# Google Developer Experts - Medium Experts on various Google products talking tech. - Medium 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 ## 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 " ## \[July 2026\] 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 ## Build Your First AI Agent in Python -- A Hands-On Guide to the Claude Agent SDK DevFeed: [Build Your First AI Agent in Python -- A Hands-On Guide to the Claude Agent SDK]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-08-21T09:14:00Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [SDKs](), [Claude](), [Python](), [AI Agent](), [anthropic](), [Claude Code](), [API keys](), [Authentication]() Tags: [agentic-ai](), [ai](), [ai-agent](), [anthropic](), [anthropic-claude](), [api](), [authentication](), [claude](), [claude-code](), [guide](), [python](), [sdk](), [tutorial]() ### AI overview This step-by-step tutorial explains how to build an AI agent in Python with Anthropic's Claude Agent SDK. It covers project setup, SDK installation, API-key configuration, and the SDK's agent loop, tools, MCP servers, and subagents. ### Source excerpt A step-by-step, no-hype tutorial using Anthropic's official Agent SDK By Geeta Kakrani -- AI Consultant | Google Developer Expert (AI) If you've been writing simple "call the API, get a response" scripts with an LLM, you already know the limitation: every call is a one-shot Q&A. You ask, it answers, the conversation is over. There's no planning, no tool use, no "keep working until the task is actually done." The Claude Agent SDK -- Anthropic's official, open-source Python and TypeScript library -- solves exactly this. It gives you the same agent loop, tool execution engine, and context management that powers Claude Code, but as a library you can call from your own Python program. No need to build your own tool-calling loop from scratch. In this tutorial, we'll install it, set it up, and build a working agent -- step by step, using only what's documented and verified. What you'll need Python 3.10 or later An Anthropic API key (from the Claude Console) 15-20 minutes Step 1: Set up your project Create a fresh folder for this project. The SDK, by default, has access to files in this folder and its subfolders -- so keep it clean and dedicated. bash mkdir my-agent && cd my-agent python3 -m venv .venv source .venv/bin/activate # on Windows: .venv\Scripts\activateStep 2: Install the SDK bash pip install claude-agent-sdk That's it -- no separate CLI install needed. The package bundles the Claude Code CLI binary internally and uses it automatically. Note: If pip throws an externally-managed-environment error (common on newer Ubuntu/Debian/Homebrew Python), make sure you're inside the virtual environment you just activated in Step 1.Step 3: Set your API key Create a .env file in your project folder: ANTHROPIC_API_KEY=your-api-key-here (If you're on AWS, Google Cloud, or Azure, the SDK also supports Bedrock, Vertex AI, and Azure Foundry authentication -- but for this tutorial, a plain API key is simplest.) The architecture, before you write any code It helps to see the whole picture b ## Building a Local, Multimodal AI Terminal Agent with Gemma 4 DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4]() Original publisher: [Read original article]() Author: Arjun Prabhulal Published: 2026-08-12T09:25:11Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [gemma4](), [Artificial Intelligence](), [multimodal-ai](), [Command-line interface](), [Large Language Model](), [Local AI](), [Code](), [Google]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [code](), [function-calling](), [gemma](), [gemma-4](), [google](), [google-cloud-platform](), [local-ai](), [multimodal](), [ollama](), [open-source](), [terminal]() ### AI overview A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture. ### Source excerpt Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La ## Building AI Agents in Dart with the Genkit Dart SDK DevFeed: [Building AI Agents in Dart with the Genkit Dart SDK]() Original publisher: [Read original article]() Author: Abhishek Doshi Published: 2026-08-10T23:16:05Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Dart](), [Genkit](), [AI Development](), [Flutter](), [Cloud Functions](), [Firebase](), [SDKs]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-development](), [cloud-functions](), [dart](), [firebase](), [flutter](), [generative-ai-tools](), [genkit](), [sdk]() ### AI overview This tutorial introduces the Genkit Dart SDK for building AI agents natively in Dart and deploying them to Cloud Functions for Firebase. It explains agent orchestration, conversational state, history, streams, models, tools, flows, and multi-agent delegation. ### Source excerpt Because your Flutter app deserves a brain, and you deserve to never look at a Python traceback again. Let's be honest. We all love Dart. It's clean, it's safe, and it powers our beloved Flutter. But until recently, if you wanted to build serious, multi-step AI agents, the industry basically told you to pack your bags, leave your cozy typed ecosystem, and go write Python. Gross 🤮 Thankfully, Google finally heard our collective sighs and dropped the Genkit Dart SDK. Now, you can build full-stack, hyper-intelligent AI agents natively in Dart and deploy them straight to Cloud Functions for Firebase. No context-switching. No spinning up a random Node.js microservice just to talk to an LLM. Grab your coffee (or your energy drink of choice). Let's look at how Genkit turns your Dart code into an absolute powerhouse. https://medium.com/media/91c2efa4198c043f3f400f216214650e/hrefThe Shift: From "Dumb Chatbots" to "Autonomous Overlords" Building traditional chatbots or strict multi-step UI flows often feels like playing one of those terrible, rigid video games that block you at a level until you do exactly what the developers scripted. Agentic AI, by contrast, is the ultimate open-ended sandbox. An agent doesn't just blindly answer a prompt. It analyzes what the user wants, figures out which tools it needs to achieve the goal, and dynamically orchestrates the steps to get there. With Genkit's new Agents API, all the messy plumbing: maintaining conversational state, keeping track of history, and parsing streams, is handled behind a single API. You just focus on giving it a brain. The Holy Trinity: