#Google

Advancing science & engineering with Antigravity’s /teamwork

Google describes how multi-agent workflows in Antigravity's /teamwork, built on Gemini 4 models, produced results on open problems including a full proof of the 2013 Courtade–Kumar conjecture formalized in Lean 4, a machine-checked formalization of the Weil conjectures spanning about 1.8 million lines of Lean, and 95% top-1 accuracy on molecular structure determination from NMR data. The post also reports engineering gains such as a memory-safe Rust rewrite of the Zircon microkernel adding about 800,000 lines, a libgav1 Rust port that became 20-30% faster than the original C++, and fleet-wide memory savings surpassing initial estimates of 1 PiB. Google says the tools are available to external researchers and developers in Antigravity today, with 10,000 academic researchers offered six months of free access to its highest usage tier.

Catch Go bugs that fuzzing misses with Gemini and a constraint solver

The author describes a workflow that compiles Go functions to RISC-V assembly, has Gemini 4 Argon translate the surviving runtime.panicBounds traps into Z3 constraints, and replays any satisfying input through go test to produce a real reproducer. The pipeline caught three planted bugs and three latent bugs in skill-lint, and the post explains why go test -fuzz missed two crashes because fuzz inputs usually have spare slice capacity, fixed by clipping capacity to length with a full slice expression. It also notes that an unsat result only means "maybe correct," since the LLM-written solver model is not a sound proof system.

How Agent Anomaly Detection scores on benchmarks we didn't write

Google tested its Private Preview Agent Anomaly Detection feature on five public, non-Google agent-security benchmarks (AgentDojo, InjecAgent, Agent Security Bench, AgentDyn and AgentHarm), running 1,774 sessions through an ADK agent using gemini-2.5-flash. It flagged 87% of harmful instructions the agent actually carried out, with false alarms on 2% to 16% of benign sessions, but caught only 4 of 18 sessions where poisoned long-term memory drove the agent to act. The post details per-benchmark and per-app results, known gaps such as resisted instructions and small actions, and recommended defenses including Model Armor, Semantic Governance Policy and human approval for high-impact actions.

Google Search gets a major upgrade with Gemini 4 Hydrogen

Google announced that Gemini 4 Hydrogen, described as the biggest model upgrade to Search yet, is now available globally in Google Search for all users. The company says it improves understanding of intent for nuanced multi-part questions and typos, strengthens query fan-out and linking to surface more helpful sites, and delivers more visual responses such as images, comparison tables and custom tools, with richer language understanding across supported AI Mode languages.

Introducing Gemini 4 Hydrogen

Google announced Gemini 4 Hydrogen, a fast, efficient model for everyday coding and agentic workflows, available across the Gemini API, AI Studio, Gemini for business, the Gemini app, Google Search, and paid Google AI Pro and Ultra subscribers in Google Antigravity. It is priced at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window, and surpasses Argon on the Artificial Analysis Intelligence Index at its default high thinking level (51 vs. 50, reaching 54 at xhigh). Google also introduced Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success rates on Terminal-Bench 4.0 while consuming 24% less quota and completing 19% faster.

Modal becomes a gVisor maintainer

Google is donating gVisor, including its name and trademarks, to the CNCF, and the project is moving to a maintainer-led governance model; Modal is one of three companies, alongside Ant Group and Tines, committing to join gVisor's maintainers, with OpenAI, Tencent and NVIDIA continuing their contributions. Modal says it has run gVisor as its major container runtime since the beginning, launching upwards of 40 million gVisor containers a day for GPU inference, multi-node training, AI coding agents and sandboxes, and has contributed upstream since 2023. The post details upstreamed work including nvproxy GPU support, an rdmaproxy device proxy for RDMA, snapshot/restore, and filesystem and memory performance improvements, and outlines future plans such as multi-GPU memory snapshots and GPUDirect Storage.

Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching

Google researchers describe an investigational framework that builds a "coachable health age" from wearable signals such as resting heart rate, steps, sleep duration and Active Zone Minutes, plus a Sensor World Model trained on over 2.2 million person-weeks of Fitbit data to simulate 12-week "what-if" habit trajectories. In blinded evaluations, lifestyle coaches preferred counterfactual-grounded plans in 71.4% of cases, and physicians rated 99.2% of those plans within safe exercise intensity limits. A companion study extends the approach to multi-organ CKM health by combining wearables with routine blood biomarkers including cholesterol-to-HDL ratio, eGFR and HOMA-IR.

Gemini Decisions API: Fast, calibrated multimodal decisions

Google introduced the Gemini Decisions API, powered by the new Gemini Decision 1 model (gemini-decision-1) and available through the Gemini API in Google AI Studio, for closed-set decisions such as tool selection, policy checks, model routing, and context grading. It accepts one multimodal input (text, images, audio, video, PDFs up to 256K tokens) with up to 1,000 yes/no, multiple-choice, and rubric-scoring questions, scoring options in a single forward pass with confidence scores at 150-250 ms end-to-end latency and $0.10 per 1M input tokens with zero output tokens billed. Google reports that Gemini Decision 1 leads across agent routing, reasoning, ranking, and multimodal benchmarks with the lowest calibration error, and is nearly 5x cheaper than Gemini 3.5 Flash-Lite with the Interactions API across the shared benchmark suite.

A2UI 1.0: Build delightful, fast, reliable, agentic experiences

Google released A2UI 1.0, the first stable version of its open protocol for generative UI, which lets AI agents respond with interactive screens that each app renders with its own native components. Version 1.0 adds bidirectional function calls, accessibility support and extensions, plus open-source renderers for React, Angular, Lit, Flutter, SwiftUI and Jetpack Compose (currently v0.9.1) and agent SDKs for Python, TypeScript and Dart. Atlassian, Alation, Bloomberg and ByteDance are already building with it, and the web and Flutter renderers work inside MCP Apps such as Claude and Gemini.

Try Tabletop: Customizable, collaborative spaces for groups to plan, play and do more together.

Google is introducing Tabletop, a new Google Labs experiment for real-time group collaboration where users add links, files and ideas, then prompt @tabletop or pick a starter template to generate interactive trip itineraries, dinner polls, expense splitters and tournament brackets. Once a space is ready, an invite link can be shared in a group chat so everyone can use it at the same time, like in a Google Doc. Tabletop is rolling out in the U.S. (18+) via waitlist at labs.google/tabletop.

AlphaGenome 2: Deciphering how cells read DNA to better understand disease

Google DeepMind announced AlphaGenome 2, a conditional DNA sequence model trained on over 107 million individual cell profiles across 68 tissues that predicts how genetic variants affect gene expression in specific human cell types and states. The model identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data on the TenK10K immune cell dataset, and can generalize to unseen single-cell experiments without retraining. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

Experience the art and legacy of Dr. Esther Mahlangu

Google Arts & Culture has launched the first online retrospective of South African artist Dr. Esther Mahlangu, timed to her 91st birthday, featuring more than 45 curated stories and an archive of her art, life and legacy. Curator Nontobeko Ntombela describes using AI, including Veo, to visually recreate Mahlangu's undocumented memories from age 10 until her first documented works in 1980, in collaboration with Google DeepMind, the Google Arts & Culture Lab and Domestic Data Streamers. The exhibition was curated with partners including Art of Contemporary Africa, Iziko Museums of South Africa, the British Museum, the Jean Pigozzi Collection, the National Museum of Women in the Arts and Serpentine, and is available at goo.gle/esther-mahlangu.

We don’t understand whether GDM intentionally trains on CoT

Redwood Research researchers note that the Gemini 4 Safety report appears to say GDM uses auxiliary rewards that observe chain-of-thought content, citing Kaufmann et al. on training on CoT, while a GDM blog post says they never penalize what the model writes in its CoT and GDM staff said they weren't training on CoT but couldn't explain details. The authors argue that "orthogonal" CoT rewards are a weak guarantee and that avoiding training against monitors is insufficient to preserve CoT integrity, and they present an appendix experiment in which five LLMs rated transcripts with hack acknowledgments removed 20-50 points higher than the originals. They conclude they suspect a misunderstanding but want clarification and greater transparency.

