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Thumbnail for Google Antigravity: Building a Real-Time AI Race Coach5:56

Google Antigravity: Building a Real-Time AI Race Coach

Google Developer Experts collaborated at Sonoma Raceway to build an on-device AI race coach capable of delivering low-latency real-time guidance. Because cloud connectivity was unavailable on the racetrack, the team deployed an open-weight Gemma model running directly on an onboard device Tensor Processing Unit. Vehicle telemetry including GPS, accelerometer, pedal position, and steering angle was routed through a CAN to USB converter into the AI model. The engineering team used Google Antigravity to rapidly build, debug, and iterate on the application directly trackside between 20-minute driving sessions. After initial issues with excessive chatter and mismatched track positioning were resolved, the AI coach successfully provided timely throttle prompts that increased the driver lap speed.

Sep 10, 2026Read summary
Thumbnail for Graph Engineering with ADK6:10

Graph Engineering with ADK

Relying on a single large prompt to fetch data and reason in one model call causes hallucinations because ungrounded models lack deterministic API and course data. Graph engineering organizes complex systems by separating predictable operations into deterministic Python functions while reserving large language model calls for reasoning tasks. In Google's Agent Development Kit 2.0 (ADK 2.0), functions and agent nodes operate as peers within the same orchestration graph and wiring structure. Key structural patterns in graph engineering include fan-out for parallel execution, join nodes for aggregating parallel branch outputs without custom aggregator agents, and routing for selecting execution paths. Deterministic routers using code logic are faster, cheaper, and more reliable than LLM routers whenever branching decisions are based on structured data and closed sets of options. Developers should use static graph workflows when a process structure is known beforehand, dynamic workflows when graph shape depends on runtime inputs, and autonomous agents when workflows cannot be predefined.

Sep 10, 2026Read summary
Thumbnail for Graph Engineering 1015:56

Graph Engineering 101

Graph engineering is an architecture pattern for complex artificial intelligence systems that structures processes into networks of agent nodes and deterministic function nodes. The core components of agent architectures include the harness for tools, memory, and guardrails, the loop for iterative reasoning cycles, and the graph for multi-step workflow routing. Google Agent Development Kit enables shared state across different nodes and agents throughout an orchestrated graph workflow. A pull request review pipeline illustrates graph engineering through parallel fan-out tasks, a join node that synthesizes findings, and a router that directs code to fixer agents or human approval. Graph engineering focuses on operational behavior and execution control flow, whereas knowledge graphs and GraphRAG focus on data representation. While agent swarms rely on dynamic agent personas to tackle ambiguous tasks, graph engineering offers predictability, debuggability, and control for well-defined workflows.

Sep 3, 2026Read summary
Thumbnail for How to monitor & optimize Google Gemini agents with Grafana Cloud7:22

How to monitor & optimize Google Gemini agents with Grafana Cloud

Manual prompt-by-prompt debugging fails when AI agents move to production environments serving hundreds or thousands of concurrent users. The Grafana Sigil SDK provides OpenTelemetry-native instrumentation that captures Gemini agent tool calls, execution decisions, and response latency without altering underlying business logic. Grafana Cloud AI Observability aggregates metrics including error rates, token consumption breakdown, cost estimations, and latency percentiles across model versions. Built-in AI evaluators and automated Slack assistants can diagnose operational bottlenecks such as rate-limit exhaustion and excessive token usage. Integration with Claude Code inside Slack allows engineering teams to close the remediation loop by generating and applying fixes to agent prompts and source code.

