# Google Cloud Platform (GCP) Google Cloud is a cloud computing platform that provides services for designing, creating, and running applications. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Publishing a Kubernetes SIG's Images to registry.k8s.io DevFeed: [Publishing a Kubernetes SIG's Images to registry.k8s.io]() Original publisher: [Read original article]() Author: The Kubernetes Authors Published: 2026-09-16T18:00:00Z Content type: tutorial Language: en Sources: [Kubernetes Contributors Blog]() Topics: [Kubernetes](), [container images](), [GitHub Actions](), [CI/CD](), [Google Cloud Platform (GCP)]() Tags: [ci-cd](), [container-image](), [container-image-registry](), [container-images](), [documentation](), [github-actions](), [google-cloud](), [guide](), [kubernetes]() ### AI overview A guide to publishing official Kubernetes SIG container images through registry.k8s.io. It explains why GHCR is unsuitable for public distribution and describes the ordered workflow involving Prow, Google Cloud Build, staging registries, and image promotion. ### Source excerpt If you're publishing container images for a Kubernetes SIG project, you might expect the same publishing workflow used by other container registries to work. That was my assumption too. My workflow successfully published the images, but they weren't publicly available. Instead, official Kubernetes project images are distributed through registry.k8s.io , the Kubernetes project's official container image registry. No single step was hard, but the steps were spread across multiple repositories and had to happen in a particular order, something I mostly learned by tripping over them. This post is the guide I wish I had at the start. It walks through that workflow end to end using Cluster Inventory API from SIG Multicluster as an example. The same process applies to eligible Kubernetes subprojects that publish official container images. My first attempt: GHCR I first tried a common GitHub release pattern: using GitHub Actions to publish images to ghcr.io on a tag push (cluster-inventory-api#40 ). The workflow succeeded, but Kubernetes GitHub organizations keep GHCR packages private, so GHCR cannot be used for public distribution. As described in the registry.k8s.io documentation , official images take a different route: Prow (the Kubernetes project's CI/CD system) picks up a tag push and runs Google Cloud Build on Kubernetes-owned infrastructure to push the image to a staging registry, and the image promoter then copies it to registry.k8s.io. The first-time setup, step by step Besides the image-owning repository, this touches three infrastructure repositories: kubernetes/k8s.io , kubernetes/test-infra , and kubernetes/org . The pieces depend on each other like this: Before you start: decide your project details Before setting up the publishing workflow, decide a few project-specific details. These values will be reused throughout the setup when creating the staging registry, configuring image builds, and setting up image promotion: , which determines the staging ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## Set Up Cloud OIDC From the Pulumi CLI DevFeed: [Set Up Cloud OIDC From the Pulumi CLI]() Original publisher: [Read original article]() Author: Sean Yeh Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [OpenID connect (OIDC)](), [Command-line interface](), [AWS IAM](), [Cloud](), [AWS Identity and Access Management (IAM)](), [Azure](), [Google Cloud Platform (GCP)]() Tags: [automated](), [aws](), [azure](), [cli](), [cloud](), [configuration](), [docs](), [esc](), [google-cloud](), [how-to](), [iam](), [identity](), [oidc](), [openid-connect](), [product](), [pulumi](), [pulumi-cli](), [security](), [terminal](), [workflow]() ### AI overview This tutorial explains how to use the Pulumi CLI command pulumi env setup to configure Pulumi ESC as an OIDC provider for AWS, Azure, and Google Cloud. It covers interactive onboarding, non-interactive flags for scripts and agents, cloud credentials, account selection, access policies, and automatic creation of identity providers, IAM roles, policy attachments, and ESC environments. ### Source excerpt Pulumi ESC can act as an OpenID Connect (OIDC) provider for AWS, Azure, and Google Cloud, issuing short-lived, signed tokens that these clouds exchange for temporary credentials. This eliminates hard-coded credentials and improves your security posture. Last year, we introduced an onboarding flow in the Pulumi Cloud console that makes it super easy to configure OIDC for your cloud provider in a few guided steps. We're bringing Pulumi Cloud into the CLI so agents can use its capabilities directly from the terminal, without requiring a human to complete steps in the console. The new pulumi env setup command brings OIDC onboarding to that workflow, with interactive prompts for guided setup and non-interactive flags for scripts and agents. pulumi env setup - how it works Run the command with your desired cloud provider (aws, azure, gcp). For example: pulumi env setup aws The command then asks what it needs to configure your cloud, including your credentials, the accounts to configure, and the level of access. The questions differ per cloud. For AWS, it asks: How to authenticate to AWS. It uses the credentials you already have, or it signs you in with AWS SSO. Which accounts to configure. Which policy to attach to the OIDC