Multimodal Knowledge Agent (MKA) benchmark: testing knowledge work capabilities with complex visual files

Artificial Analysis partnered with Google DeepMind to launch the Multimodal Knowledge Agent (MKA) benchmark, comprising 200 private real-world visual tasks across 10 industries in which agents work from dense visual files like architectural drawings, engineering schematics, and clinical scans to produce deliverables such as slide decks, spreadsheets, PDFs, Word documents, and marked-up images. Tasks are graded against expert rubrics averaging ~70 weighted criteria split into Regression and Climb checks, with the headline Climb WSR score, run in the open-source Stirrup agent harness with up to 250 turns and graded by GPT-6 Luna (medium) as an agentic judge. Claude Fable 5.2 (max) leads with 57% Climb WSR, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%, while Claude Fable 5.2 averages $31.97 per task versus $6.29 for Gemini 4 Carbon and $1.11 for GPT-6.1 Sol.

Gemini Intelligence: New features, on more devices

Google announced that Gemini Intelligence is expanding to more devices, including the motorola signature 27 (pre-order in the U.S. from November 19) and Pixel 10, Pixel 10 Pro XL and Pixel 10 Pro Fold via today's Pixel Drop. New and expanded features include Create My Widget for building custom widgets from a prompt, Device Assistance for controlling and fixing your device by asking, Rambler on additional devices, and task automation expanding to the UK, India and Japan. Gemini Intelligence is also coming to Android Auto for proactive suggestions and to Pixel Watch for Create My Widget in the coming weeks.

Pay for Hugging Face Hub with your Google Cloud Account

Hugging Face organizations can now link to a Google Cloud account through the Google Cloud Marketplace so that Hugging Face usage appears on the existing Google Cloud invoice instead of a credit card. The setup involves subscribing to the Hugging Face Hub listing, selecting the organization to link, and waiting for Hugging Face team approval, after which the Google Cloud account becomes the active payment method. The article notes that included Team or Enterprise credits still apply before pay-as-you-go usage is reported, and that linking and unlinking are recorded in audit logs.

On-Device Speech Recognition and Text-to-Speech in Google AI Edge Gallery

Google introduced on-device ASR and TTS in Google AI Edge Gallery, pairing lightweight speech models with on-device SLMs like Gemini Nano and Gemma 4 for fully offline voice interaction. ML Kit on Android offers multilingual transcription across 100+ languages at 10x real-time speed plus a few-step diffusion TTS that starts in ~100 ms, while MediaPipe Tasks and LiteRT bring cross-platform support for open-weight models including Parakeet-TDT 0.6B, PocketTTS 100M, Kokoro-82M, and Phonon 31M. The release powers the XR Language Teacher showcase and voice-enabled AI Chat, with open-source reference code on GitHub.

Build Safer n8n Agents and Workflows with Google Gemini 4 Carbon

Gemini 4 Carbon is now available in n8n, with Google reporting a 53.9% score on AutomationBench, 88.2% on SEC-bench Pro, and a 0.0% prompt injection success rate on Gray Swan's benchmark (down from 0.7% for Argon). It can be selected in the AI Agent node's Google Gemini Chat Model or the standalone Google Gemini node, using Gateway credits on n8n Cloud or your own API key when self-hosted. Pricing is $2 per million input tokens and $10 per million output tokens during the introductory period, rising to $4 and $20 after November 30, 2026.

Gemini 4 Carbon is now available

Google released Gemini 4 Carbon to developers, enterprises, scientists, and consumers, describing it as its strongest model for cybersecurity capabilities and long-horizon agentic workflows in knowledge work, software engineering, and scientific reasoning. It ranks #1 on CursorBench 4.0, Perplexity's WANDR, and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is available via the API in Google AI Studio and Gemini for business at $2 per million input tokens and $10 per million output tokens. Google also previewed /swarm in Google Antigravity and reported safety results including a 0% prompt injection attack success rate on the Gray Swan IPI benchmark.

