---
title: "Google"
description: "Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms"
---

# Google
> Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms

## Articles

### [Google for Developers Blog - News about Web, Mobile, AI and Cloud](https://yomu.fyi/post/google-for-developers-blog-news-about-web-mobile-ai-and-cloud.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Published: Sep 2, 2026
- Type: Explainer

The page presents six featured Google developer articles spanning multi-agent systems, content credentials, TPU optimization, agent plugins, Go, and Genkit Go. One article identifies bidirectional MCP, async event buses, unified validation, and tiered routing as recurring patterns in successful AI Agents Challenge submissions. Another introduces Credentio, an open-source C++ library for local-first C2PA validation, while a HeyGen case study reports a 1.86x speedup for Avatar IV on Trillium TPUs using torchax, XLA, FSDP, Ulysses sequence parallelism, and custom kernel/compiler optimizations. The collection also covers Agent Plugins 1.0.0’s plugin.json directory specification and Genkit Go’s progressive-disclosure Agent Skills based on SKILL.md bundles. A final article argues that Go’s compiler, toolchain, readability, and backward compatibility provide guardrails for reviewing and maintaining AI-generated software.


### [Decoding cosmic signals with deep learning and Keras](https://yomu.fyi/post/decoding-cosmic-signals-with-deep-learning-and-keras.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Yufeng Guo, Jonas Glombitza, PhD
- Published: Aug 27, 2026
- Type: Explainer

Astroparticle physics must reconstruct cosmic particles from indirect measurements because high-energy particles create extensive air showers before reaching distributed detectors. Modern observatories such as Pierre Auger, IceCube, and CTAO record spatially distributed sensor data and nanosecond-resolution waveforms, producing image-like spatiotemporal structures at enormous scale. The post explains how deep learning can improve instrument sensitivity, reveal hidden patterns, search for anomalies, and support event reconstruction across cosmic-ray, neutrino, and gamma-ray observations. It also frames fast prototyping of physics models in Keras as a practical entry point, while noting that more data and efficient scaling can sometimes outperform physics-inspired architectures. The resulting tension is between predictive performance from machine-learned black boxes and the physical insight, causal understanding, and interpretability scientists seek.


### [Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU](https://yomu.fyi/post/enterprise-grade-precision-for-long-context-multimodal-embedding-infer.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Anthony Su, Injae Kwak
- Published: Aug 26, 2026
- Type: Benchmark

Embedding models convert text, images, and audio into dense vectors used for semantic search, recommendation, intent classification, content discovery, and clustering. The post describes production support for long-context Qwen3 Embedding models on Cloud TPUs through native vLLM TPU integration and GKE Custom Compute Classes, allowing prioritized autoscaling across TPU and GPU capacity pools. Its implementation pads vocabulary tensors for hardware-safe sharding, hardens lazy loading, pre-warms sharding-aware JAX/XLA compilation caches, and uses a hybrid StepPool with CachedRequestState to preserve pooling state during chunked prefill and preemption. Parity evaluations target cosine similarity of at least 0.999 for text and 0.995 for multimodal inputs; on TPU Ironwood, Qwen3-Embedding-8B reached 83,996 total tokens per second and 5.13 requests per second at 16K+ sequence length with bf16 and TP=4.


### [How to Evaluate Live & Voice Agents in ADK](https://yomu.fyi/post/how-to-evaluate-live-voice-agents-in-adk.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Stephen Allen
- Published: Aug 24, 2026
- Type: Tutorial

ADK now supports native evaluation of live, voice-based agents, addressing regressions in tool use, context continuity, turn-taking, timing, and recovery that may not appear in demos. The walkthrough builds a graph-based workflow of three single-purpose agents running gemini-live-2.5-flash-native-audio, with a mocked date-of-birth tool between stages and session state carried across the open audio stream. It shows how to author scenario-based or fixed conversations, synthesize simulated user turns with Gemini TTS, configure live mode and rubric-based multi-turn trajectory judges, and run evaluations from the CLI or AgentEvaluator. ADK Web then reconstructs the audio as transcripts with playable clips, while configurable voices, languages, invocation limits, thresholds, and per-turn metrics support repeatable debugging and CI/CD regression checks.


