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 Development
Antigravity
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.
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 with specialized agents for orchestration, execution, and approval workflows. It uses policy hooks and approval gates to classify tool invocations by risk level, balancing autonomous execution with human oversight.
Swarm Intelligence by Google Antigravity SDK by AI GDE Kshitiz Rimal (Nepal) introduces an advanced Gen AI orchestration paradigm that addresses traditional RAG limitations through parallel-executing AI agents. It demonstrates a Map-Reduce architecture using Python’s asyncio to synthesize complex, cross-document information into a cohesive report.
Moving Fast Without Losing Track: My antigravity-cli Changelog Habit by GDE Giorgio Boa (Italy) discusses the importance of maintaining a changelog when transitioning software projects from PoC to production. It explains how integrating custom changelog tools into AI-assisted workflows creates a reliable project memory that improves system manageability and trust.
Building AgyQueue: The Architecture Behind Asynchronous AI Agents (Part 2) by GDE Jitendra Gupta (India) highlights integration with ADK and MCP to enable multi-agent orchestration, persistent state, and scalable governance on Google Cloud.
The AI-Native Developer Experience, Part 2: Harness Engineering with Google Antigravity by GDE Jaroslav Pantsjoha (UK) explores how Antigravity IDE elevates the AI Harness framework to robust product primitives using a multi-agent trading platform as a case study.
OpenGravity: Turn Antigravity into your autonomous cloud agent, controlled from WhatsApp (repository) by GDE Stéphane GIRON (France) turns Antigravity into an AI agent by enabling interaction with WhatsApp.
Utilizing Google Antigravity as Agent Harness for Running a Sales and Purchasing Multiple Agent System (repository) by GDE Shang Yi Lim (Malaysia) explores the use of Antigravity 2.0 as a low-code harness to orchestrate specialized AI agents for automating business tasks like purchase order processing, PDF parsing, and price lookups.
[Codelab] Codelab for the The Agentic Architect Sprint: Asynchronous Background Tasks by GDE Ivanna Kaceviča (Latvia) demonstrates how an Antigravity 2.0 agent offloads slow background tasks and polls long test runs to maintain non-blocking productivity during web app development.ADK
https://medium.com/media/5f1d3bb156f17275a07bc4b680a94f38/href
[Hot 👏] FinSavant Part 1: Building an Agentic FinOps Platform with Google ADK, A2UI and Gemini Enterprise Agent Platform — Goals, Architecture, and Tech Stack (Part 2 | repository | video) by GDE Darren Lester (UK) introduces an agentic FinOps and explains how it leverages AI tools like ADK and GEAP to deliver actionable insights and real-time operational context for GCP cost management.https://medium.com/media/6a76aa4566bd852800f4a9777c5cb7e8/href
Building an AI Sales Assistant in Slack with Google ADK by AI GDE Carlos Alarcon (Colombia) shared how to build an AI-powered sales assistant using ADK to help automate repetitive workflows and coordinate tasks without leaving Slack. The session explored the architecture behind the agent, including tool integrations, memory, orchestration patterns, and secure enterprise deployment considerations.
Kaggle: Competing Through AI Agents
Starting and iterating on a Kaggle competition in Google Antigravity by AI GDE Andrey Lukyanenko (UK) details an experience using Antigravity 2.0 with Gemini 3.1 Pro in a Kaggle competition using a multi-agent pipeline with models like LightGBM, CatBoost, and PyTorch MLP. An highlights benefits like systematic iteration alongside challenges such as agent overconfidence, emphasizing the need for human oversight in AI-driven workflows.
Google Antigravity applied to Kaggle tabular competitions by AI GDE Luca Massaron (Italy) details an experiment using Antigravity to predict student health risk in a Kaggle competition with an imbalanced dataset. Luca highlights techniques like context curation, feature selection, prior-correction, and mega ensembles to demonstrate the potential of AI agents in competitive data science.
https://medium.com/media/4649ba842395c6bea0b476e40d444810/href
Kaggle’s Product Lead On Agents, Benchmarks & What’s Left For Humans w/ Megan Risdal Google DeepMind by AI GDE Taha Bouhsine (US) discusses how autonomous AI agents are reshaping Kaggle competitions by enabling competitors to win without writing code.
