# Google AI Google's AI platform and ecosystem, including models, APIs, developer tools, research, and AI-powered products. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking DevFeed: [Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking]() Original publisher: [Read original article]() Author: techno\_mot (Selectel) Published: 2026-09-16T13:40:43Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Google](), [speech-to-speech](), [Google AI](), [Artificial Intelligence](), [API]() Tags: [ai](), [api](), [audio](), [gemini](), [google](), [llm](), [realtime](), [selectel](), [speech-to-speech](), [synthid](), [tag-efc6fa45f1fe](), [voice]() ### AI overview Google introduced two native speech-to-speech models: Gemini 3.8 Live, focused on scale and cost, and Gemini 3.8 Live Extended Thinking, designed for multi-step tasks with reasoning during dialogue. The article discusses direct audio processing, asynchronous function calls, visual context, multilingual conversations, SynthID watermarking, pricing, and availability through the Gemini API and Google AI Studio. ### Source excerpt 15 сентября Google представила две нативные speech-to-speech модели, Gemini 3.8 Live и Gemini 3.8 Live Extended Thinking. Первая заточена под масштаб и цену, вторая -- под многошаговые задачи с рассуждением прямо в диалоге. Интереснее баллов то, что голосовые агенты наконец получили признаки продакшен-продукта. Асинхронные вызовы функций, предсказуемая цена, интеграции с тем, на чем такие системы реально собирают. Хочу напомнить, как это делалось раньше. Голосовой бот -- конвейер из трех сервисов. Распознавание речи, языковая модель и синтез. Задержки складываются, а вместе с текстом может потеряться все остальное -- интонация, скорость речи, эмоция, шум на фоне. Модель получает расшифровку и не знает, что собеседник злится. Проблемным было и перебивание. Пока распознавание не закрыло фразу, система вообще не понимает, что ее прервали. Нативная speech-to-speech модель работает с аудио напрямую и снимает оба ограничения разом. Читать далее ## Gemini Live audio DevFeed: [Gemini Live audio]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-15T22:47:07Z Content type: tutorial Language: en Sources: [Simon Willison's Weblog]() Topics: [speech-to-speech](), [WebSocket](), [Google AI](), [browser](), [Playback](), [implementation](), [API]() Tags: [api](), [audio](), [browser](), [gemini](), [gemini-196](), [generative-ai](), [generative-ai-1-982](), [google](), [google-416](), [llm-release](), [llm-release-231](), [llms](), [llms-1-948](), [model](), [models](), [openai](), [playback](), [release](), [speech-to-speech](), [speech-to-text](), [speech-to-text-21](), [tools](), [tools-78](), [ui](), [voice](), [websocket](), [websockets](), [websockets-21]() ### AI overview The article describes a browser-based web UI for trying Google's Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking speech-to-speech models. The implementation supports model and voice selection, an optional system prompt, voice conversations, and interruption while the model is speaking. It uses no libraries, connecting to a WebSocket endpoint and using the Web Audio API for capture and playback. ### Source excerpt Tool: Gemini Live audio Google released Gemini 3.8 Live and 3.8 Live Extended Thinking today - two new speech-to-speech models that are a similar shape to OpenAI's GPT-Live family. I pointed GPT-6 Astra Extra High at the documentation and had it build me this web UI for trying out the new models. You can select a model and voice preset, enter an optional system prompt and then start a voice conversation through your browser, including the ability to interrupt the model while it is talking. The implementation uses no libraries. It connects to the wss://generativelanguage.googleapis.com/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key=... WebSocket endpoint and uses a Web Audio API AudioContext for both capture and playback. Here's the Gemini Live tutorial for getting started with that WebSockets API. Tags: google, tools, websockets, generative-ai, llms, gemini, llm-release, speech-to-text ## Google and OpenAI take different approaches to reducing voice-agent latency DevFeed: [Google and OpenAI take different approaches to reducing voice-agent latency]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T21:50:15Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Latency](), [Google AI](), [OpenAI](), [API](), [Orchestration](), [real-time](), [Artificial Intelligence]() Tags: [ai](), [ai-agents](), [ai-engineering](), [api](), [comparison](), [google-ai](), [large-language-models](), [latency](), [openai](), [orchestration](), [real-time]() ### AI overview The article compares Google's Gemini 3.8 Live Extended Thinking with OpenAI's GPT-Live-1 for reducing latency in voice agents. Google keeps speech, reasoning, and asynchronous