# Google AI Published articles for Google AI. 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 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. ## 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. ## 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. ## \[July 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[July 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-08-24T02:23:20Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [AI Agent](), [Agentic development](), [Artificial Intelligence](), [SDK](), [Orchestration](), [Computer vision](), [Microsoft Agent Framework]() Tags: [2026](), [agentic-development](), [ai](), [ai-agents](), [computer-vision](), [google](), [google-ai](), [google-antigravity](), [orchestration](), [sdk]() ### AI overview A July 2026 roundup highlights Google AI community projects built with the Antigravity SDK and related tools. The featured work covers asynchronous triggers, autonomous and self-correcting agents, approval-gated workflows, computer vision operations, and parallel multi-agent orchestration. ### 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 Antigravity has no task queue. Meet @trigger, its real async primitive by AI GDE Omotayo Aina (UK) explores the design philosophy behind Antigravity SDK, detailing how it leverages asyncio and triggers instead of a traditional task queue. It demonstrates how to construct asynchronous patterns like bounded task queues and cron-like scheduling using this minimalist primitive. https://medium.com/media/0311867ab42ff4749c6db6e2653e2716/href Inside the /goal Loop: How to Build Autonomous AI Agents (repository) by GDE Alexander Amin (Germany) explores the architecture of a custom autonomous agent built with Antigravity SDK that coordinates a multi-agent squad to retrieve data and edit documents. It demonstrates how to implement human gate policies and maintain secure, production-ready agentic loops. Anatomy of a Self-Correcting Agent -- How /goal Closes the Loop in Antigravity by AI GDE Krupa Galiya (India) is a framework with a live dashboard to analyze an AI agent's self-correction process. It examines how agents respond to intentional failures through a loop of verification, diagnosis, replanning, and retrying. image source VisionOps Crew: A Multi-Agent Architecture for Computer Vision Operations Using Google ADK and the Antigravity SDK (repository) by AI GDE Henry Ruiz (US) introduces a multi-agent assistant designed to address fragmentation in computer vision engineering using ADK and Antigravity SDK. Henry leverages specialized agents and external tool integrations to coordinate model discovery, data inspection, and workflow execution. EscrowGuard: Building Approval-Gated AI Agents with the Google Antigravity SDK (repository) by AI GDE Aye Hninn Khine (Thailand) leverages Antigravity SDK to build a multi-agent architecture wi ## 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. ## 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 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. ## 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. ## Gemini 3.1 Flash Live: Making audio AI more natural and reliable DevFeed: [Gemini 3.1 Flash Live: Making audio AI more natural and reliable]() Original publisher: [Read original article]() Author: Valeria Wu Published: 2026-03-26T15:23:35Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google AI](), [API](), [real-time](), [Benchmark](), [Google]() Tags: [ai](), [api](), [audio](), [benchmark](), [developers](), [enterprise](), [gemini](), [google](), [google-ai](), [latency](), [launch](), [model](), [none](), [real-time](), [scale](), [search](), [speed](), [train](), [voice]() ### AI overview Google DeepMind introduces Gemini 3.1 Flash Live, a real-time audio and voice model designed to make dialogue more natural, reliable, and responsive. The article highlights improved reasoning, multi-step task execution, tonal understanding, multilingual capability, lower latency, and faster responses for developers, enterprises, and users across Gemini products, the Gemini Live API, Search Live, and Gemini Enterprise for Customer Experience. ### Source excerpt Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise. ## Lyria 3 Pro: Create longer tracks in more DevFeed: [Lyria 3 Pro: Create longer tracks in more]() Original publisher: [Read original article]() Author: Myriam Hamed Torres Published: 2026-03-25T16:01:39Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Google AI](), [Artificial Intelligence](), [API](), [App]() Tags: [ai](), [api](), [app](), [audio](), [creative-tools](), [creativity](), [developers](), [experiment](), [gemini](), [generate](), [generation](), [google](), [google-ai](), [integrations](), [marketing](), [model](), [music](), [none](), [products](), [tool](), [vertex]() ### AI overview Lyria 3 Pro is Google's advanced music generation model for creating tracks up to three minutes long with greater customization and structural awareness. The announcement covers its availability across Vertex AI, Google AI Studio, the Gemini API, Google Vids, the Gemini app, and ProducerAI. ### Source excerpt Introducing Lyria 3 Pro, which unlocks longer tracks with structural awareness. We're also bringing Lyria to more Google products and surfaces. ## A new way to express yourself: Gemini can now create music DevFeed: [A new way to express yourself: Gemini can now create music]() Original publisher: [Read original article]() Author: Joël Yawili Published: 2026-02-18T16:01:38Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google AI](), [Google](), [App](), [synthid]() Tags: [ai](), [ai-content](), [audio](), [creators](), [gemini](), [generation](), [google](), [google-ai](), [images](), [music](), [none](), [synthid]() ### AI overview Google DeepMind announces Lyria 3, a generative music model