### [Build zero-trust AI agents with Google's Agent Development Kit](https://yomu.fyi/post/build-zero-trust-ai-agents-with-google-s-agent-development-kit.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shubham Saboo, Eric Dong
- Published: Aug 17, 2026
- Type: Explainer

Connecting an autonomous Agent Development Kit (ADK) agent to databases, internal APIs, and code execution turns model decisions into production-state changes that system prompts cannot securely constrain. The post presents an open-source Customer Support & Returns Agent built with ADK and Gemini, using an example where prompt injection could produce an unauthorized refund or leak environment secrets. Its zero-trust design combines hardware-backed signing for database mutations, gVisor isolation with no network egress and resource limits for generated Python, and deterministic semantic gateways for prompts, tool calls, and database updates. Local equivalents use HMAC keys, a database ingress verifier, a constrained Docker runner, and Python validation tests, while production mapping uses Cloud KMS, HSM, and VPC Service Controls. The conclusion is that infrastructure-enforced boundaries let the model reason dynamically within strict limits.


### [HeyGen x Google Cloud: Bringing Avatar IV to TPUs](https://yomu.fyi/post/heygen-x-google-cloud-bringing-avatar-iv-to-tpus.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Published: Aug 13, 2026
- Type: Benchmark

HeyGen and Google Cloud ported Avatar IV, an 18B-plus-parameter diffusion stack for streamed talking-head video, to an eight-chip Trillium (v6e) host. The pipeline uses two diffusion transformers and a VAE decoder, with FSDP weight sharding and Ulysses sequence parallelism across the mesh, and runs through torchax, JAX, XLA, and shape-specific Pallas kernels. Optimization targeted exposed all-to-all collectives, frame-misaligned sparse-attention blocks, serial softmax work, layout copies, scheduler choices, and custom-kernel cost estimates; changes were measured per video chunk against output-quality gates. The resulting pipeline was 1.86× faster than the first working TPU version, comparable in performance to the 8×H100 production setup and up to 25% more cost efficient per generated-video minute, while shipped changes preserved byte-identical output or passed defined similarity review.


### [Introducing Credentio: Open Source C++ Library for C2PA Content Credentials from Google](https://yomu.fyi/post/introducing-credentio-open-source-c-library-for-c2pa-content-credentia.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Sherif Hanna
- Published: Aug 13, 2026
- Type: Announcement

Google introduces Credentio, an open-source C++ library for local validation of C2PA Content Credentials, initially supporting specification versions 2.2 and 2.4. The library is the code used by nearly 40 conformant C2PA-enabled Google products to process tens of billions of generated images, videos, audio files, and documents across many formats. Its API keeps validation inside the developer’s application, avoiding media transmission while providing immediate verdicts, privacy, and zero bandwidth overhead; it is also designed to maintain a small memory footprint for assets ranging from images to multi-gigabyte videos. Credentio parses manifests, assertions, digital signatures, and claims, accepts official or custom trust lists, and reports verification statuses and integrity errors, while planned work includes generating and embedding Content Credentials.


### [Mastering Edge AI on Raspberry Pi with LiteRT and Gemma](https://yomu.fyi/post/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Lu Wang, Terry Heo, Naushir Patuck, José María Casanova
- Published: Aug 11, 2026
- Type: Tutorial

Running edge AI on a Raspberry Pi 5 is presented as a way to build autonomous, low-latency systems that operate offline, with Reachy Mini serving as the demonstration. The setup combines LiteRT, LiteRT-LM, Gemma models, and LiteRT’s WebGPU (Vulkan) backend via ML Drift, splitting workloads between the CPU and VideoCore VII GPU. On CPU, Gemma 4 E2B reportedly reaches 99 tokens/sec prefill, 9 tokens/sec decode, and 1,432 MB peak memory; its tokenizer supports about 27.3 characters/sec in the voice demo. The pipeline runs YOLO object detection on the GPU, Moonshine speech recognition, Gemma reasoning, and text-to-speech on the CPU, while LiteRT CLI covers conversion, quantization, benchmarking, inference, and model execution from the LiteRT Hugging Face Community.