TPU, Frameworks and Research
JAX/TPU
TPU 101 with JAX: A Medium Series by AI GDE Roya Kandalan (US) provides a beginner-friendly guide to leveraging TPUs with JAX, covering setup, core concepts, performance benchmarks, and practical applications like multi-chip parallelism. It features code snippets run on actual TPU hardware to help developers transition to JAX for high-performance ML.
Train Your First LLM on a Free TPU by AI GDE Keshan Sodimana (Sri Lanka) provides a practical guide to training a small character-level GPT model on a free Colab TPU using JAX/Flax NNX. It covers essential steps from setup to sampling, offering insights into LLM architecture and scalability for beginners.
TPU v6e-1 vs A100 80GB Single-Chip Showdown: vLLM × Qwen3–8B Actual Measurement, $/token is TPU with Same Settings by AI GDE Sho Tanaka (Japan) compares the performance and cost-efficiency of TPU v6e-1 and NVIDIA’s A100 GPUs for serving Qwen3–8B using vLLM. Sho finds that TPU v6e-1 offers greater cost-efficiency due to lower hourly rates, while the A100 delivers higher raw performance; it does not justify the price difference.
From 1,540 to 19,511 Tokens per Second on a Single TPU v5e Chip: A Measurement Study of Large Language Model Inference Optimization by AI GDE Rubens Zimbres (Brazil) demonstrates that profiling-driven optimizations — specifically token selection corrections, batch scaling, and attention kernel tuning — can boost Gemma 2B inference throughput on a single TPU v5e by a factor of 12.7 without altering the model weights or hardware, drastically reducing energy consumption and generation costs.
TPU Cost Guardrails by TPU Builder Masaru Koido (Japan) is an open-source cost-control system for safely running workloads on Cloud TPUs. It introduces layered guardrails, including quota controls, maximum workload durations, and an automated kill switch that deletes TPU VMs when a predefined budget threshold is exceeded.
Optax: Optimizers You Can Compose Like LEGO by AI GDE Wesley Kambale (Uganda) explores Optax and key techniques such as gradient clipping, learning rate schedules, decoupled weight decay, and per-parameter optimization for modern deep learning architectures. In Catching bugs before they catch you, Wesley also addresses debugging challenges in JAX-based ML workflows by introducing Chex as a validation library. It details methods for enforcing shape and type contracts, detecting NaN values, performing runtime checks, and tracking recompilations to build reliable ML systems.
Automating the LLM Fine-Tuning Lifecycle on GCP TPU v5e via Multi-Agent Workflows by AI GDE Chansung Park (Korea) presents an automated multi-agent architecture to orchestrate the fine-tuning lifecycle of LLMs using JAX/Flax on GCP TPUs. Chansung addresses key challenges such as distributed state orchestration and hardware recovery to significantly reduce manual iteration time.
Jaxotron🚀 by AI GDE Rishiraj Acharya (India) introduces a scalable LLM trainer built using JAX and Equinox to prove that you can build a 3D parallelized training pipeline can be built in a faction of the code it takes in PyTorch, just by letting JAX’s compiler do the heavy lifting.
When JIT hides your errors: Debugging JAX by AI GDE Wesley Kambale (Uganda) addresses common challenges in debugging JAX code and presents native debugging utilities such as jax.debug.print, jax.debug.breakpoint, and jax.disable_jit. It covers automatic NaN detection alongside Flax NNX-specific tools for architectural inspection and capturing intermediate activations.
Gemma 4 12B Flax NNX Port by AI GDE Taha Bouhsine (US) provides a from-scratch Flax NNX implementation of Gemma 4 12B, porting text, vision, and audio components along with a weight converter. The model offers an open, verified module to support research and experimentation in the JAX/Flax ecosystem.
Tunix: Tune in JAX by AI GDE Aye Hninn Khine (Thailand) introduces Tunix and covers modern techniques for adapting open-source LLMs, including supervised fine-tuning and preference optimization.