Sep 3, 2026Read summary
Thumbnail for End-to-end agentic development with Google Gemini, Antigravity, and GitLab8:13

End-to-end agentic development with Google Gemini, Antigravity, and GitLab

GitLab integrates directly with Google Cloud IAM and Google Artifact Registry to manage permissions and container images. Developers can use Gemini inside the Antigravity IDE to create visual UI mockups and translate design images into working application code. Model Context Protocol (MCP) server integration allows the Antigravity IDE to create and update GitLab issues and track planning workflows smoothly. GitLab Duo Agent Platform enables automated code review using specialized agents such as Develop with Gemini before merging code. GitLab CI/CD pipelines run automated security tests, upload build artifacts to Google Artifact Registry, and deploy directly to Cloud Run.

Sep 2, 2026Read summary
Thumbnail for He built a voice controlled drone to avoid using a remote!2:03

He built a voice controlled drone to avoid using a remote!

Bryen Param won the Live Agents category of the Gemini Live Agent Challenge with his drone-copilot project. The project replaces physical drone remote controllers with real-time voice commands. The backend infrastructure for drone-copilot is hosted on Google Cloud Run. The system uses the Gemini Live API for conversational flight control and Gemini Flash for visual inspection tasks. Details of the winning project and other competition entries are hosted on Devpost.

Sep 2, 2026Read summary
Thumbnail for Automate Google Cloud with Cloud CLI Remote MCP Server2:12

Automate Google Cloud with Cloud CLI Remote MCP Server

The Google Cloud CLI Remote MCP Server allows AI agents to interact with Google Cloud environments without installing local CLI binaries. Enabling the plugin equips an agent with tools to execute both gcloud and BigQuery (bq) commands through natural language prompts. During an incident triage demonstration, the agent queried Cloud Logging to locate a missing data file and inspected BigQuery tables to verify pipeline impact. Remediation actions that modify data require user confirmation before executing commands such as gcloud storage cp. All agent operations run under the user's IAM credentials within a secure sandbox to preserve enterprise governance.

Aug 28, 2026Read summary
Thumbnail for Launch your portfolio website with AI8:30

Launch your portfolio website with AI

Google AI Studio Build Mode allows developers to rapidly prototype full-stack and frontend web applications using natural language prompts. The build interface automatically generates code, provides selectable visual design themes, and displays a live interactive preview sandbox. Generative AI outputs are non-deterministic, meaning identical prompts can yield different code, layouts, and simulated background details. Developers can iterate on generated applications by asking the assistant in natural language to add, modify, or remove specific UI sections and features. Web applications created in Google AI Studio can be deployed directly to Google Cloud Run with a hosted URL ending in ai.studio or mapped to a custom domain. Completing the builder lab awards the Completed Builder Journey learning badge on Google Developer profiles.

Aug 28, 2026Read summary
Thumbnail for Launch your to-do web app with AI7:57

Launch your to-do web app with AI

A full-stack application consists of a front end user interface, a back end server connection, and a database for persistent storage. Google AI Studio Build Mode generates full-stack web applications from natural language prompts using Gemini models. Firebase Firestore integration allows applications generated in Google AI Studio to store data with real-time synchronization. Firebase Authentication provides built-in Google Sign-In support to restrict data access to specific users. Applications can be deployed to Cloud Run with a single click and given custom vanity URLs under the ai.studio domain.

Aug 28, 2026Read summary
Thumbnail for Launch your web-based video game with AI11:39

Launch your web-based video game with AI

Event sourcing records every game turn as a distinct sequential event rather than overwriting existing database entries. Google AI Studio Build Mode generates the full-stack web application, Firestore integration, and Minimax AI opponents from natural language prompts. Firestore security permission errors can be diagnosed and resolved automatically using the Fix feature in Google AI Studio. Firebase Firestore real-time data synchronization allows live multiplayer matches across separate devices without manual socket configuration. Logging event history enables time-travel features such as scrubbing through past turns and rolling back moves.