tool execution in one stateful session, while OpenAI uses a real-time conversation model alongside a backend reasoning model, shifting more orchestration to the application. ### Source excerpt Voice agents have a latency problem that shows up as soon as they have to do real work. Within five The post OpenAI's voice model doesn't think. That's the point. appeared first on The New Stack. ## Mastering Edge AI on Raspberry Pi with LiteRT and Gemma DevFeed: [Mastering Edge AI on Raspberry Pi with LiteRT and Gemma]() Original publisher: [Read original article]() Author: Lu Wang; Terry Heo; Naushir Patuck; José María Casanova Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [LiteRT](), [On-device AI](), [Local AI](), [AI, ML & Data Engineering](), [Google AI](), [cpu](), [Security, Privacy and Abuse Prevention]() Tags: [cli](), [cpu](), [edge](), [gemma](), [gpu](), [inference](), [litert](), [offline](), [on-device](), [real-time](), [robotics]() ### AI overview The article explains how to deploy Gemma models with LiteRT on a Raspberry Pi for local, real-time edge AI applications such as robotics. It highlights LiteRT-LM, CPU and GPU optimization, and reported performance figures for Gemma 4 E2B on Raspberry Pi 5. ### Source excerpt Deploying secure, real-time Edge AI on Raspberry Pi is now simplified using LiteRT and lightweight Gemma open models. LiteRT optimizes CPU and GPU performance, delivering fast token speeds for models like Gemma4, enabling real-time local reasoning for robotics. Developers can quickly convert, quantize, and run these models using the lightweight LiteRT CLI tool. Support for Hailo AI accelerators is also coming very soon. ## How to use Google microbenchmarks for evaluating TPU performance DevFeed: [How to use Google microbenchmarks for evaluating TPU performance]() Original publisher: [Read original article]() Author: Junjie Qian; Chi Shuen Lee; Yu-Hsuan (Amy) Lin; Haixiong (Sean) Wang Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Google AI](), [Inference Performance]() Tags: [architecture](), [benchmarks](), [compute](), [developers](), [google](), [guides](), [hardware](), [how-to](), [kernel](), [machine-learning](), [memory](), [mesh](), [metrics](), [model](), [open](), [open-source](), [performance](), [scale](), [software](), [tpu]() ### AI overview A tutorial on using Google's TPU microbenchmark suite to measure network, compute, memory, host-transfer, and attention performance. The results can establish a Roofline baseline and guide workload-specific optimization. ### Source excerpt Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound. This empirical baseline directly guides targeted software optimizations--such as kernel tuning, mesh sharding, and rematerialization--to maximize hardware utilization for large-scale model deployments. ## Introducing agentic video understanding with Gemini DevFeed: [Introducing agentic video understanding with Gemini]() Original publisher: [Read original article]() Author: Rohan Doshi Published: 2026-09-01T17:08:51Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Google]() Tags: [agentic](), [ai](), [analysis](), [api](), [audio](), [benchmarks](), [cost](), [developers](), [enterprise](), [feature](), [flash](), [gemini](), [google-ai](), [models](), [none](), [performance](), [reasoning](), [retrieval](), [tools](), [transcripts](), [video]() ### AI overview Google launches agentic video understanding for Gemini Flash models. It uses native video tools to inspect relevant frames, audio, and transcripts, aiming to improve video-analysis accuracy while reducing token use and cost. ### Source excerpt We're launching agentic video understanding across our latest Gemini models for improved accuracy and lower costs and token usage. ## From Project Description to Funding Opportunity DevFeed: [From Project Description to Funding Opportunity]() Original publisher: [Read original article]() Author: Gabriel Preda Published: 2026-08-31T05:03:07Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [Google AI](), [Streamlit](), [Cloud](), [PostgreSQL](), [postgresql clusters]() Tags: [adk](), [ai](), [building](), [cloud](), [developer](), [gemini](), [google](), [google-adk](), [google-cloud-sql](), [pgvector](), [postgresql](), [search](), [streamlit]() ### AI overview This article describes GrantMatch AI, a Streamlit application that matches project descriptions with potentially relevant grant and funding opportunities. It explains how hybrid keyword and embedding search, Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit are used to connect related concepts despite different wording. ### Source excerpt Building GrantMatch AI with hybrid keyword and embedding search using Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit Continue reading on Google Developer Experts " ## Gemini Omni 1.1 Flash lets you build with more control