Exploring the Power of Gemma 4 by AI GDE Nathaly Alarcon (Bolivia) explores how to leverage Gemma 4, and use Keras and JAX to expand agent creation toolkits. Nathaly also delivered a JAX and Keras Introduction. Gen AI, Open LLM for students.
ML Research
Google TabFM, Zero-Shot Tabular Predictions, Tested Honestly (Notebook) by GDE Sascha Heyer (Germany) provides an evaluation of Google’s TabFM for zero-shot tabular predictions, benchmarking its performance against XGBoost and TabICL on real datasets. details key installation insights, practical limitations, agent skill integration, and deployment on Google Cloud’s Vertex AI.
Community Highlights
AIML Indonesia hosted a community event bringing TPU & frameworks to local developers together with active AI GDEs. Five sessions were delivered and panel discussions proceeded along with the event.
- TPU Introduction by Esther Irawati Setiawan (AI GDE, Indonesia)
- Batch image captioning on TPU by Sayak Paul (AI GDE, India)
- Unlocking LLM Potential: Introduction to Tune in JAX, as Post-training (Tunix) by Joan Santoso (AI GDE, Indonesia)
- Introduction to Pallas: JAX’s extension for simple kernel building by Aritra Roy Gosthipaty (AI GDE, India)
- JAX, High-performance ML Research by Aakash Nain (AI GDE, India)
APIs and Models
Gemini
Build a Managed Tech Digest Agent with the Gemini API by GDE Saoussen Chaabnia (Tunisia) explains managed agents on the Gemini API and details how a single call provisions a Google-hosted Linux sandbox for autonomous web browsing, code execution, and file management. It walks through core API patterns from the official Google Codelab, including behavior customization, persistent sandboxes, and building a tech digest agent that generates PDF summaries.
Gemma
Gemma4 12b with Cline and Continue by GDE Isaac Johnson (US) shares hands-on experience running Gemma4 12b model on personal hardware, including its setup with Ollama and integration into development workflows. Issac evaluated its coding performance, compared multi-agent systems with deterministic code, and highlighted its potential as a reliable local development assistant.
Fine Tuning Gemma for Medical Data (slides | video) by AI GDE Bilguun Jargalsaikhan (Mongolia) delivered insights on fine-tuning Gemma models using Cloud TPUs for healthcare applications. He detailed technical challenges with clinical datasets and provided an architectural blueprint for domain-specific AI models.
Accelerating Domain-Specialized AI Research Through Open Source by AI GDE Chansung Park (Korea) discusses a talk given at the Hugging Face Seoul meetup regarding open-source AI research. He explained the limitations of service-oriented LLMs for specialized areas and demonstrated intelligent network operations using open-source tools and Gemma open-weight models.
Scaling Pediatric Accessibility: How Eye Aim Arena Leverages the Google WebAI Ecosystem- LiteRT.js + MediaPipe Iris by GDE Cyrus Wong (Hong Kong) introduces Eye Aim Arena, a client-side, real-time gaze-controlled framework designed to provide accessible, privacy-focused pediatric oculomotor therapy using standard web cameras and edge ML. It utilizes an adaptive local LLM (Gemma), browser-based processing, and clinical analytics to support offline, zero-latency rehabilitation.
Build with Gemma on Kaggle by AI Communities
Three AI Communities hosted the Gemma Hackathon throughout July. At Hackathon Build with Gemma: GDG Callao by GDG Callao, 248 attendees in 70 teams participated and implemented solutions in local execution on Edge/On-device, the integration of multimodal AI with vision processing, and the creation of autonomous agents based on native function calling. Build With Gemma @Bangladesh was a hybrid hackathon focusing on solving real world problems for students from over 42 universities. Build with Gemma: ML, AI, Deep Learning & NLP Community was hosted along with a mentorship session at a local university for 44 teams participating in.
[July 2026] AI Community — Activity Highlights and Achievements was originally published in Google Developer Experts on Medium, where people are continuing the conversation by highlighting and responding to this story.