Models, Tools, and Flows To stop your AI from just hallucinating fan-fiction, Genkit uses a few core primitives. Think of this as the toddler-proofing stage of AI development. Models (ai.generate()): The actual brain. Whether you are using Gemini, Claude, or OpenAI, the API stays exactly the same. Tools (defineTool): This is how you give your AI hands. By defining strict input schemas ## Batch-Evaluating LLM Agent Trajectories for Responsible AI Checks on Cloud TPU v5e DevFeed: [Batch-Evaluating LLM Agent Trajectories for Responsible AI Checks on Cloud TPU v5e]() Original publisher: [Read original article]() Author: Noble Ackerson Published: 2026-08-04T23:28:06Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Monitoring](), [vllm](), [Cloud](), [hugging face](), [Large Language Model](), [Claude](), [gemma](), [Security](), [Benchmark](), [Artificial Intelligence](), [incident]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [benchmark](), [claude](), [cybersecurity](), [evaluation](), [gemma](), [google-cloud-platform](), [hugging-face](), [incident](), [infrastructure](), [monitoring](), [responsible-ai](), [security](), [vllm]() ### AI overview This article describes batch-evaluating LLM agent trajectories for responsible-AI checks before incidents occur. It connects reported production-system breaches with Hugging Face's use of LLM-driven analysis over more than 17,000 attacker events, then presents a scheduled approach using Gemma through vLLM on Cloud TPU v5e. ### Source excerpt Batch-evaluating agent trajectories on Cloud TPU v5e (compliance-at-scale, part 2) Trajectory batch eval pipeline for rai-checklist-cli A week or so ago, Hugging Face disclosed that an autonomous agent had broken into its production infrastructure. Five days later, OpenAI confirmed the agent was theirs: a combination of its own models, running an internal cyber-capability eval with the production safety classifiers switched off. The models were being tested on a benchmark called ExploitGym. The fastest observable path to a solution ran through the answer key. They escaped the isolated environment through a package-registry proxy, chained stolen credentials with zero-day vulnerabilities, and pulled the test solutions out of Hugging Face's production database. Per Axios, the agent kept pursuing its assigned objective even after it had escaped the test environment. Nine days later, Anthropic said hold my beer, checked its own logs and found three more. It reviewed 141,006 runs and found three cases where Claude models had reached the open internet and breached real production systems, the earliest dating to April. Two of the three organizations learned about it when Anthropic notified them. One lab looked and found something. A second lab looked and found something. That is the whole story here, and it should be the uncomfortable part: none of this surfaced through production monitoring. It surfaced because somebody went back and read the trajectories. Nobody has published what score that run produced. It doesn't matter. The part of this story that matters for this series is what Hugging Face did next with their findings. To reconstruct the intrusion, Hugging Face's security team ran LLM-driven analysis agents over the full attacker action log: more than 17,000 recorded events. Reporting indicates they did that analysis with an open-weight model on their own infrastructure, partly so no hosted safety classifier sat between the responders and the attack data, and partly ## 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 ## AI Engineer vs. Platform Engineer: Differences in AI Roles DevFeed: [AI Engineer vs. Platform Engineer: Differences in AI Roles]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-07-22T00:21:05Z Content type: comparison Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [AI Platform](), [Prompt Engineering](), [MLOps]() Tags: [agentic-ai](), [ai](), [ai-engineer](), [artificial-intelligence](), [career](), [generative-ai-tools](), [llm](), [mlops](), [platform](), [platform-engineering]() ### AI overview The article compares AI Engineer and Platform Engineer roles amid overlapping job titles. It describes AI Engineers as working on model choice, prompts, and agent behavior, while Platform Engineers focus on the infrastructure and operations needed to run AI systems. ### Source excerpt By Geeta Kakrani (GDE in AI & TPU) Open any job board right now and search "AI." Within a few listings, you'll notice something odd: the roles don't line up. Two postings with almost identical requirements have completely different titles. One company's "AI Engineer" is another company's "AI Platform Engineer" is a third company's "MLOps Engineer." This isn't a small naming quirk. For anyone trying to plan a career -- or even just understand where they fit -- it's a real, growing source of confusion. Not because AI itself is hard to understand, but because nobody clearly explains who is responsible for what anymore. The job title problem nobody talks about You'll find titles like: AI Engineer AI Platform Engineer AI Infrastructure Engineer MLOps Engineer LLM Platform Engineer Applied AI Engineer These titles overlap heavily. They pay in similar ranges. They list nearly identical skills. And most of them didn't exist as separate roles even three years ago. This isn't because companies are confused. It's because the industry is still figuring out what to call a very real, very new job -- one that sits between two older disciplines that used to be completely separate. If you're a student or early-career professional trying to plan your path, this title chaos makes it genuinely hard to know what to learn, what to apply for, and what a company actually expects from you on day one. So what's the actual difference? Strip away the job titles, and there are really two different jobs hiding underneath the AI buzzword. The AI Engineer works on the intelligence itself. Choosing which model to use. Designing prompts and instructions. Deciding how an agent should behave, what it should refuse to do, when it should use a tool versus answer directly. This is the "thinking" layer. The Platform Engineer works on everything that lets that intelligence actually run in the real world. Servers, deployment, scaling, security, monitoring, cost control. This is the layer most people never see