rolling out in beta in the Gemini app. Users can create 30-second tracks from text, photos, or videos, with generated lyrics, customizable musical elements, and custom cover art. Tracks include SynthID watermarking to help identify Google AI-generated content. ### Source excerpt The Gemini app now features our most advanced music generation model Lyria 3, empowering anyone to make 30-second tracks using text or images. ## Project Genie: Experimenting with infinite, interactive worlds DevFeed: [Project Genie: Experimenting with infinite, interactive worlds]() Original publisher: [Read original article]() Author: Diego Rivas Published: 2026-01-29T17:01:05Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [World models](), [Artificial Intelligence](), [Google AI](), [Simulation](), [Web app]() Tags: [3d](), [ai](), [experimental](), [google](), [google-ai](), [none](), [prototype](), [research](), [research-prototype](), [simulation](), [web](), [web-app](), [world-model]() ### AI overview Google is opening Project Genie to Google AI Ultra subscribers in the U.S. The experimental research prototype, powered by Genie 3, Nano Banana Pro and Gemini, lets users create, explore and remix interactive worlds. ### Source excerpt Google AI Ultra subscribers in the U.S. can try out Project Genie, an experimental research prototype that lets you create and explore worlds. ## Gemini 3 Flash: frontier intelligence built for speed DevFeed: [Gemini 3 Flash: frontier intelligence built for speed]() Original publisher: [Read original article]() Author: Tulsee Doshi Published: 2025-12-17T11:58:17Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [agentic workflows](), [multimodal](), [API](), [Code](), [Google](), [google-antigravity]() Tags: [agentic](), [agentic-workflows](), [ai](), [ai-studio](), [antigravity](), [api](), [benchmarks](), [cost](), [development](), [efficiency](), [gemini](), [google-ai](), [google-antigravity](), [latency](), [none](), [performance](), [platform](), [reasoning](), [release](), [speed](), [tokens]() ### AI overview Google announces Gemini 3 Flash, a faster and more efficient model in the Gemini 3 family. The release targets developers, consumers, and enterprises across Google's platforms, with support for reasoning, multimodal understanding, and agentic workflows. ### Source excerpt Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost. ## How we're bringing AI image verification to the Gemini app DevFeed: [How we're bringing AI image verification to the Gemini app]() Original publisher: [Read original article]() Author: Pushmeet Kohli Published: 2025-11-20T15:13:19Z Content type: news Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [AI Chat]() Tags: [ads](), [ai](), [app](), [audio](), [content](), [ecosystem](), [embedded](), [feature](), [gemini](), [generative](), [google](), [google-ai](), [images](), [launch](), [media](), [none](), [partners](), [product](), [products](), [reasoning](), [research]() ### AI overview Google is adding Gemini app image verification that checks SynthID watermarks to indicate whether an image was created or edited with Google AI. The company also plans broader support for video, audio, C2PA metadata, and additional Google products. ### Source excerpt Our new Gemini app feature allows you to verify Google AI images and determine whether content was created or edited by AI. ## How Android and Google Messages protect users from mobile scams DevFeed: [How Android and Google Messages protect users from mobile scams]() Original publisher: [Read original article]() Author: Edward Fernandez (noreply@blogger.com) Published: 2025-10-30T16:59:00Z Content type: article Language: en Sources: [Google Online Security]() Topics: [Android](), [Cybersecurity](), [Security](), [Mobile](), [Google AI](), [iOS](), [Google](), [iphone](), [Messaging]() Tags: [ai](), [android](), [android-security](), [cybersecurity](), [google](), [google-ai](), [ios](), [iphone](), [messaging](), [mobile](), [none](), [pixel](), [protection](), [report](), [scams](), [security](), [smartphone](), [survey]() ### AI overview This article describes Android's layered protections against mobile scams, including defenses for malicious calls and messages and safety checks for RCS. It also presents a Google and YouGov survey comparing reported scam experiences and confidence among Android and iOS users in the U.S., India, and Brazil. ### Source excerpt Posted by Lyubov Farafonova, Product Manager, Phone by Google; Alberto Pastor Nieto, Sr. Product Manager Google Messages and RCS Spam and Abuse; Vijay Pareek, Manager, Android Messaging Trust and Safety As Cybersecurity Awareness Month wraps up, we're focusing on one of today's most pervasive digital threats: mobile scams. In the last 12 months, fraudsters have used advanced AI tools to create more convincing schemes, resulting in over $400 billion in stolen funds globally.¹ For years, Android has been on the frontlines in the battle against scammers, using the best of Google AI to build proactive, multi-layered protections that can anticipate and block scams before they reach you. Android's scam defenses protect users around the world from over 10 billion suspected malicious calls and messages every month2. In addition, Google continuously performs safety checks to maintain the integrity of the RCS service. In the past month alone, this ongoing process blocked over 100 million suspicious numbers from using RCS, stopping potential scams before they could even be sent. To show how our scam protections work in the real world, we asked users and independent security experts to compare how well Android and iOS protect you from these threats. We're also releasing a new report that explains how modern text scams are orchestrated, helping you understand the tactics fraudsters use and how to spot them. Survey shows