables, and synthetic fuels. Telborg stands out for its concise AI-generated summaries, real-time updates via WhatsApp, and direct source links. The platform covers global news comprehensively, making it a valuable resource for staying informed about climate-related developments.

Climate Policy Radar is an AI-powered application that serves as a live, searchable database containing over 5,000 national climate laws, policies, and UN submissions. The app aims to organize, analyze, and democratize climate data by providing open data, code, and machine learning models. It promotes a responsible approach to AI, fosters a climate NLP community, and offers an API for organizations to utilize the data. The tool addresses the challenge of sparse and siloed climate-related information, empowering decision-makers with evidence-based policies to accelerate climate action.

AMP Smart Sortation™ is waste sortation's permanent solution. As the leader in AI-powered sortation, we give waste and recycling leaders the power to reduce labor costs, increase resource recovery, and deliver more reliable operations. AMP's AI-powered automation allows real-time material characterization and configuration to capture the most value from any material stream.

EV Intersection is a website that provides detailed information about various electric vehicle models available in the market. Users can explore different electric vehicles, their specifications, and pricing. The site offers AI review summaries for each model, including key features like range, motor type, acceleration, and horsepower. Whether you're looking for a luxury sedan or a compact SUV, EV Intersection has you covered with comprehensive data to help you make an informed decision when choosing your next electric vehicle.

The Value Chain Generator® is an AI & Big Data platform for circular bioeconomy that helps companies, waste processors, and regions maximize the value and minimize the carbon footprint of by-products and waste. It uses global techno-economic and climate intelligence to identify circular opportunities, match with suitable partners and technologies, and create profitable and impactful solutions. The platform accelerates the circular transition by integrating local industries through technology, reducing waste, and increasing profits.

LLM-Geo is an AI-powered geographic information system (GIS) that leverages Large Language Models (LLMs) for automatic spatial data collection, analysis, and visualization. By adopting LLM as the reasoning core, it addresses spatial problems with self-generating, self-organizing, self-verifying, self-executing, and self-growing capabilities. The tool aims to make spatial analysis easier, faster, and more accessible by reducing manual operation time and delivering accurate results through case studies. It uses GPT-4 API in a Python environment and advocates for further research and development in autonomous GIS.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

Can AI Code is a self-evaluating interview tool for AI coding models. It includes interview questions written by humans and tests taken by AI, inference scripts for common API providers and CUDA-enabled quantization runtimes, a Docker-based sandbox environment for validating untrusted Python and NodeJS code, and the ability to evaluate the impact of prompting techniques and sampling parameters on large language model (LLM) coding performance. Users can also assess LLM coding performance degradation due to quantization. The tool provides test suites for evaluating LLM coding performance, a webapp for exploring results, and comparison scripts for evaluations. It supports multiple interviewers for API and CUDA runtimes, with detailed instructions on running the tool in different environments. The repository structure includes folders for interviews, prompts, parameters, evaluation scripts, comparison scripts, and more.

IPEX-LLM is a low-bit LLM library on Intel XPU (Xeon/Core/Flex/Arc/PVC) that provides tutorials to help users understand and use the library to build LLM applications. The tutorials cover topics such as introduction to IPEX-LLM, environment setup, basic application development, Chinese language support, intermediate and advanced application development, GPU acceleration, and finetuning. Users can learn how to build chat applications, chatbots, speech recognition, and more using IPEX-LLM.

BigCodeBench is an easy-to-use benchmark for code generation with practical and challenging programming tasks. It aims to evaluate the true programming capabilities of large language models (LLMs) in a more realistic setting. The benchmark is designed for HumanEval-like function-level code generation tasks, but with much more complex instructions and diverse function calls. BigCodeBench focuses on the evaluation of LLM4Code with diverse function calls and complex instructions, providing precise evaluation & ranking and pre-generated samples to accelerate code intelligence research. It inherits the design of the EvalPlus framework but differs in terms of execution environment and test evaluation.

CodeTF is a Python transformer-based library for code large language models (Code LLMs) and code intelligence. It provides an interface for training and inferencing on tasks like code summarization, translation, and generation. The library offers utilities for code manipulation across various languages, including easy extraction of code attributes. Using tree-sitter as its core AST parser, CodeTF enables parsing of function names, comments, and variable names. It supports fast model serving, fine-tuning of LLMs, various code intelligence tasks, preprocessed datasets, model evaluation, pretrained and fine-tuned models, and utilities to manipulate source code. CodeTF aims to facilitate the integration of state-of-the-art Code LLMs into real-world applications, ensuring a user-friendly environment for code intelligence tasks.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

This repository contains the official implementation of RPG, a powerful training-free paradigm for text-to-image generation and editing. RPG utilizes proprietary or open-source MLLMs as prompt recaptioner and region planner with complementary regional diffusion. It achieves state-of-the-art results and can generate high-resolution images. The codebase supports diffusers and various diffusion backbones, including SDXL and SD v1.4/1.5. Users can reproduce results with GPT-4, Gemini-Pro, or local MLLMs like miniGPT-4. The repository provides tools for quick start, regional diffusion with GPT-4, and regional diffusion with local LLMs.