NVIDIA Expands Open Source Commitment With OpenShell Donation and New AI Infrastructure

At KubeCon + CloudNativeCon, NVIDIA is contributing its OpenShell agent security runtime to the CNCF as a Sandbox project and contributing to Google-led Agent Substrate, also accepted into the CNCF Sandbox. NVIDIA also announced new DSX AI Factory Platform components: NVIDIA Kubernetes Engine, NVIDIA Kubernetes Recipes and NVIDIA Snapshot, which restores checkpointed replicas in seconds without model reload or warmup. Design partners Crusoe and Saturn Cloud reported reduced workload readiness and vLLM startup times using Snapshot.

Gemini Robotics 2.5 accelerates real-world general dexterity

Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model, which it says raises skill breadth and learning speed, with 30% to 100% higher success rates and 20% to 30% faster task completion than previous baselines across 200 tasks. In a proof-of-concept on Mercedes-Benz production lines in Hungary and Germany, Apptronik's Apollo 2 humanoid performed automotive kitting with a 96% success rate. The release also adds inference-time safety steering (33% better safe completion around obstacles, 43% around people) and the open-source ASIMOV-Jailbreak benchmark, on which Gemini 4 Argon reaches 99.5% jailbreak resistance.

We’re bringing Adobe’s creative and productivity tools closer to your everyday Google apps.

At Adobe MAX, Google announced an expansion of its partnership with Adobe that will bring Adobe's creative and productivity tools into Google products, and Google's frontier AI models into Adobe's apps. Planned integrations include a direct pathway from Google Drive and Gmail into Adobe Acrobat with an "Edit with" entry point in file previews, expanded Adobe connectors in Gemini and Gemini Enterprise, Adobe creative tools in AI Mode in Google Search, and Adobe's CX Enterprise Coworker agent in Gemini Enterprise. Adobe and Google DeepMind will also explore new editing capabilities for creative professionals, starting with an image model for Photoshop that could enable near-real-time edits.

Google Cloud launches Gemini for healthcare.

Google Cloud announced Gemini for healthcare, an AI solution designed to unify fragmented clinical, operational, and administrative systems by turning siloed data into coordinated action. The platform integrates specialized skills and data connectors with existing third-party systems, drawing on electronic health records, billing platforms, clinical guidelines, and Medicare datasets to orchestrate multi-step workflows such as medical records synthesis and prior authorization review within defined guardrails and with human oversight. It is initially available in preview with launch partners including HCA Healthcare, Highmark Health, and Northwell Health.

Introducing AlphaProof Code: a breakthrough in verifying complex software at depth

Google DeepMind researchers used AlphaProof Code, a multi-LLM agentic framework combining human expertise with AI agents, to formally verify key security properties of the actual code underlying the Signal protocol, generating over six million lines of Lean. The verification took four weeks, and when two previously fixed bugs were reintroduced into an isolated version of Signal's code, the system produced machine-checked proofs that the buggy versions were insecure. DeepMind is open-sourcing its formal verification artifacts.

Opening the datasets behind our AI weather models

Google's WeatherNext team released SatWeave, an hourly global mosaic of geostationary satellite imagery across 17 channels blending six satellites, and PARDIG, an AI-based global precipitation dataset at 15-minute and roughly 10-kilometer resolution trained on NASA GPM radar observations. Both datasets, key to the WeatherNext 3 model, are available as live feeds and historical archives on Google Cloud Storage, with PARDIG catalogs also in Google Earth Engine. PARDIG reportedly improves hourly precipitation detection skill over NASA's IMERG-Final by more than 30% on light-to-moderate precipitation.

Exploring the world's languages with Language Explorer

Google introduced Language Explorer, an interactive web platform developed as part of its 1,000 Languages Initiative, which lets researchers, educators, and language enthusiasts discover and visualize linguistic diversity around the world using data across thousands of languages built on the open-source LinguaMeta dataset. The tool is featured as an interactive installation in the new exhibition "Real Talk! Language. Power. World." at the Museum of Communication Berlin, running through February 2028, with a companion exhibition at the Museum of Communication Frankfurt through September 2029. Google also noted that Google Translate now supports nearly 250 languages and Gboard supports 900+ language varieties.