Android users' confidence in scam protections Google and YouGov3 surveyed 5,000 smartphone users across the U.S., India, and Brazil about their scam experiences. The findings were clear: Android users reported receiving fewer scam texts and felt more confident that their device was keeping them safe. Android users were 58% more likely than iOS users to say they had not received any scam texts in the week prior to the survey. The advantage was even stronger on Pixel, where users were 96% more likely than iPhone owners to report zero scam texts4. At the other end o ## Now in Android #116 DevFeed: [Now in Android #116]() Original publisher: [Read original article]() Author: Meghan Mehta Published: 2025-05-09T15:58:03Z Content type: article Language: en Sources: [Android Developers - Medium]() Topics: [now-in-android](), [Compose](), [android-development](), [Jetpack](), [Google](), [Google AI]() Tags: [android](), [android-16](), [bug-fixes](), [compose](), [featured](), [google](), [google-ai](), [google-io-2025](), [google-play](), [jetpack](), [jetpack-compose](), [now-in-android](), [release](), [upgrade]() ### AI overview Now in Android #116 covers the Google I/O program, Jetpack Compose 1.8, the redesigned Play Console app dashboard, new Android Vitals metrics, the Testing at Scale series, Android 16 Beta 4, and recent AndroidX updates. ### Source excerpt Google IO program lineup, Jetpack Compose 1.8, Play Console insights Android Vitals Metrics, Testing at Scale blog series and AndroidX! Welcome to Now in Android, your ongoing guide to what's new and notable in the world of Android development. In this edition, we'll cover the Google IO program lineup, what's new in Jetpack Compose 1.8, the redesigned Play Console app dashboard, new Android Vitals Metrics, the launch of the Testing at Scale blog series, and the latest in AndroidX. https://medium.com/media/f791874912c70580592d3dc0ac12e0fa/hrefhttps://medium.com/media/514683104227eecfef6dcedea0d6ddb4/href Most of the content of this post is available in the form of a video or podcast, so feel free to watch or listen rather than read on. (Or do all three to help you remember! There won't be a quiz.) Articles 📚Get ready for Google I/O: Program lineup revealed 🗓 The Google I/O agenda is now available, and you can register to explore sessions on AI, Android, Web, and Cloud, taking place May 20-21. The Google Keynote will be on May 20th at 10:00 AM PT, with the Developer Keynote at 1:30 PM PT. You can join online for livestreams May 20-21, with on-demand sessions and codelabs on May 22. Sessions will cover AI advancements using Gemini models, building apps for multiple devices using Google AI, and new features for web development. Get ready for Google I/O: Program lineup revealed What's new in the Jetpack Compose April '25 release 🚀 Jetpack Compose 1.8 is out with new features, API updates, and bug fixes. You can upgrade your Compose BOM version to 2025.04.01 to use the new release. Here are some of the key updates: You can now integrate Autofill functionality into your Compose applications. The new autoSize parameter lets the text size adapt to the container size The onLayoutRectChanged modifier solves many use cases that the existing onGloballyPositioned modifier does; however, it does so with much less overhead. LookaheadScope is stable and includes numerous performanc ## Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications DevFeed: [Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-12-05T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [Training AI Models](), [machine learning overfitting]() Tags: [ai](), [benchmarks](), [community](), [computer-vision](), [data-quality](), [datasets](), [edge](), [google-ai](), [litert](), [machine-learning](), [on-device-ai](), [person-detection](), [research](), [tensorflow-lite](), [training]() ### AI overview Wake Vision is a roughly 6-million-image dataset for TinyML person detection. It provides large and quality-focused training sets, emphasizing that accurate labels can be especially valuable for constrained models. ### Source excerpt Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi - Harvard University TinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in--a new dataset designed to accelerate research and development in TinyML. Why TinyML Needs Better Data The development of TinyML requires compact and efficient models, often only a few hundred kilobytes in size. The applications targeted by standard machine learning datasets, like ImageNet, are not well-suited for these highly constrained models. Existing datasets for TinyML, like Visual Wake Words (VWW), have laid the groundwork for progress in the field. However, their smaller size and inherent limitations pose challenges for training production-grade models. Wake Vision builds upon this foundation by providing a large, diverse, and high-quality dataset specifically tailored for person detection--the cornerstone vision task for TinyML. What Makes Wake Vision Different? Wake Vision is a new, large-scale dataset with roughly 6 million images, almost 100 times larger than VWW, the previous state-of-the-art dataset for person detection in TinyML. The dataset provides two distinct training sets: Wake Vision (Large): Prioritizes dataset size. Wake Vision (Quality): Prioritizes label quality. Wake Vision's comprehensive filtering and labeling process significantly enhances the dataset's quality. Why Data Quality Matters for TinyML Models In traditional overparameterized models, it is widely believed that data quantity matters more than data quality, as an overparameterized model can adapt to errors in the training data. But according to the image below, TinyML tells a different story: The figure above shows that high-quality labels (less error) are more beneficial for under-parameterized mo [Next page]()