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

Devon is an open-source pair programmer tool designed to facilitate collaborative coding sessions. It provides features such as multi-file editing, codebase exploration, test writing, bug fixing, and architecture exploration. The tool supports Anthropic, OpenAI, and Groq APIs, with plans to add more models in the future. Devon is community-driven, with ongoing development goals including multi-model support, plugin system for tool builders, self-hostable Electron app, and setting SOTA on SWE-bench Lite. Users can contribute to the project by developing core functionality, conducting research on agent performance, providing feedback, and testing the tool.

StarCoder2-Instruct is an open-source pipeline that introduces StarCoder2-15B-Instruct-v0.1, a self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. It generates instruction-response pairs to fine-tune StarCoder-15B without human annotations or data from proprietary LLMs. The tool is primarily finetuned for Python code generation tasks that can be verified through execution, with potential biases and limitations. Users can provide response prefixes or one-shot examples to guide the model's output. The model may have limitations with other programming languages and out-of-domain coding tasks.

Vocode is an open source library that enables users to easily build voice-based LLM (Large Language Model) apps. With Vocode, users can create real-time streaming conversations with LLMs and deploy them for phone calls, Zoom meetings, and more. The library offers abstractions and integrations for transcription services, LLMs, and synthesis services, making it a comprehensive tool for voice-based applications.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

Hal9 is a tool that allows users to create and deploy generative applications such as chatbots and APIs quickly. It is open, intuitive, scalable, and powerful, enabling users to use various models and libraries without the need to learn complex app frameworks. With a focus on AI tasks like RAG, fine-tuning, alignment, and training, Hal9 simplifies the development process by skipping engineering tasks like frontend development, backend integration, deployment, and operations.

Moatless Tools is a hobby project focused on experimenting with using Large Language Models (LLMs) to edit code in large existing codebases. The project aims to build tools that insert the right context into prompts and handle responses effectively. It utilizes an agentic loop functioning as a finite state machine to transition between states like Search, Identify, PlanToCode, ClarifyChange, and EditCode for code editing tasks.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

This repository contains the code and dataset for LDB, a novel debugging framework that enables Large Language Models (LLMs) to refine their generated programs by tracking the values of intermediate variables throughout the runtime execution. LDB segments programs into basic blocks, allowing LLMs to concentrate on simpler code units, verify correctness block by block, and pinpoint errors efficiently. The tool provides APIs for debugging and generating code with debugging messages, mimicking how human developers debug programs.

Prompty is an asset class and format for LLM prompts designed to enhance observability, understandability, and portability for developers. The primary goal is to accelerate the developer inner loop. This repository contains the Prompty Language Specification and a documentation site. The Visual Studio Code extension offers a prompt playground to streamline the prompt engineering process.

Multilspy is a Python library developed for research purposes to facilitate the creation of language server clients for querying and obtaining results of static analyses from various language servers. It simplifies the process by handling server setup, communication, and configuration parameters, providing a common interface for different languages. The library supports features like finding function/class definitions, callers, completions, hover information, and document symbols. It is designed to work with AI systems like Large Language Models (LLMs) for tasks such as Monitor-Guided Decoding to ensure code generation correctness and boost compilability.

Moim zadaniem jest promowanie świadomości i angażowanie użytkowników w konkretne działania, które przyczyniają się do ochrony środowiska naturalnego.

Expert in environmental justice data, with specific response criteria.

Thinking partner on Just Transitions

Hi ;) Ask me for data & insights gathered from an environmentally aware global community

I'm Burning Earth, alarming users about environmental harm and climate change. Powered by Breebs (www.breebs.com)

Guiding users through the world of swamps and wetlands with environmental expertise.

Expert in wind energy and renewable technology, informative and environmental

Recycling assistant using image recognition to advise on recyclability.

Expert in waste management, recycling, and eco-friendly practices.

Exactly like ChatGPT, except 100% of the revenue received from OpenAI is used for ocean cleanup and restoration projects!

Your personal guide to making a difference in the climate crisis

Your expert guide to 2022-2023 IPCC climate documents 📝🌎 Powered by Breebs (www.breebs.com)

Balancing love and logic in decision-making.

Informative guide on transgender issues

A supportive guide for managing sensory dysregulation with tailored advice.

Tech-Savvy Environmental Consultant

Advises on sustainable engineering solutions to environmental challenges.

Simulates and analyzes potential environmental disaster scenarios for preparedness.

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