Announcing Long-Context Training on TPUs with MaxText Context Parallelism

Google Cloud announced long-context training support in MaxText, its open-source JAX training framework, via four context-parallel strategies on Cloud TPU: All-gather, A2A (DeepSpeed Ulysses), P2P (Ring Attention), and the A2A + P2P hybrid (USP), all using the Tokamax Splash Attention kernel. Benchmarks report Llama 3.1 8B reaching 70% MFU on TPU 5p for sequences up to 4 million tokens, with context extendable to 14 million tokens on TPU 5p and TPU 7, where 14M-token P2P training on 64 TPU 7 chips reached 48.7% MFU versus 37.1% on TPU 5p. The feature is available in MaxText v0.2.4 on PyPI, Docker images and GitHub main, with sample recipes for TPU 7 and TPU 5p.

From prompt to app: Build with Google Maps in AI Studio

Google Maps Platform is now available in Google AI Studio, letting users describe an app idea and have AI Studio handle configuration and API key management with no credit card required. The post lists core APIs for Maps, Places, Routes and Environment, plus example builds like a City Immersive Tour, Pet Grooming Route Optimizer and Store Locator. Built-in sandbox quotas of 100 calls per day per API reset every 24 hours, and linking a Google Cloud billing account unlocks production-grade quotas.

What is Pathways? Single-controller training for large models

Google announces the General Availability of Pathways on Google Cloud Platform, describing the distributed system it built to train large models including Gemini. The post explains where SPMD execution falls short—pipeline bubbles, RL loops with distinct programs, and failures that stop every process—and how Pathways instead has a single Python client declare the whole computation as a graph of blocks and tensors, with a distributed runtime placing it on virtual devices, moving and resharding tensors, ordering work across the cluster, and remapping around a failed host. It also contrasts Pathways with Ray, framing Ray as orchestration and Pathways as the execution layer beneath it.

Agent Distraction

The post reports that 13% of agent file-reading sessions expose the agent to inline TODO(bug/...) or FIXME(bug/...) references, and in 6% of encounters the agent takes follow-up action on that bug ID, 83% of which are unprompted by the developer. Cross-referencing these detours against live bug status, it finds 41% chase already-closed or obsolete tickets while 57% involve still-open bugs where the agent expanded scope, and a fixed-effects regression shows unprompted bug detours add 70% more steps, 77% more tokens and 105% more wait time per session. The authors built agents that scan codebases and remove confirmed-obsolete bug references, reporting thousands removed over six months with over 95% acceptance from human reviewers.

VIALS-AA: different paths to scientific image reasoning

VIALS-AA analyzes how 19 models reason over scientific images, classifying 9,177 tool calls from its original test set: 38.3% were image viewing and 47.3% prepared or measured images. Image measurement made up just 5–6% of tool calls for GPT-6 Astra (max) and GPT-6.1 Sol (max), versus 25–27% for Claude Opus 5.5 (max) and Claude Sonnet 5.5 (max). The report also notes that Gemini 3.8 Flash (high) accounts for 86.7% of commentary/no-op calls, that more intelligent models tend to call fewer tools, and that cost per task varies by nearly 100× across models.

10 Best AI Game Makers for Beginners in 2026 (No Coding Needed)

This guide ranks ten AI game-making tools for beginners in 2026—including Astrocade, Manus, Aippy, Rosebud AI, PlayDrop, Kimi, Google Playground, GDevelop, Replit and Roblox Studio with Assistant—rating each on time to a playable game, no-code editing, free access, sharing and setup. It compares pricing, notes limits such as credit costs and region locks, and includes a table on code access, portability and selling rights.

Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips

Google DeepMind released Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, adding native 15-second video generation, multi-turn conversational editing, and stronger reference adherence. The model is priced 30% below Gemini Omni 1.1 at $0.07 per second for 720p video, with duration settable in one-second increments from 3 to 15 seconds. It also adds more natural speech and lip-sync, more controllable cinematic camera moves, and outputs carrying an imperceptible SynthID watermark.

Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips

Google DeepMind released Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, adding native 15-second video generation, multi-turn conversational editing, and stronger reference adherence. The model is priced 30% below Gemini Omni 1.1 at $0.07 per second for 720p video, with duration settable in one-second increments from 3 to 15 seconds. It also adds more natural speech and lip-sync, more controllable cinematic camera moves, and outputs carrying an imperceptible SynthID watermark.

