# google-antigravity Google Antigravity is an agentic development platform with an AI-powered IDE, CLI, SDK, and tools for orchestrating autonomous agents and software engineering tasks. 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 ## Customizing Angular Aria Tabs Quickly with Google Antigravity CLI DevFeed: [Customizing Angular Aria Tabs Quickly with Google Antigravity CLI]() Original publisher: [Read original article]() Author: Angular Published: 2026-09-02T01:50:59Z Content type: tutorial Language: en Sources: [Angular]() Topics: [Angular](), [Accessibility](), [google-antigravity](), [CSS](), [User interface design]() Tags: [accessibility](), [angular](), [antigravity](), [css](), [material-design](), [ui]() ### AI overview This tutorial tests how far Angular Aria tabs can be customized while preserving accessibility. It uses Google Antigravity's coding agent and a /goal command to transform a basic tab interface into a neumorphic design, then addresses contrast problems identified by Lighthouse. After a correction, the application reaches a 100 accessibility score. ### Source excerpt The Angular team has called Angular Aria a set of customizable, fully accessible UI patterns. This is a strong claim, but in practice, how possible is it to heavily customize the components while maintaining accessibility features such as keyboard navigation and focus management? In this post, we're going to use Google Antigravity to put that claim to the test by updating a tab UI from generic styles to a neumorphism-based design. Motivation In the angular.dev documentation, the team has examples with a set of basic, Material Design-based, and retro styles meant to show developers what's possible with these new accessibility patterns. Here's the challenge: take the tabs basic styles example further by implementing the following neumorphism-inspired design. Here's a look at the UI before any changes: Angular Aria tabs component with basic styles applied The Lighthouse accessibility report shows a 100 score before making any changes: Accessibility score for the tabs based on the original styles Neumorphic designs are typically criticized for their poor accessibility. This experiment is designed to push the coding agent with complex CSS styles while stress-testing the customizability of Angular Aria tabs. Additionally, I'll ask the agent to update any accessibility issues that come up. Making the changes The styles for this UI are well organized, but require a lot of changes to achieve something like a neumorphic design. Instead of writing them by hand, Google Antigravity's coding agent can make the changes. Here's the prompt: /goal update the tabbed UI to be neumorphic style. The example UI is @example-ui.jpeg and the styles are in @src/app/app.css A few things to note here: This example used a reference image for inspiration (@example-ui.jpeg) to inform the designs. This example also uses the /goal command in Google Antigravity instructs the agent to work a task to completion without asking the user for immediate input. The Result Here's the resulting UI based on the ## \[July 2026\] 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 ## Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram DevFeed: [Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram ]() Original publisher: [Read original article]() Author: Nicola Guglielmi Published: 2026-08-21T12:12:57Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [MCP Server](), [cursor](), [Claude Code](), [Terminal](), [Code]() Tags: [agent](), [ai](), [ai-agent](), [ai-coding](), [antigravity](), [coding](), [google-antigravity](), [mcp](), [mcp-server](), [telegram-bot](), [terminal](), [vibe-coding]() ### AI overview This tutorial introduces Telegravity, a single-binary MCP server that connects Telegram with AI coding agents such as Google Antigravity, Claude Code, and Cursor. It describes remote instructions, live status, conversation history, workspace selection, and an active mode for reacting to Telegram messages. ### Source excerpt Your agent runs for 20 minutes. You walk away from the laptop. What if your phone became your mission control? We are living a strange new moment in the software industry. We kick off an AI coding agent: Antigravity, Claude Code, Cursor, give it a task, and then... we sit there. Watching a terminal. Babysitting a process that might run for two minutes or for two hours. That always felt wrong to me. The whole promise of an autonomous agent is that it works while you live your life. But the moment you stand up to grab a coffee, you go blind: you can't see what it's doing, you can't nudge it, you can't say "wait, not that file" until you're back at the keyboard. So I built the missing piece I needed. I call the project Telegravity, the uplink between Telegram and my AI coding agent. In this article I want to show how it turns Antigravity into something you can drive remotely, from the same chat app you already check fifty times a day. Let's launch. 🚀 https://github.com/nicolaguglielmi/Telegravity The idea explained in one sentence Telegravity is a single-binary MCP server that exposes a tiny set of tools to your agent, pull instructions, post live status, stream conversation history, while the Telegram side gives you a polished dashboard, a conversation hub, and an Active Mode that wakes the agent the instant you type. That's it. One process. No cloud service in the middle, no extra account, no telemetry. Your bot token never leaves your machine. End to end, the whole conversation stays private. The magic is that it speaks the Model Context Protocol (MCP), so it doesn't care which agent you use. Antigravity is a first-class citizen, but the exact same setup works for Claude Code, Cursor, Cline, or anything else that speaks MCP. What you actually get Before the setup, here's the payoff, so you know what we're building toward: A live dashboard with an agent heartbeat: 💭 Thinking - ⚡ Executing - ✅ Done an unread inbox counter, the current workspace, and a chat-mode badge. A ## LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0 DevFeed: [LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0]() Original publisher: [Read original article]() Author: Esther Irawati Setiawan Published: 2026-07-29T09:40:30Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [AI Engineering](), [google-antigravity](), [Agent Harness](), [SDKs](), [Command-line