DevFeed: [Gemini Omni 1.1 Flash lets you build with more control]() Original publisher: [Read original article]() Author: Anish Nangia Published: 2026-08-27T16:11:32Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [API](), [Deployment]() Tags: [ai](), [api](), [cost](), [developer](), [gemini](), [generative](), [none](), [production](), [prototyping](), [video]() ### AI overview Google introduces Gemini Omni 1.1 Flash, adding controllable generative video features for developers through the Gemini API in Google AI Studio. The update supports scene extension, keyframe-based transitions, and faster, lower-cost 360p previews for prototyping and creative workflows. ### Source excerpt Gemini Omni 1.1 Flash brings a new suite of creative controls and generative video capabilities to developers. ## Intelligent transcription with Gemini 3.5 Transcribe DevFeed: [Intelligent transcription with Gemini 3.5 Transcribe]() Original publisher: [Read original article]() Author: Diego Melendo Casado Published: 2026-08-26T17:01:00Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [asr](), [voice ai](), [Google AI](), [API](), [real-time](), [Streaming](), [Latency](), [Artificial Intelligence]() Tags: [ai](), [analysis](), [analytics](), [android](), [api](), [audio](), [developer](), [developers](), [gemini](), [google](), [google-ai](), [latency](), [macos](), [model](), [none](), [real-time](), [speech](), [streaming](), [transcription](), [voice](), [workflows]() ### AI overview Google introduces Gemini 3.5 Transcribe, a speech-to-text model for accurate, formatted transcription in noisy and specialized audio. Developers can use it through the Gemini API in Google AI Studio and Gemini Enterprise Agent Platform for voice agents, real-time captioning, and post-call analytics. It supports real-time streaming and pre-recorded audio processing, custom vocabulary, speaker attribution, word-level timestamps, and more than 85 languages. ### Source excerpt Now you can get more intelligent speech-to-text transcription with Gemini 3.5 Transcribe. ## How to build a Gemini content assistant that belongs inside your Webflow CMS DevFeed: [How to build a Gemini content assistant that belongs inside your Webflow CMS]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-08-08T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Content Management System](), [webflow](), [Google AI](), [Next.js](), [API](), [JSON](), [Streaming](), [Node.js]() Tags: [api](), [gemini](), [google-ai](), [guides](), [how-to](), [json](), [next-js](), [node-js](), [streaming](), [webflow]() ### AI overview A tutorial for building a Gemini-powered content assistant inside Webflow CMS using Webflow Cloud, Next.js, and the Gemini API. It covers streaming drafts for open-ended writing and a JSON mode that returns content mapped to CMS fields. ### Source excerpt Learn how to build a Gemini content assistant on Webflow Cloud that streams drafts and returns CMS-ready JSON. ## \[May-Jun 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[May-Jun 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-07-22T01:22:04Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [Google](), [Google AI](), [AI Agent](), [Agent Skills](), [Model Context Protocol (MCP)](), [Windows Subsystem for Linux](), [Python](), [HTML](), [Command-line interface](), [CSV](), [SDKs]() Tags: [2026](), [ai](), [ai-agent](), [ai-agents](), [antigravity](), [cli](), [community](), [google](), [google-ai](), [here](), [html](), [mcp](), [python](), [sdk](), [skills](), [wsl]() ### AI overview This article highlights May-June 2026 activities and achievements from Google AI communities. It summarizes community projects, codelabs, experiments, and guides involving Google Antigravity, Gemini, the Antigravity SDK, AI agents, MCP servers, agent skills, CLI and IDE workflows, HTML dashboards, CSV data, and Windows Subsystem for Linux. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity [Codelab] Building Trustable AI at 100 MPH by GDEs in the US: Hemanth HM, Vikram Tiwari, Lynn Langit, Sebastian Gomez, Rabimba Karanjai; Googler: Ajeet Mirwani; and the partner: Ocupop guides you in building a trustable AI prototype inspired by the field test from the Unstoppable GDE Cohort. The project summary, Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini and the members of the July cohort were featured on Google for Developers Blog✨. Skills over System Prompts: Building an Anki Tutor with the Antigravity SDK by AI GDE Ertuğrul Demir (Türkiye) demonstrates the value of a modular approach to AI agent design by creating custom Python tools and reusable skill packages for an Anki flashcard tutor using Antigravity SDK. (Image source) Startup on a Shoestring -- Building with a 50c Budget using Google Antigravity Cost & Token Monitors💸 by AI GDE Jigyasa Grover (US) conducted an SDK-powered cost engineering