Featured coverage of this story: 《Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips》

Detecting signs of hypertension using wearable sensors

Google researchers describe AI-PPG-ACC-HTN, a multimodal AI system that uses smartwatch PPG and accelerometry data, without cuff calibration, to detect a persistent hypertensive state by aggregating 15-second waveform embeddings over multiple days alongside height. Validated against 24-hour ambulatory blood pressure monitoring in a 196-participant, five-site US study, it reached 65.8% sensitivity and 90.0% specificity for initial screening and 78.4% sensitivity, 90.9% specificity and 96.7% PPV for confirmatory testing among 48 participants with elevated office BP. The authors frame it as early feasibility research for passive opportunistic screening, not a replacement for clinical cuffs or a standalone diagnosis.

Agile Deliberation: How AI helps humans define the gray areas

Google Research introduces Agile Deliberation, a framework that treats building subjective AI classifiers as an iterative conversation, splitting the task into concept scoping (breaking a broad idea into sub-concepts and exclusions) and concept iteration (using vision-language models to surface borderline images that challenge the user's definitions). In 18 live 1.5-hour user sessions across the subjective domains of "Healthy Food" and "Pay-to-Play" content moderation, the approach produced more consistent, higher-performing classifiers than zero-shot, automated collaborator, and manual deliberation baselines while reducing user fatigue and anxiety. The code and algorithms are open-sourced on GitHub, with a Colab/Jupyter demo available.

Ai2 and Google collaborate on a new generation of climate models

Ai2 is launching a climate modeling initiative with multimillion-dollar, three-year funding from Google.org and technical support from Google Research to develop NeuralGCM+, an open-source model extending Google Research's NeuralGCM to simulate atmosphere and ocean together for faster, more reliable decade-scale climate projections. Ai2 will focus on climate trends over years to decades, such as how rainfall and water availability could change through mid-century in regions like the southwestern United States, while Google Research continues developing NeuralGCM for nearer-term forecasts over weeks to months. The work will proceed along three fronts: connecting the atmosphere and ocean, improving the response to a warming world, and making the tools easier to use and extend, including improved performance on widely used GPUs.

Cost per task: Using cheap classifiers to route across Gemini models and thinking levels

The article argues that cost per successful task, not price per token, governs production economics for coding agents, and reports a 50-question auto-graded benchmark routing tasks with TypeSafe's Jev classifier across Gemini configurations. A frozen regex and prompt-length heuristic scored 93.8% on 16 development questions but fell 23.2 points to 70.6% on 34 held-out tasks, while Jev agreed with hand labels on all 34 and 48 of 50 overall; capping maxOutputTokens at 300 saved nothing on easy tasks and broke 7 of 25 hard ones. A router sending tasks to Flash-Lite first and escalating Jev-flagged complex ones to Flash on low thinking solved all 50, as did Flash on low thinking alone, and the article notes caching favors keeping long context-heavy turns on one model.

Agentic cheminformatics with Google Antigravity

Google describes Cheminformatics Skills for Google Antigravity that pair a language model for workflow orchestration and scientific reasoning with deterministic RDKit-based tools and ChEMBL/PubChem connectors. In a lung cancer walkthrough, an agent retrieved thousands of compounds, ran matched molecular pair analysis, built potency models, and proposed rigidified macrocycles with synthesis routes in about an hour. The authors note that sparse data and assay noise limit predictive precision, so computational designs remain testable hypotheses requiring experimental validation.

Lakebase Postgres is now generally available on Google Cloud

Databricks announced that Lakebase Postgres is generally available on Google Cloud, offering fully-managed, serverless Postgres on the Databricks Data + AI Platform with compute separated from storage and a shared storage layer with analytical data. Features include serverless autoscaling and scale to zero, instant branching, point-in-time recovery, standard Postgres with pgvector-compatible search, and unified governance via Unity Catalog. GA regions are asia-southeast1, europe-west3, us-central1, us-east4, and us-west1.