WeatherNext Integrations

Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging.

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.

BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process.

Google AI Studio is a comprehensive, web-based development environment that democratizes access to Google's cutting-edge AI models, notably the Gemini family, enabling a broad spectrum of users to explore and build innovative applications. This platform facilitates rapid prototyping by providing an intuitive interface for prompt engineering, allowing developers to meticulously craft and refine their interactions with AI. Beyond basic experimentation, AI Studio supports the seamless integration of AI capabilities into diverse projects, from simple chatbots to complex data analysis tools. Users can rigorously test different prompts, observe model behaviors, and iteratively refine their AI-driven solutions within a collaborative and user-friendly environment. This empowers developers to push the boundaries of AI application development, fostering creativity and accelerating the realization of AI-powered solutions.

GitHub is the world’s most secure, most scalable, and most loved developer platform. Join millions of developers and businesses building the software that powers the world. Build with the world’s most innovative communities, backed by our best tools, support, and services. If you manage multiple contributors , there’s a free option: GitHub Team for Open Source. We also run GitHub Sponsors, where we help fund your work. The Pack is back. We’ve partnered up to give students and teachers free access to the best developer tools—for the school year and beyond. Work for a government-recognized nonprofit, association, or 501(c)(3)? Get a discounted Organization account on us.

Gemini is Google’s advanced AI assistant designed to help users think, create, learn, and complete tasks with a new level of intelligence. Powered by Google’s most capable models, including Gemini 3, it enables users to ask complex questions, generate content, analyze information, and explore ideas through natural conversation. Gemini can create images, videos, summaries, study plans, and first drafts while also providing feedback on uploaded files and written work. The platform is grounded in Google Search, allowing it to deliver accurate, up-to-date information and support deep follow-up questions. Gemini connects seamlessly with Google apps like Gmail, Docs, Calendar, Maps, YouTube, and Photos to help users complete tasks without switching tools. Features such as Gemini Live, Deep Research, and Gems enhance brainstorming, research, and personalized workflows. Available through flexible free and paid plans, Gemini supports everyday users, students, and professionals across devices.

The Gemini Deep Research Agent is an autonomous research system that plans, searches, analyzes, and synthesizes multi-step findings using Gemini 3 Pro. Built for complex, long-running tasks, it performs iterative web searches, evaluates sources, and generates deeply structured, fully cited reports. Developers can run tasks asynchronously with background execution, enabling reliable long-duration workflows without timeouts. The agent also integrates with your own data through File Search, combining public web intelligence with private documents. Real-time streaming delivers progress, intermediate thoughts, and updates for transparent research. Designed for high-value analysis, the agent turns traditional research cycles into automated, repeatable, and scalable intelligence workflows.

Gemini Enterprise is a comprehensive AI platform built by Google Cloud designed to bring the full power of Google’s advanced AI models, agent-creation tools, and enterprise-grade data access into everyday workflows. The solution offers a unified chat interface that lets employees interact with internal documents, applications, data sources, and custom AI agents. At its core, Gemini Enterprise comprises six key components: the Gemini family of large multimodal models, an agent orchestration workbench (formerly Google Agentspace), pre-built starter agents, robust data-integration connectors to business systems, extensive security and governance controls, and a partner ecosystem for tailored integrations. It is engineered to scale across departments and enterprises, enabling users to build no-code or low-code agents that automate tasks, such as research synthesis, customer support response, code assist, contract analysis, and more, while operating within corporate compliance standards.

Google Earth Engine is a cloud-based platform for scientific analysis and visualization of geospatial datasets, providing access to a vast public data archive that includes over 90 petabytes of analysis-ready satellite imagery and more than 1,000 curated geospatial datasets. This extensive catalog encompasses over 50 years of historical imagery, updated daily, with resolutions as fine as one meter per pixel, featuring datasets such as Landsat, MODIS, Sentinel, and the National Agriculture Imagery Program (NAIP). Earth Engine enables users to analyze Earth observation data and apply machine learning techniques through its web-based JavaScript Code Editor and Python API, facilitating the development of complex geospatial workflows. The platform's integration with Google Cloud allows for large-scale parallel processing, empowering users to conduct comprehensive analyses and visualize Earth data efficiently. Additionally, Earth Engine offers interoperability with BigQuery.

These exquisite, intricate machines are proteins. They underpin not just the biological processes in your body but every biological process in every living thing. They’re the building blocks of life. Currently, there are around 100 million known distinct proteins, with many more found every year. Each one has a unique 3D shape that determines how it works and what it does. But figuring out the exact structure of a protein remains an expensive and often time-consuming process, meaning we only know the exact 3D structure of a tiny fraction of the proteins known to science. Finding a way to close this rapidly expanding gap and predict the structure of millions of unknown proteins could not only help us tackle disease and more quickly find new medicines but perhaps also unlock the mysteries of how life itself works.