Aug 28, 2026Read summary
Thumbnail for 7 AI agent patterns to improve your coding workflow11:29

7 AI agent patterns to improve your coding workflow

Modular agent skills replace long, confusing prompts by allowing coding agents to read short skill descriptions and dynamically load full instructions only when needed. Skill files are plain text files stored either per project in the project root directory or globally in Gemini configuration directories. The seven core skill design patterns categorize capabilities into domain knowledge, tool wrappers, inversion, generators, reviewers, pipelines, and meta skills. Inversion patterns force the AI agent to stop guessing and ask clarifying questions or follow structured root cause analysis before writing code. Pipeline skills orchestrate sequential development workflows such as test-driven development through strict multi-step execution guards. Developers can create custom skills as text files following guided resources like Google codelabs and evaluate them using published whitepapers.

Aug 27, 2026Read summary
Thumbnail for What "real-time voice AI" actually means0:52

What "real-time voice AI" actually means

Conventional voice AI typically stitches together three separate systems comprising speech-to-text, a central model, and text-to-speech. Stitched voice pipelines force a turn-taking conversation dynamic where the system cannot hear the user while speaking. A real-time voice agent processes audio input and output simultaneously within a single live session. True real-time voice architecture supports barge-in, allowing a user to interrupt the agent mid-sentence and have it stop speaking immediately. Google Cloud demonstrates building genuine real-time conversational agents using Gemini Live.

Aug 27, 2026Read summary
Thumbnail for Build a real-time voice AI agent with Gemini Live API7:13

Build a real-time voice AI agent with Gemini Live API

Traditional text-to-speech AI systems operate in one direction, whereas Gemini Live provides bidirectional, simultaneous audio-to-audio streaming. The application architecture consists of three components: a browser frontend, a lightweight backend relay, and the Gemini Live API connected via WebSockets. The core interaction loop follows four simple steps: open the session, send microphone audio, receive model audio, and play the response in the browser. Voice Activity Detection (VAD) is built directly into Gemini Live to detect when speech starts and stops without requiring custom detection code. Barge-in allows users to interrupt the agent mid-sentence, which feels instantaneous when local audio playback is stopped immediately upon detecting mic input. External tools and functions allow the voice agent to execute real actions like controlling music playback, provided the functions return responses quickly.

Aug 26, 2026Read summary
Thumbnail for From notebook to production: Serving JAX at scale3:05

From notebook to production: Serving JAX at scale

Moving a trained JAX model from a notebook into production requires choosing a serving pattern that avoids unexpected just-in-time compilation latency during live user requests. Using jax.jit provides the simplest in-process Python serving path, but models should be warmed up with dummy inputs to absorb initial compilation costs before handling live traffic. Ahead-of-time compilation explicitly lowers Python code to StableHLO intermediate representation and compiles it to a device executable to eliminate first-call latency spikes. Portable deployments can be achieved using jax.export for serialized StableHLO artifacts or jax2tf to export standard TensorFlow SavedModel formats for existing TensorFlow serving infrastructure. Debugging and optimizing JAX on GPUs relies on a four-layer mental model consisting of JAX Python tracing, XLA compilation, CUDA or cuDNN execution, and GPU hardware.

Aug 26, 2026Read summary
Thumbnail for Scale JAX models to multi-GPU systems3:28

Scale JAX models to multi-GPU systems

Scaling JAX workloads across multiple GPUs focuses on array placement and sharding declarations rather than rewriting training routines with low-level communication code. Data parallelism in JAX operates by replicating model parameters across devices, splitting data batches into shards, and performing automatic gradient averaging across GPUs. Four core concepts structure multi-GPU placement in JAX: Mesh, PartitionSpec, NamedSharding, and jax.device_put. JAX supports both declarative automatic sharding and explicit low-level control through shard_map combined with jax.lax.pmean. A Tiny Transformer language model built with Flax NNX demonstrates multi-GPU training, checkpointing with Orbax, and length-padded JIT generation.

