Tech • AI • Robotics
What's New in Google AI
9/10TL;DR
Google unveiled a rapidly expanding AI ecosystem centered on Gemini 3.5 models and AI Studio, enabling developers to build multimodal apps, agents, and even full Android applications directly from prompts.
KEY POINTS
Gemini 3.5 model lineup expands
Google introduced Gemini 3.5 Flash, optimized for speed and cost, alongside 3.5 Pro for complex reasoning and 3.1 Flash-Lite for low-latency tasks. Flash models are now the default for app building in AI Studio. The models support multimodal input and output across text, images, audio, video, and code, positioning them as versatile tools for production use.
Multimodal capabilities unlock new workflows
The models can interpret complex inputs such as videos and convert them into structured outputs or code. Demonstrations showed a five-minute video processed into a timestamped table with contextual insights for roughly $0.015, highlighting both efficiency and affordability. This capability enables entirely new application categories driven by video and visual understanding.
Generative media tools expand into video
Google extended its “Nano Banana” image generation system into video with Gemini Omni Flash, allowing users to generate and edit video content from prompts. The system emphasizes high-fidelity outputs and detailed editing, addressing traditionally time-consuming creative workflows.
AI Studio becomes end-to-end development platform
AI Studio now combines experimentation, prototyping, and deployment. Its Playground allows parameter tuning and rapid testing, while Build Mode enables users to create applications directly from natural language prompts. Code can be exported in multiple languages, including Python, TypeScript, and.NET.
Prompt-to-app development demonstrated
A live example showed an Android piano application generated entirely from a text prompt and deployed to a device. The system automatically produced Kotlin code, UI designs, and installable builds. This approach removes traditional setup barriers such as emulators and environment configuration.
Workspace and app integrations via prompting
New integrations allow developers to connect Gmail, Calendar, and other Google Workspace services through prompts. A prototype “calendar roulette” app demonstrated automated event selection and deletion using OAuth authentication, showcasing how AI can orchestrate real user data and workflows.
Real-time interaction with Gemini Live API
The Gemini Live API enables conversational, real-time interactions with shared screens and multimodal context. Demonstrations included live screen interpretation, multilingual responses, and integration with Google Search for grounded answers, including weather queries with cited sources.
Agent creation through natural language
A new Interactions API allows developers to create managed AI agents by describing tasks in plain language. These agents can connect to services and run on cloud infrastructure with a single API call, powered by Google’s Antigravity system.
Open models and on-device AI with Gemma
Google emphasized open AI with Gemma 4, supporting over 140 languages and a 256,000-token context window. The models can run locally on laptops and mobile devices, enabling offline use cases and reduced costs. Partnerships with platforms like Hugging Face, Ollama, and Kaggle are expanding adoption.
Full-stack AI infrastructure and TPU ecosystem
The company highlighted its integrated stack, including TPU software, JAX, and tools for training and inference. This infrastructure supports everything from model development to deployment, offering performance optimizations across the lifecycle.
Advances in robotics and world models
New releases such as Gemini Robotics 1.6 enable AI control of physical devices using standard APIs. Open-source robots like Reachy Mini and Stanford Pupper can perform tasks via conversational commands without custom training. Meanwhile, Genie 3 introduces world modeling capabilities that simulate realistic physics in generated environments.
CONCLUSION
Google’s latest AI updates signal a shift toward fully integrated, multimodal, and prompt-driven development, lowering barriers to building applications while expanding AI’s reach from cloud software to local devices and robotics.
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