interface](), [Code]() Tags: [agent-harness](), [agentic-ai](), [ai](), [ai-engineering](), [antigravity](), [cli](), [google-antigravity](), [pipelines](), [sdk](), [verification]() ### AI overview A guide to using Antigravity 2.0 to build closed-loop AI engineering workflows. It presents a state-machine pattern in which an agent writes, runs, and fixes code against a defined objective until the output is verified, while noting that the SDK is pre-v1.0 and its documented interfaces may change. ### Source excerpt LoopSmith: Closed-Loop AI Engineering -- Autonomous /goal Execution for Self-Correcting Pipelines on Antigravity 2.0Stop prompting the agent turn by turn. Hand it an objective, a bar to clear, and let the state machine write, run, and fix its own code until the output is verified.This guide targets Antigravity 2.0 -- the four-surface release (desktop app, agy CLI, google-antigravity SDK, and enterprise cloud) that shares one agent harness. The SDK is pre-v1.0; symbol names and CLI flags below reflect the documented API as of mid-2026. Treat the patterns as stable and re-check exact signatures against the current docs before you ship.Table of contents The problem with the on-demand agent The architectural shift: closed-loop engineering as a state machine Step 1 -- Initialize the project and the state machine (agy) Step 2 -- Define the objective and trigger /goal execution mode (SDK) Step 3 -- Implement the self-correcting loop Step 4 -- Verification and state finalization Conclusion 1. The problem with the on-demand agent Most "AI engineering" today is still conversational. You prompt; the model answers. You notice the answer is wrong; you prompt again. You paste a traceback; it apologizes and tries once more. The intelligence is real -- but you are the control loop. You are the thing that runs the code, reads the error, decides whether the output is good enough, and feeds the next instruction back in. Take the human out of that seat and the whole system stops. That's fine for a chat window. It falls apart the moment you want an agent to produce a deliverable -- a cleaned dataset, a reconciled financial report, a migration that actually compiles. Real analytical work is iterative and self-referential: you write a script, it crashes on a currency string, you fix the parse, it runs but the totals don't reconcile, you fix the aggregation, and only then is the output trustworthy. Every one of those arrows is a decision. An on-demand agent makes you supply all of them. There are ## \[May-Jun 2026\] 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 ## Google joins the Eclipse Foundation as a strategic member to accelerate AI-integrated developer tools DevFeed: [Google joins the Eclipse Foundation as a strategic member to accelerate AI-integrated developer tools]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-10T18:30:00Z Content type: news Language: en Sources: [Google Open Source Blog]() Topics: [Google](), [Open Source](), [Artificial Intelligence](), [dev-tools](), [google-antigravity](), [Extension](), [Critical Infrastructure]() Tags: [ai](), [ai-integrated](), [antigravity](), [critical-infrastructure](), [developer-tools](), [eclipse-foundation](), [google](), [google-ospo](), [open-source](), [open-source-security]() ### AI overview Google joined the Eclipse Foundation as a Strategic Member in April 2026. It will sponsor Open VSX, participate in the foundation's governance, and support open infrastructure for AI-integrated developer platforms, along with open source security and regulatory compliance initiatives. ### Source excerpt by amanda casari & Mike Bufano, Google Open Source Collaboration with the Eclipse Foundation will support open infrastructure for AI-integrated developer platforms like Google Antigravity, while advancing broader open source security and regulatory compliance initiatives As of April 2026, Google has joined the Eclipse Foundation as a Strategic Member, reflecting the company's continued investment in open source technologies and modern developer infrastructure. As part of this collaboration, Google will additionally sponsor Open VSX and is among the first adopters of the recently announced Open VSX Managed Registry service. Open VSX is the open source, vendor-neutral extension registry for tools built on the VS Code™ extension API. It powers a rapidly growing ecosystem of AI-integrated IDEs, cloud development environments, and developer platforms, including Google Antigravity, AWS's Kiro, Cursor, and, Windsurf among many others. As a Strategic Member, Google will participate in the Eclipse Foundation's Board of Directors and Technical Advisory Council, helping guide the technical and strategic direction of one of the world's leading open source software foundations. "The industry is feeling the massive turning point as AI continues to change how developers write, deploy, and maintain software," said amanda casari of Google's Open Source Programs Office and new Eclipse Board member. "Joining The Eclipse Foundation as a Strategic Member ensures that the next generation of AI-integrated developer experiences--including platforms like Google Antigravity--are built in partnership with transparent, vendor-neutral foundations. Open registries, like Open VSX, are critical infrastructure which keep the global developer ecosystem open to everyone." Google and the Eclipse Foundation share a deep history, having collaborated across numerous initiatives since 2006. This Strategic Membership elevates the relationship and support critical to modern initiatives like Open VSX, Open Reg ## Github Copilot vs Google Antigravity DevFeed: [Github Copilot vs Google Antigravity]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-03-22T23:19:06Z Content type: comparison Language: en Sources: [Alex Ewerlof Notes]() Topics: [GitHub Copilot](), [google-antigravity](), [GitHub Copilot CLI](), [Google](), [Chrome]() Tags: [browser](), [gemini-cli](), [github-copilot](), [google](), [google-antigravity]() ### AI overview A firsthand comparison of GitHub Copilot and Google Antigravity based on the author's experience using both tools. The article describes Antigravity's browser integration and Plan mode, while noting that its newer product maturity is visible compared with Copilot. ### Source excerpt Why Github gets developers and why it's hard to tell who Antigravity is for