Aug 26, 2026Read summary
Thumbnail for Build and optimize JAX training loops3:25

Build and optimize JAX training loops

A clean JAX training step is designed as a pure function that ingests parameters, optimizer state, and a batch, returning updated states and metrics. Using fixed batch sizes enables JAX and XLA to reuse compiled execution steps on the GPU without triggering repeated recompilations. Converting GPU metrics to Python floats on every single step forces CPU-GPU synchronization and severely degrades training loop throughput. Naive attention materializes the full quadratic attention matrix, whereas fused implementations like cuDNN scaled dot-product attention drastically reduce memory and latency. Decoder architectures rely on causal attention masking, where Multi-Head, Grouped-Query, and Multi-Query Attention offer trade-offs in KV cache footprint while keeping the same output shape.

Aug 26, 2026Read summary
Thumbnail for Getting started with JAX on NVIDIA GPUs4:00

Getting started with JAX on NVIDIA GPUs

Verifying GPU execution requires checking hardware visibility with nvidia-smi as well as inspecting jax.devices and jax.default_backend in code. JAX distinguishes between host CPU memory and device GPU memory, meaning operations like converting to NumPy arrays or casting tensors to Python floats can cause synchronization bottlenecks in hot loops. The core functional transformations in JAX are jit for compiling, grad for automatic differentiation, and vmap for vectorization and batching. JAX compiles functions via XLA based on input shape and data type signatures, causing re-compilation whenever array shapes change dynamically unless padded with masks. Python conditional branching fails on traced values inside jit-compiled functions, requiring JAX-compatible control flow primitives like jnp.where, lax.cond, and lax.scan. Accurate benchmarking on GPUs requires calling block_until_ready on array outputs because JAX dispatches GPU kernels asynchronously.

Aug 26, 2026Read summary
Thumbnail for Build state-of-the-art Real Time Voice AI with Cartesia and Google Cloud0:34

Build state-of-the-art Real Time Voice AI with Cartesia and Google Cloud

Cartesia on Google Cloud enables the creation of ultra-realistic voice agents. The Sonic model offers superior latency and naturalness in voice interactions. Developers can build customized, enterprise-ready workflows with the code-first Agent platform.

Jul 23, 2026Read summary
Thumbnail for Real-Time Data for AI and ML: Chalk + Google Cloud0:30

Real-Time Data for AI and ML: Chalk + Google Cloud

Chalk on Google Cloud provides a modern data platform tailored for AI and ML. It unifies pipelines and allows real-time feature computation. The platform ensures data consistency between training and production environments.

Jul 21, 2026Read summary
Thumbnail for AI control: Temperature, Top K, and Top P (2-minute AI with Google)2:28

AI control: Temperature, Top K, and Top P (2-minute AI with Google)

AI responses can be tuned using three main parameters: temperature, top K, and top P. Temperature controls creativity, with low settings providing safe outputs and high settings enabling imaginative responses. Top K limits the selection pool to the most probable words, while top P offers a more diverse word choice based on cumulative probability.

Jun 30, 2026Read summary
Thumbnail for 10x productivity with the Gemini CLI

10x productivity with the Gemini CLI

Gemini CLI is an open-source productivity tool for terminal users. It aims to enhance coding efficiency, promoting a 10x productivity boost. The project is one of Google's fastest-growing repositories with over 102k stars.

Jun 25, 2026Read summary
Thumbnail for How to Make SOPs & Narrated Guides in Under 10 Minutes0:33

How to Make SOPs & Narrated Guides in Under 10 Minutes

Create SOPs and narrated guides quickly with Guidde. Capture screenflow and compose content efficiently. Edit content easily and ensure data privacy.

Jun 25, 2026Read summary
Thumbnail for Gemini API for Speech and Text2:11

Gemini API for Speech and Text

Gemini APIs enable natural voice interaction in apps. Text-to-speech and audio transcription features are available. Real-time sentiment analysis and multi-language support deliver capable functionality. Simple integration into projects with SDK code generation.