experiment to observe an AI agent prioritize, panic, and ship an MVP landing page under intense budget pressure. Google Antigravity CLI: Orchestrating Parallel AI Agents by AI GDE Aashi Dutt (India) explores how to create an interactive HTML dashboard from a raw CSV file with a single command and explains the benefits of the subagent architecture. (Image source) Configuring MCP Servers and Skills for Antigravity CLI and IDE by Cloud GDE Darren Lester (UK) provides a deep-dive guide on configuring MCP servers and agent skills for the new Antigravity CLI and IDE. He also shared Resolving WSL Friction with Google Antigravity: the Agy 2.0 and Agy IDE Edition discussing how to resolve friction between the Antigravity suite and the Windows Subsystem for Linux environment. Google Antigravity 2 & Gemini 3.5 ## Dodo Payments Joins the Google for Startups Accelerator: India 2026 Cohort DevFeed: [Dodo Payments Joins the Google for Startups Accelerator: India 2026 Cohort]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Google](), [Google AI](), [Artificial Intelligence](), [Software as a service](), [Deployment]() Tags: [ai](), [deployment](), [google](), [google-ai](), [india](), [payments](), [press-release](), [saas](), [startups]() ### AI overview Dodo Payments announces its selection for the Google for Startups Accelerator: India 2026 cohort, one of 20 AI-first startups chosen from roughly 2,500 applications. The article describes Dodo Payments' Merchant of Record and billing infrastructure for AI-first and SaaS companies, and outlines the accelerator's Google AI access, technical mentorship, go-to-market support, and preparation for scaled deployment. ### Source excerpt Dodo Payments is one of 20 AI-first startups selected for the Google for Startups Accelerator: India 2026 cohort, chosen from ~2,500 applications. ## Start building with Nano Banana 2 Lite and Gemini Omni Flash DevFeed: [Start building with Nano Banana 2 Lite and Gemini Omni Flash]() Original publisher: [Read original article]() Author: Alisa Fortin Published: 2026-06-30T16:02:40Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [API](), [text-to-image](), [Artificial Intelligence]() Tags: [ai](), [api](), [benchmarks](), [build-faster](), [building](), [cost](), [efficiency](), [gemini](), [generation](), [generative](), [google-ai](), [images](), [latency](), [life](), [media](), [models](), [none](), [performance](), [prototyping](), [scale](), [video]() ### AI overview Google DeepMind announces Nano Banana 2 Lite, a fast and cost-efficient image model for high-throughput developer pipelines, and makes Gemini Omni Flash available to developers for video generation and conversational editing. The models are offered through Google AI Studio, the Gemini API and Gemini Enterprise Agent Platform. ### Source excerpt Scale your ideas with Nano Banana 2 Lite, our fastest, most cost-efficient Gemini Image model, and Gemini Omni Flash for high-quality video and conversational editing. ## Introducing computer use in Gemini 3.5 Flash DevFeed: [Introducing computer use in Gemini 3.5 Flash]() Original publisher: [Read original article]() Author: Mateo Quiros Published: 2026-06-24T16:30:01Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [AI Bots](), [Developer Tools]() Tags: [agentic](), [api](), [automation](), [browser](), [computer-use](), [enterprise](), [flash](), [gemini](), [none](), [safety](), [software-testing](), [tool]() ### AI overview Gemini 3.5 Flash adds built-in computer use for building agents that act across browser, mobile, and desktop environments. The release also describes safeguards for sensitive actions and indirect prompt injection. ### Source excerpt A look at the built-in computer use tool in Gemini 3.5 Flash. ## Fluid, natural voice translation with Gemini 3.5 Live Translate DevFeed: [Fluid, natural voice translation with Gemini 3.5 Live Translate]() Original publisher: [Read original article]() Author: Anuda Weerasinghe Published: 2026-06-09T15:16:25Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Language models]() Tags: [ai](), [api](), [audio](), [developer](), [gemini](), [google](), [google-ai](), [none](), [real-time](), [speech](), [speech-to-speech](), [streaming](), [voice]() ### AI overview Google announces Gemini 3.5 Live Translate, an audio model for continuous speech-to-speech translation that preserves vocal qualities across more than 70 languages. Developers can access it in public preview through the Gemini Live API and Google AI Studio. ### Source excerpt Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet. ## We're launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks DevFeed: [We're launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks]() Original publisher: [Read original article]() Author: Dr. Ramine Tinati Published: 2026-05-21T19:46:42Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Frontier