### [Why Go is an Ideal Language for AI-Assisted Software Engineering](https://yomu.fyi/post/why-go-is-an-ideal-language-for-ai-assisted-software-engineering.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Cameron Balahan, Richard Seroter
- Published: Aug 11, 2026
- Type: Explainer

AI-assisted software development shifts the human bottleneck from writing code to reviewing, verifying, and maintaining generated code, while people still define architecture, service boundaries, safety, and reliability. The post presents Go as a language designed for software engineering and argues that its end-to-end platform—built-in formatting, testing, dependency management, security tooling, and a comprehensive standard library—gives teams and AI agents a consistent foundation. Go’s readability, explicitness, standardized syntax through gofmt, static typing, and single-binary portability are described as guardrails that make generated code easier to inspect, validate, and operate. It also identifies gopls, the rebuilt go fix with modernizers, profiling, execution tracing, and profile-guided optimization as tools for maintaining and tuning systems over time. The conclusion is that Go’s consistency and production-oriented tooling can help humans absorb high-volume AI output without sacrificing reliability, maintainability, or system integrity.


### [Agent Plugins package your skills, tools, and more](https://yomu.fyi/post/agent-plugins-package-your-skills-tools-and-more.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Kevin Hou, Haoyu Wang, Alan Blount
- Published: Aug 6, 2026
- Type: Announcement

Agent Plugins 1.0.0 is an open, vendor-neutral specification for packaging Agent Skills and MCP servers into portable plugins, addressing incompatible wrappers that force authors to maintain client-specific copies. It defines a plugin as a directory with plugin.json, skills/ subdirectories, mcp.json, and client-owned reverse-domain extension namespaces. The manifest fixes component locations, requires explicit MCP server types for stdio, Streamable HTTP, or legacy HTTP+SSE, and lets skills continue loading when an MCP entry fails. Version 1 deliberately excludes installation, distribution, permissions, sandboxing, trust verification, and user experience, leaving those concerns to clients and related ecosystem layers such as discovery and catalogs. Google is joining the Core Maintainers and says Agents CLI and Data Agent Kit support the format, while more product integrations are expected.


### [Scaling AI Agent Infrastructure with the MCP Stateless updates](https://yomu.fyi/post/scaling-ai-agent-infrastructure-with-the-mcp-stateless-updates.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Kurtis Van Gent, Alan Blount
- Published: Aug 5, 2026
- Type: Explainer

The 2026-07-28 Model Context Protocol specification release candidate addresses production bottlenecks caused by the earlier session-oriented HTTP model, including session pinning, sticky routing, lost state, and Redis-backed infrastructure. It removes the initialize/initialized handshake and Mcp-Session-Id, carrying protocol version, client information, and capabilities in \_meta on each request while exposing method and name through HTTP headers. The update also introduces ttlMs/cacheScope caching, Multi Round-Trip Requests for elicitations and task progress, issuer and resource-indicator verification, and full JSON Schema 2020-12 support. The post says the stateless core enables ordinary load balancing, serverless deployment, and transparent failover, while SDK beta releases for TypeScript, Python, Go, and C# support migration.


### [Model routing with Google Cloud API Gateway](https://yomu.fyi/post/model-routing-with-google-cloud-api-gateway.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Mak Ahmad, Sanjay Pujare
- Published: Aug 4, 2026
- Type: Tutorial

Google Cloud API Gateway adds model routing in Public Preview for AI applications that need to direct traffic among models without hardcoded endpoints or open-source proxies. The serverless ingress layer accepts OpenAI-compatible requests and uses routing rules in an OpenAPI 3.x specification, where x-google-api-management defines backends and routers map virtual model names to targets such as Gemini, Claude, or OpenAI OSS-GPT. After deployment, clients call a stable endpoint; the Gateway transcodes the request to the backend’s native schema, adds an Agent Platform authentication token, and forwards it, separating client authentication from provider credentials. The guide also notes that every backend in one router must share a host, and shows standalone routing alongside possible Agent Gateway integration for security governance.