Jun 22, 2026Read summary
Thumbnail for How Tinybird Uses Gemini to Write and Optimize SQL1:47

How Tinybird Uses Gemini to Write and Optimize SQL

Tinybird uses Gemini to enhance SQL code generation. Gemini collaborates with developers to optimize the coding process. The platform automates documentation and performance optimization.

Jun 18, 2026Read summary
Thumbnail for How Tinybird Built a Full Data Dev Agent with Gemini1:22

How Tinybird Built a Full Data Dev Agent with Gemini

Tinybird enables faster data product development. Gemini powers natural language data exploration. Users can create custom data agents with Gemini. Advanced context handling enhances large data analysis.

Jun 18, 2026Read summary
Thumbnail for Build & Deploy an AI-powered web app with Google AI Studio2:14

Build & Deploy an AI-powered web app with Google AI Studio

Build an AI-powered web app quickly with Google AI Studio. Use plain English to create apps that generate marketing content and images. Deploy your app smoothly to Google Cloud Run in minutes.

Jun 18, 2026Read summary
Thumbnail for The Google for Startups Gemini Kit | Paige Bailey1:16

The Google for Startups Gemini Kit | Paige Bailey

The Google for Startups Gemini Kit simplifies AI access for founders. Startups can receive up to $350,000 in cloud credits for scalable development. Tools include Firebase for app building and Google Cloud Skills Boost for team training.

Jun 18, 2026Read summary
Thumbnail for Image Generation and Editing with Google AI Studio & Imagen1:54

Image Generation and Editing with Google AI Studio & Imagen

Generate unique images quickly with Google AI Studio and Imagen. Edit images using natural language commands for specific requirements. Create social media content and product descriptions automatically.

Jun 18, 2026Read summary
Thumbnail for Get Your Gemini API Key in Google AI Studio EASY Tutorial1:33

Get Your Gemini API Key in Google AI Studio EASY Tutorial

Start by accessing Google AI Studio to get your API key. Generate and manage multiple API keys for your projects. Use the key in various programming languages to integrate Gemini features.

Jun 18, 2026Read summary
Thumbnail for How to turn general AI into an industry expert! (2-minute AI with Google)2:20

How to turn general AI into an industry expert! (2-minute AI with Google)

AI models need fine-tuning to specialize in specific industries. Pre-training provides a broad knowledge foundation. Fine-tuning tailors AI for specific tasks using high-quality data. AI's capabilities require specialized knowledge for complex problems. For deep expertise, think of AI training like education.

Jun 16, 2026Read summary
Thumbnail for Automate Agent Development Life Cycle Using Antigravity 2.0 and Gemini Enterprise Agent Platform

Automate Agent Development Life Cycle Using Antigravity 2.0 and Gemini Enterprise Agent Platform

Easily manage the agent development life cycle with Gemini Enterprise and Antigravity 2.0. Build an SRE triage agent that can parse logs and generate incident reports. Deploy the agent to Google Cloud with simple prompts and evaluate performance before deployment.

Jun 5, 2026Read summary
Thumbnail for How to Manage Break-Glass Access with Privileged Access Manager | Google Cloud Security Showcase

How to Manage Break-Glass Access with Privileged Access Manager | Google Cloud Security Showcase

Privileged Access Manager (PAM) crucial for managing elevated permissions during high-stakes incidents. Minimizes risks by adhering to the principle of least privilege in cloud environments. Allows pre-authorized users to self-elevate permissions in emergencies without admin intervention.

Jun 1, 2026Read summary
Thumbnail for How to Secure GKE Clusters with the CIS GKE Benchmark | Google Cloud Security Showcase

How to Secure GKE Clusters with the CIS GKE Benchmark | Google Cloud Security Showcase

Use the CIS GKE benchmark to secure Google Kubernetes Engine (GKE) clusters. Policy controller enforces compliance with security policies in GKE. Automate security configurations through CI/CD pipelines instead of manual methods.

Jun 1, 2026Read summary