AI](), [Google AI](), [Artificial Intelligence]() Tags: [ai](), [energy](), [frontier-ai](), [global](), [google](), [google-ai](), [growth](), [none](), [scale](), [science](), [solutions](), [startups]() ### AI overview Google DeepMind is launching a three-month Asia-Pacific accelerator focused on using frontier AI for environmental challenges. Startups, research teams and nonprofits will receive mentorship, tailored support and help integrating Google AI and science AI models into climate, nature, agriculture and energy projects. ### Source excerpt The Asia-Pacific region is a global engine for economic growth, but it's also highly vulnerable to climate change. While green technologies are gaining momentum, a recen... ## Simulate real-world places with Project Genie and Street View DevFeed: [Simulate real-world places with Project Genie and Street View]() Original publisher: [Read original article]() Author: Diego Rivas Published: 2026-05-17T19:53:18Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [World models](), [Artificial Intelligence](), [Google](), [Google AI]() Tags: [agents](), [ai](), [ai-agents](), [generative](), [google](), [google-ai](), [model](), [models](), [none]() ### AI overview Google DeepMind describes a new Street View grounding capability for Project Genie, a general-purpose world model that generates interactive environments. The capability connects real-world imagery with Genie to create imaginative, explorable worlds based on places in the United States, with broader availability planned. ### Source excerpt We're expanding access to Google AI Ultra subscribers globally and introducing a new capability powered by Street View. ## Gemini 3.5: frontier intelligence with action DevFeed: [Gemini 3.5: frontier intelligence with action]() Original publisher: [Read original article]() Author: Koray Kavukcuoglu Published: 2026-05-15T22:50:12Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [coding](), [API](), [Google]() Tags: [agentic](), [agents](), [ai](), [benchmarks](), [coding](), [gemini](), [google](), [google-ai](), [latency](), [models](), [multimodal](), [none](), [performance](), [speed](), [tokens](), [workflows]() ### AI overview Gemini 3.5 Flash is introduced as a frontier model for agentic tasks and coding. The article highlights its benchmark performance, multimodal understanding, speed, lower cost, and ability to support long-horizon workflows, application development, code maintenance, and collaborative subagents. ### Source excerpt Gemini 3.5 is built to help you execute complex, agentic workflows. ## Gemini 3.1 Flash TTS: the next generation of expressive AI speech DevFeed: [Gemini 3.1 Flash TTS: the next generation of expressive AI speech]() Original publisher: [Read original article]() Author: Vilobh Meshram Published: 2026-04-15T16:03:19Z Content type: news Language: en Sources: [Google DeepMind News]() Topics: [voice ai](), [Google AI](), [Artificial Intelligence](), [API](), [Google](), [Benchmark]() Tags: [ai](), [api](), [audio](), [benchmark](), [building](), [developer](), [gemini](), [generation](), [google](), [none](), [text-to-speech](), [updates]() ### AI overview Google introduces Gemini 3.1 Flash TTS, a text-to-speech model designed for more controllable, expressive, and natural AI speech. It offers native multi-speaker dialogue, support for more than 70 languages, audio tags for directing vocal style and delivery, and availability through the Gemini API, Google AI Studio, Vertex AI, and Google Vids. ### Source excerpt Our newest audio model introduces granular audio tags that give you precise control to direct AI speech for expressive audio generation. ## Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning DevFeed: [Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning]() Original publisher: [Read original article]() Author: Laura Graesser; Peng Xu Published: 2026-04-13T15:52:13Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Robotics](), [Google AI](), [API](), [Artificial Intelligence](), [Google]() Tags: [ai](), [api](), [autonomous](), [developer](), [gemini](), [google-ai](), [models](), [reasoning](), [robotics](), [tools](), [vision]() ### AI overview Google DeepMind introduces Gemini Robotics-ER 1.6, a reasoning-focused model for robots that improves spatial reasoning, multi-view understanding, task planning, success detection, and instrument reading. The model is available to developers through the Gemini API and Google AI Studio, with a developer Colab providing configuration and prompting examples. ### Source excerpt Gemini Robotics ER 1.6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics. ## Google Workspace's continuous approach to mitigating indirect prompt injections DevFeed: [Google Workspace's continuous approach to mitigating indirect prompt injections]() Original publisher: [Read