Creating solutions to unforeseen problems is second nature in human intelligence, a result of critical thinking informed by experience. The machine learning community has made tremendous progress in generating and understanding textual data, but advances in problem-solving remain limited to relatively simple maths and programming problems, or else retrieving and copying existing solutions. As part of DeepMind’s mission to solve intelligence, we created a system called AlphaCode that writes computer programs at a competitive level. AlphaCode achieved an estimated rank within the top 54% of participants in programming competitions by solving new problems that require a combination of critical thinking, logic, algorithms, coding, and natural language understanding. AlphaCode uses transformer-based language models to generate code at an unprecedented scale, and then smartly filters to a small set of promising programs.

Language, and its role in demonstrating and facilitating comprehension - or intelligence - is a fundamental part of being human. It gives people the ability to communicate thoughts and concepts, express ideas, create memories, and build mutual understanding. These are foundational parts of social intelligence. It’s why our teams at DeepMind study aspects of language processing and communication, both in artificial agents and in humans. As part of a broader portfolio of AI research, we believe the development and study of more powerful language models – systems that predict and generate text – have tremendous potential for building advanced AI systems that can be used safely and efficiently to summarise information, provide expert advice and follow instructions via natural language. Developing beneficial language models requires research into their potential impacts, including the risks they pose.

Project Mariner is a research prototype developed by Google DeepMind, built upon their advanced AI model, Gemini 2.0. It explores the future of human-agent interaction by automating tasks within a user's browser. Leveraging multimodal understanding, Project Mariner comprehends and reasons across various browser elements, including text, code, images, and forms. This enables it to navigate complex websites, automate repetitive tasks, and provide visual feedback to users. The system can interpret voice instructions and offers updates on task progress, ensuring users remain informed and in control. Additionally, Project Mariner can follow complex instructions by breaking them down into actionable steps, understanding relationships between web elements, and providing clear plans and actions to users. Currently, Project Mariner is in the testing phase with a select group of trusted users. Those interested in participating can join the waitlist for future testing opportunities.

AlphaEvolve is an evolutionary coding agent powered by large language models for general-purpose algorithm discovery and optimization. It pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers and uses an evolutionary framework to improve upon the most promising ideas. AlphaEvolve enhanced the efficiency of Google's data centers, chip design, and AI training processes, including training the large language models underlying AlphaEvolve itself. It has also helped design faster matrix multiplication algorithms and find new solutions to open mathematical problems, showing incredible promise for application across many areas.

Music AI Sandbox is a set of experimental tools designed to spark new creative possibilities and help artists explore unique musical ideas. Developed in close collaboration with musicians, these tools are practical, useful, and can open doors to new forms of music creation. It includes features that allow users to generate fresh instrumental ideas by describing the desired sound, understanding genres, moods, vocal styles, and instruments. It generates musical continuations based on uploaded or generated audio clips, aiding in overcoming writer's block. It also enables users to transform the mood, genre, or style of an entire clip or make targeted modifications to specific parts, with intuitive controls for subtle tweaks or dramatic shifts. These tools help musicians discover new sounds, experiment with different genres, expand and enhance their musical libraries, or develop entirely new styles.

Gemini Diffusion is our state-of-the-art research model exploring what diffusion means for language and text generation. Large-language models are the foundation of generative AI today. We’re using a technique called diffusion to explore a new kind of language model that gives users greater control, creativity, and speed in text generation. Diffusion models work differently. Instead of predicting text directly, they learn to generate outputs by refining noise, step by step. This means they can iterate on a solution very quickly and error correct during the generation process. This helps them excel at tasks like editing, including in the context of math and code. Generates entire blocks of tokens at once, meaning it responds more coherently to a user’s prompt than autoregressive models. Gemini Diffusion’s external benchmark performance is comparable to much larger models, whilst also being faster.

Gemini Robotics brings Gemini’s capacity for multimodal reasoning and world understanding into the physical world, allowing robots of any shape and size to perform a wide range of real-world tasks. Built on Gemini 2.0, it augments advanced vision-language-action models with the ability to reason about physical spaces, generalize to novel situations, including unseen objects, diverse instructions, and new environments, and understand and respond to everyday conversational commands while adapting to sudden changes in instructions or surroundings without further input. Its dexterity module enables complex tasks requiring fine motor skills and precise manipulation, such as folding origami, packing lunch boxes, or preparing salads, and it supports multiple embodiments, from bi-arm platforms like ALOHA 2 to humanoid robots such as Apptronik’s Apollo. It is optimized for local execution and has an SDK for seamless adaptation to new tasks and environments.

Chinchilla is a large language model. Chinchilla uses the same compute budget as Gopher but with 70B parameters and 4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.