# google-antigravity Published articles for google-antigravity. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[July 2026\] 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 ## Learning faster with Antigravity DevFeed: [Learning faster with Antigravity]() Original publisher: [Read original article]() Author: Andrew Brogdon Published: 2026-07-01T04:08:53Z Content type: article Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [Agent Skill](), [ai-coding](), [Python](), [Development](), [SDK]() Tags: [agent-skill](), [agentic-ai](), [ai-coding](), [architecture](), [development](), [flutter](), [gemini](), [google-antigravity](), [ios](), [multi-agent](), [python](), [sdk](), [web-development]() ### AI overview The article describes using Antigravity and a structured, iterative workflow to learn Python-based Agent Development Kit (ADK) concepts and build Flutter frontends for an existing multi-agent research coordinator. The author created a reusable developer skill, flutter_frontend_for_adk, with reference documents covering agent analysis, frontend usage, architecture, and design. ### Source excerpt Dash enjoying AntigravityCreating Flutter frontends for ADK How can I build a Flutter frontend for an agent when that agent is built with an SDK and a language I've never used before? This was the challenge I faced when approaching a Python-based agent written with Agent Development Kit (ADK). With limited experience in Python and no prior exposure to the ADK framework, building a client that integrates with the backend server presented a significant learning curve. Plus, even if I could get a coding agent to crank out something that worked, finishing the project without understanding the code was also a form of failure. After a few false starts, though, I found an answer. Using a structured, iterative workflow with my AI coding partner, Antigravity, I created a reusable developer skill that codified what I learned with each go-round. I started from scratch, generated notes about the code, created multiple apps that connected to the deep_search agent (a multi-agent research coordinator from the official ADK samples repository), and incrementally built up the skill and my own understanding. At times, I was using multiple agents at the same time, an "author" agent to create the skill with me, and a "coder" agent to use that guidance to build frontends. What I ended up with was an agent skill called flutter_frontend_for_adk. It includes five reference docs that guide Antigravity through a sequence of phases, each one ending in a deliverable. The first phases generated the following notes files, so that I could structure how the "coder" agent thought about the task as it analyzed the agent and prepared to generate the app: AGENT_INTERFACE_NOTES.md -- Notes taken during an analysis of the agent's source code. What is it meant to accomplish and how is it constructed? What are the interfaces and APIs this agent exposes and how do they work? FRONTEND_USAGE_NOTES.md -- The first spec. What should the frontend do and how should users interact with it? FRONTEND_ARCHITECTURE_NOTE ## Vibe once, run anywhere with Antigravity and Flutter DevFeed: [Vibe once, run anywhere with Antigravity and Flutter]() Original publisher: [Read original article]() Author: Craig Labenz Published: 2026-06-29T16:01:02Z Content type: article Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [AI-assisted coding](), [cross-platform](), [Artificial Intelligence](), [Dart](), [Large Language Model]() Tags: [agentic](), [agentic-engineering](), [ai](), [antigravity](), [code](), [coding-agents](), [cross-platform](), [dart](), [flutter](), [flutter-app-development](), [game-development](), [google-antigravity]() ### AI overview This article describes how a team used Google's Antigravity and Flutter to build and launch DashLander, a moonlander-style game set on procedurally generated asteroids. It explains how coding agents handled assets, code, testing, marketing, and deployment, and why Flutter's cross-platform model and Dart's strong typing can support agentic development. ### Source excerpt co-authored with Rody Davis Coding agents and the way we use them has already dramatically evolved since their arrival just a handful of months ago. Initially, the focus was heavily on observation -- reviewing every single output line by line. But as models rapidly increased in capability, the industry shifted toward true agentic engineering. Today, developers, PMs, and designers are wearing multiple hats, focusing on lofty conceptual goals and letting agents tackle the individual components. To explore this new frontier, our team wanted to build an experience that showcased this exact workflow. We wanted to build a game, generate its assets, write the marketing page, and deploy the whole thing, all using Google's premiere AI-native platform: Antigravity. Antigravity brings the best of Google into one place, using a tight feedback loop of planning, acting, and verifying. It creates artifacts, writes code, runs tests, and even clicks buttons in your UI to ensure it actually completed the task correctly. The result of our agentic adventure is DashLander -- a moonlander-style game set on procedurally generated asteroids. Here is the story of how we built it. Why Flutter in the AI era? A lot of people might be thinking: If agents can write native code, why not just have them write Android, iOS, and web apps entirely separately? That is a valid question; and is what originally inspired the concept of Flutter as a "Vibe once, run anywhere" UI toolkit. Having a single source of truth is just as important for AI as it is for humans. By having an agent write a single, cross-platform app, teams can eliminate the subtle bugs that inevitably creep in between different languages or platform-specific paradigms. Furthermore, Dart's strong typing gives LLMs excellent feedback. Languages with looser typing systems require dramatically more analysis by the LLM to know whether a given piece of code is correct across all scenarios. Flutter and Dart, on the other hand, use their analysis ## Harness MCP Server Now Connects Google Antigravity IDE DevFeed: [Harness MCP Server Now Connects Google Antigravity IDE]() Original publisher: [Read original article]() Author: Rohan Gupta Chinmay Gaikwad Published: 2026-06-22T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [MCP Server](), [Model Context Protocol (MCP)](), [ide](), [Artificial Intelligence](), [Google]() Tags: [agents](), [ai](), [ai-agents](), [antigravity](), [google-antigravity](), [harness](), [ide](), [integration](), [mcp](), [mcp-server](), [personal-access-token]() ### AI overview Harness announces that its MCP Server connects directly to the Google Antigravity IDE, allowing AI agents to inspect pipelines, debug deployments, trigger approved runs, and use real-time delivery context. The article also describes setup with a Harness Personal Access Token and Antigravity's Customizations panel. ### Source excerpt Connect Harness MCP Server inside Google Antigravity to let AI agents inspect pipelines, debug deployments, trigger approved runs, and act on real-time delivery | Blog ## 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 ## 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. ## Start building with Gemini 3 DevFeed: [Start building with Gemini 3]() Original publisher: [Read original article]() Author: Logan Kilpatrick Published: 2025-11-18T17:49:13Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [agentic-coding](), [Development](), [Command-line interface](), [ide](), [Benchmark]() Tags: [agentic-coding](), [agentic-development](), [antigravity](), [benchmark](), [cli](), [gemini](), [google](), [google-antigravity](), [jetbrains](), [linux](), [macos](), [none](), [preview](), [windows]() ### AI overview Google introduces Gemini 3, highlighting Gemini 3 Pro's reasoning, coding, agentic workflow, and benchmark performance. The model is available in preview through the Gemini API, Google AI Studio, and Vertex AI, with integration across developer tools. ### Source excerpt Gemini 3 is introducing advanced agentic coding capabilities, plus Google Antigravity, a new agentic development platform.