original article]() Author: Google GenAI Security Team Published: 2026-04-02T19:14:00Z Content type: article Language: en Sources: [Security]() Topics: [Application Security](), [Language models](), [Google AI](), [Vulnerabilities]() Tags: [ai](), [attacks](), [gemini](), [google](), [llms](), [none](), [security](), [testing](), [vulnerabilities]() ### AI overview Google describes its continuous defense strategy against indirect prompt injection in Workspace with Gemini. The article covers attack discovery through human and automated red-teaming, plus collaboration with external researchers through its AI Vulnerability Rewards Program. ### Source excerpt Indirect prompt injection (IPI) is an evolving threat vector targeting users of complex AI applications with multiple data sources, such as Workspace with Gemini. This t... ## Google Workspace's continuous approach to mitigating indirect prompt injections DevFeed: [Google Workspace's continuous approach to mitigating indirect prompt injections]() Original publisher: [Read original article]() Author: Kimberly Samra (noreply@blogger.com) Published: 2026-04-02T16:00:00Z Content type: opinion Language: en Sources: [Google Online Security]() Topics: [prompt injection](), [Google](), [Security](), [Adversarial attacks](), [Machine Learning, Security Attacks](), [Machine learning](), [Artificial Intelligence](), [Google AI]() Tags: [ai](), [attacks](), [automated](), [discovery](), [google](), [llms](), [machine-learning](), [none](), [prompt](), [prompt-injection](), [red-teaming](), [research](), [safety](), [security](), [testing]() ### AI overview Google describes its ongoing approach to mitigating indirect prompt injection attacks against Workspace with Gemini. The approach includes discovering new attack vectors, human and automated red-teaming, and collaboration with external researchers through the Google AI Vulnerability Rewards Program. ### Source excerpt Posted by Adam Gavish, Google GenAI Security Team Indirect prompt injection (IPI) is an evolving threat vector targeting users of complex AI applications with multiple data sources, such as Workspace with Gemini. This technique enables the attacker to influence the behavior of an LLM by injecting malicious instructions into the data or tools used by the LLM as it completes the user's query. This may even be possible without any input directly from the user. IPI is not the kind of technical problem you "solve" and move on. Sophisticated LLMs with increasing use of agentic automation combined with a wide range of content create an ultra-dynamic and evolving playground for adversarial attacks. That's why Google takes a sophisticated and comprehensive approach to these attacks. We're continuously improving LLM resistance to IPI attacks and launching AI application capabilities with ever-improving defenses. Staying ahead of the latest indirect prompt injection attacks is critical to our mission of securing Workspace with Gemini. In our previous blog "Mitigating prompt injection attacks with a layered defense strategy", we reviewed the layered architecture of our IPI defenses. In this blog, we'll share more detail on the continuous approach we take to improve these defenses and to solve for new attacks. New attack discovery By proactively discovering and cataloging new attack vectors through internal and external programs, we can identify vulnerabilities and deploy robust defenses ahead of adversarial activity. Human Red-Teaming Human Red-Teaming uses adversarial simulations to uncover security and safety vulnerabilities. Specialized teams execute attacks based on realistic user profiles to exploit weaknesses, coordinating with product teams to resolve identified issues. Automated Red-Teaming Automated Red-Teaming is done via dynamic, machine-learning-driven frameworks to stress-test environments. By algorithmically generating and iterating on attack payloads, we can mimi ## Reimagining the mouse pointer for the AI era DevFeed: [Reimagining the mouse pointer for the AI era]() Original publisher: [Read original article]() Author: Adrien Baranes; Rob Marchant Published: 2026-03-29T10:50:49Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [User Interfaces](), [Google AI](), [Google](), [GUI]() Tags: [ai](), [chrome](), [gemini](), [google](), [google-ai](), [research](), [user-interfaces]() ### AI overview Google DeepMind explores an AI-enabled mouse pointer that understands the context and purpose of what users point at. Powered by Gemini, the experimental concept aims to make AI collaboration more seamless across websites, documents, workflows, Chrome, and tools such as Google AI Studio. ### Source excerpt Google DeepMind is transforming the mouse pointer into a context-aware AI partner. Move beyond the friction of traditional prompting with intuitive AI collaboration in Chrome and beyond. [Next page]()