Stitch Integrations

Where teams design together. Figma helps teams create, test, and ship better designs from start to finish. Fast and powerful, just like your work. Packed with design features you already love plus unique inventions like the Arc tool and Vector Networks, Figma helps you keep the ideas flowing. No need to stop to install, save, or export. It’s what any good cloud software should be. Bring your ideas to life faster in animated prototypes that feel like the real thing. Get insights from users and test concepts earlier and more often. Share a link to your design files or prototypes, and get feedback in context. Or, jump into the same file with your teammates—no matter where y’all are in the world—and co-edit live. Create a scalable design system that’s accessible for your organization and easy for you to manage. When all designers are speaking the same language, everyone’s more empowered to do their best work.

Gemini is Google's advanced AI chatbot designed to enhance creativity and productivity by engaging in natural language conversations. Accessible via the web and mobile apps, Gemini integrates seamlessly with various Google services, including Docs, Drive, and Gmail, enabling users to draft content, summarize information, and manage tasks efficiently. Its multimodal capabilities allow it to process and generate diverse data types, such as text, images, and audio, providing comprehensive assistance across different contexts. As a continuously learning model, Gemini adapts to user interactions, offering personalized and context-aware responses to meet a wide range of user needs.

Galileo AI creates delightful, editable UI designs from a simple text description. It empowers you to design faster than ever. Our technology learns from thousands of top user experience designs and builds the UI that meets your needs in lightning speed. Populate your designs with our carefully curated AI-generated illustrations and images to match your vision and style. By leveraging large language models, our AI understands the complex context and fills the end-to-end product copy accurately. Spend less time on tedious tasks such as creating repetitive UI patterns and making small visual tweaks. Instead, focus your efforts on landing bigger impact: designing creative solutions.

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.

HTML, short for HyperText Markup Language, is the markup language that is used by every website on the internet. HTML is code that websites use to build and structure every part of their website and web pages. HTML5 is a markup language used for structuring and presenting content on the World Wide Web. It is the fifth and final major HTML version that is a World Wide Web Consortium (W3C) recommendation. The current specification is known as the HTML Living Standard. It is maintained by the Web Hypertext Application Technology Working Group (WHATWG), a consortium of the major browser vendors (Apple, Google, Mozilla, and Microsoft). HTML5 includes detailed processing models to encourage more interoperable implementations; it extends, improves, and rationalizes the markup available for documents and introduces markup and application programming interfaces (APIs) for complex web applications. For the same reasons, HTML5 is also a candidate for cross-platform mobile applications.

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.