### [Scaling real-time AI agents with session-aware load balancing](https://yomu.fyi/post/scaling-real-time-ai-agents-with-session-aware-load-balancing.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Simerus Mahesh
- Published: Aug 3, 2026
- Type: Explainer

Real-time AI agents require infrastructure that manages continuous, bidirectional conversations rather than isolated requests, including interruptions that halt speech generation and update context without dropping the connection. The post argues that QPS and instantaneous CPU utilization are insufficient because they miss long-lived committed work and silent sessions that can become active simultaneously. It proposes application-level active-session tracking around the stream lifecycle, with cleanup in finally blocks and consistent snapshots, then combines session pressure with CPU and memory utilization in a hybrid routing model. The model normalizes session counts into a rate and uses target utilization, smoothed utilization, cost per session, and a safety scaler to estimate effective capacity and regulate new assignments. Validation should use long-lived, varied workloads and metrics such as session distribution, overloaded assignments, startup latency, dropped sessions, and counter behavior; high-concurrency trackers may require sharded or LongAdder-style aggregation.


### [Enable on-demand expertise with Agent Skills in Genkit Go](https://yomu.fyi/post/enable-on-demand-expertise-with-agent-skills-in-genkit-go.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Daniela Petruzalek
- Published: Jul 31, 2026
- Type: Tutorial

Agent Skills in Genkit Go address the prompt-context problem caused by loading every procedure, guide, and document into an agent’s persistent context. The tutorial defines skills as discoverable knowledge packs organized around a SKILL.md file, whose frontmatter is exposed first and whose instructions, references, assets, and scripts are loaded through progressive disclosure when needed. It integrates the Skills middleware with Genkit’s wrapper architecture, scans SkillPaths for metadata, matches descriptions, and invokes the use\_skill tool before injecting the selected skill into active context; Go examples use ai.WithUse during Generate or GenerateText. Recipe and image-restoration examples show skills activating for matching requests, while the post notes that the restoration skill may require several attempts because model behavior is non-deterministic.


### [Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA](https://yomu.fyi/post/agent-and-model-evaluations-in-gemini-enterprise-agent-platform-are-no.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Alex Martin, Dima Melnyk
- Published: Jul 31, 2026
- Type: Announcement

Agent and model evaluations in Agent Platform are now generally available, providing one engine and consistent metrics for measuring agents and models during development and against live production traffic. The service offers more than 20 pre-built metrics across quality, safety, grounding, tool use, trajectories, and reference-based tasks, plus adaptive rubrics that generate case-specific criteria and a versioned, org-wide registry for custom code-based or LLM-as-a-judge metrics. Experiments can run locally or server-side, with Cloud Storage artifacts supporting auditability and reproducibility, while case generation, user simulation, and environment simulation expand coverage and model slow or failing backends. Online monitors grade sampled production traces, chart scores over time, and issue drift alerts through configured channels. Access is available through the SDK, REST API, agents-cli, console, and ADK; model-based metrics incur standard model-call charges, while code-based and computation metrics add no additional cost.


### [How to use Google microbenchmarks for evaluating TPU performance](https://yomu.fyi/post/how-to-use-google-microbenchmarks-for-evaluating-tpu-performance.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Junjie Qian, Chi Shuen Lee, Yu-Hsuan (Amy) Lin, Haixiong (Sean) Wang
- Published: Jul 30, 2026
- Type: Explainer

Evaluating TPU performance requires measurements beyond product specifications, because real-world behavior depends on architecture and workload constraints. The suite measures network collectives, compute, HBM bandwidth, host transfers, and Ragged-Paged Attention primitives, using metrics such as GB/s, latency, TFLOPs, MFU, and TTFT-related throughput. Its src and configs directories provide implementations and tunable parameters, while sample Kubernetes recipes connect measurements to TPU architecture and runtime behavior. Roofline analysis uses peak MXU throughput and HBM bandwidth to distinguish compute-, memory-, and network-bound workloads and guide kernel, sharding, or rematerialization choices. In a 110B MoE workload on a 4x4x4 TPU 7x configuration, microbenchmark-guided changes addressed ICI stalls and memory-bound attention, reducing training step time by 21.2%.


### [Run Ray on TPU, Part 2: Ray AI libraries](https://yomu.fyi/post/run-ray-on-tpu-part-2-ray-ai-libraries.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Ivan Nardini, Spencer Peterson
- Published: Jul 24, 2026
- Type: Tutorial

Running Ray on TPUs requires multi-host workloads to remain on one intact TPU slice whose hosts share a high-speed ICI link. This guide presents Ray's AI libraries on top of GKE and Ray Core: Ray Serve for serving, Ray Data's iter\_jax\_batches() for input pipelines and batch inference, and Ray Train's JaxTrainer for distributed JAX training. Serve uses accelerator\_config.topology to defer slice placement to the replica, while iter\_jax\_batches() returns device-sharded JAX arrays and handles ragged final batches through drop, pad, or raise choices. JaxTrainer takes a training function and slice topology, launches one worker per host, and provides checkpointing, fault-tolerant restarts, and multi-slice scale-out; official TPU images and dashboard metrics round out the workflow.


### [Scaling Agentic RL: High-Throughput Agentic Training with Tunix](https://yomu.fyi/post/scaling-agentic-rl-high-throughput-agentic-training-with-tunix.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Haoyu Gao, Lance Wang, Shadi Noghabi, Tianshu Bao, Weiren Yu
- Published: Jul 21, 2026
- Type: Explainer

Tunix is presented as a post-training library for scaling agentic reinforcement learning, where multi-turn tool use and environment latency can leave TPUs idle. Its design combines an asyncio-based, high-concurrency trajectory collector with asynchronous vLLM-TPU and SGLang-Jax integration, then streams variable-length trajectories through a producer-consumer queue rather than waiting for a complete synchronous batch. Composable agent and environment abstractions expose APIs for custom tools, benchmarks, and external systems while preserving multi-turn token handling and lifecycle management. Lightweight RL-specific metrics correlate rollout, training, weight synchronization, model-call, and environment stages with TPU timelines, complementing targeted XProf traces. The stated outcome is a more observable pipeline intended to reduce stalls, keep accelerators utilized, and support agentic RL natively in the JAX/TPU ecosystem.


### [Run Ray on TPU, Part 1: The foundations](https://yomu.fyi/post/run-ray-on-tpu-part-1-the-foundations.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Ivan Nardini, Spencer Peterson
- Published: Jul 20, 2026
- Type: Explainer

Ray 2.55 makes Google Cloud TPUs a first-class accelerator in Ray, with official APIs, pre-built images, and core-library support rather than the earlier experimental path. The central constraint is TPU slicing: a multi-host workload must land on one intact slice because its hosts communicate through the slice’s ICI (Inter-Chip Interconnect). On GKE, the Ray Operator installs KubeRay and a TPU webhook; the webhook labels hosts, while Ray Core uses those labels to reserve a whole slice atomically through the public slice\_placement\_group() API, with topology and accelerator version as inputs. The guide shows node selection by TPU generation and topology, a multi-host numOfHosts field, and a RayCluster manifest that brings up one worker per host. Ray AI libraries such as Data, Train, and Serve generally invoke this placement primitive themselves, while direct use targets custom distributed workloads; the API is public but alpha.


### [Evolving Spec-Driven Development: Conductor Now Supports Antigravity](https://yomu.fyi/post/evolving-spec-driven-development-conductor-now-supports-antigravity.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Mahima Shanware, Sherzat Aitbayev, Jay Kornder
- Published: Jul 16, 2026
- Type: Announcement

Conductor, introduced as a Gemini CLI extension for spec-driven development (SDD), is evolving into a plugin that supports Antigravity CLI and other tools. The change moves project awareness from ephemeral chat logs into persistent, version-controlled Markdown artifacts while replacing strict command sequences with conversational creation and iteration of context, specs, and plans. Plugins package skills, rules, MCP servers, and hooks, and the Conductor Plugin can update project context and mark plan tasks complete as discussions progress, while preserving existing commands, specs, plans, shared configuration, and development tracks. The post reports a higher success rate on the most complex subset of TerminalBench tasks than a user not using SDD, and gives an Antigravity installation command: agy plugins install https://github.com/gemini-cli-extensions/conductor.


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