role. Choose AdministratorAccess for Pulumi Deployments, ReadOnlyAccess for Pulumi Insights, or any other policy ARN. Then, it will print out the plan: About to configure OIDC for organization my-org: account 111111111111: create role pulumi-esc-oidc-622e86ea-319ba4c675bb3c00-role attach arn:aws:iam::aws:policy/AdministratorAccess create ESC environment my-org/aws-login/sandbox-account-env Proceed? [yes/no] After you confirm, the command creates the identity provider, the IAM role, and the policy attachment in each account. It then creates one ESC Environment per account, with the aws-login provider already configured. Non-interactive setup You can also run the command without interactive prompts by passing in the necessary flags. Each cloud has its ow ## Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace DevFeed: [Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace]() Original publisher: [Read original article]() Author: Jay Sivachelvan Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Google Cloud Platform (GCP)](), [SDKs](), [Google](), [reliability](), [Artificial Intelligence](), [Cloud Run](), [Resilience](), [API](), [Serverless]() Tags: [agent](), [agent-development-kit](), [agent-framework](), [announcements](), [api](), [billing](), [cloud-marketplace](), [cloud-run](), [execution](), [gemini](), [google](), [google-cloud](), [integration](), [launch](), [partnership](), [pricing]() ### AI overview Temporal announces expanded Google Cloud integrations, including Public Preview support for the Google Gen AI Python SDK, durable execution for Gemini-based workflows, and pre-release Serverless Workers for Google Cloud Run. Temporal Cloud is also available on Google Cloud Marketplace with pay-as-you-go pricing. ### Source excerpt Temporal expands its Google Cloud partnership with Gemini integration, durable execution, and pay-as-you-go pricing on Google Cloud Marketplace. ## The Pulse: Grok's CLI caught uploading all your local files to the cloud DevFeed: [The Pulse: Grok's CLI caught uploading all your local files to the cloud]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-19T14:21:33Z Content type: news Language: en Sources: [The Pragmatic Engineer]() Topics: [Command-line interface](), [Artificial Intelligence](), [Git](), [.env](), [Google Cloud Platform (GCP)]() Tags: [ai](), [cli](), [files](), [gcp](), [git](), [secret]() ### AI overview The article reports claims that the Grok Build coding CLI uploaded local workspace data to cloud storage, including repository contents, Git history, and .env file contents in documented tests. ### Source excerpt Devs discover that Grok CLI pushed all local files, .env files and git history have been pushed to a GCP bucket, unencrypted. SpaceX's initial reaction? Blame the devs ## The Pulse: Bending Spoons' Acquisition Strategy DevFeed: [The Pulse: Bending Spoons' Acquisition Strategy]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-05T11:45:13Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [migration](), [Architecture & Design](), [Microservices](), [Java](), [virtual machines](), [Cloud](), [Google Cloud Platform (GCP)](), [legacy application]() Tags: [architecture](), [backend](), [cloud](), [google-cloud](), [java](), [legacy](), [microservices](), [migration](), [performance](), [provisioning](), [virtual-machines]() ### AI overview The article examines Bending Spoons' acquisition strategy, including its announced $1.285 billion cash purchase of Airtable and its approach to operating struggling products with smaller teams. It also describes the company's reported modernization of Evernote from a Java 11 monolith running across 750 manually provisioned virtual machines on Google Cloud to a managed-database and microservices architecture in about six months, with reported improvements in performance, reliability, operating cost, and on-call load. ### Source excerpt In only 5 years, Hopin went from zero to a $7.7B valuation, and back to zero again. Also: Bending Spoons' startup acquisition model. ## Agentic kill switch is a database problem. So we built Redpanda SQL DevFeed: [Agentic kill switch is a database problem. So we built Redpanda SQL]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-08-03T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Databases](), [SQL](), [stream-processing](), [Google Cloud Platform (GCP)](), [Amazon Web Services](), [systems]() Tags: [agentic](), [announcement](), [aws](), [cloud](), [code](), [database](), [exploit](), [google-cloud](), [governance](), [models](), [olap](), [real-time](), [scale](), [sql](), [stream-processing](), [systems](), [tools]() ### AI overview The article presents Redpanda SQL as a database built for agentic governance. It describes real-time bridge queries, efficient SQL JOINs, a ring-buffer shuffle algorithm, and analytical processing designed to detect agent behavior and provide an agentic kill switch. Redpanda SQL is stated to be available on AWS and Google Cloud. ### Source excerpt Agentic governance needs a new kind of database, so we built Redpanda SQL. Now available on both AWS and Google Cloud. ## Coding Challenge #129 - Coding Challenges Coach DevFeed: [Coding Challenge #129 - Coding Challenges Coach]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-07-31T08:02:05Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [AI Agent](), [Artificial Intelligence](), [Google](), [Google Cloud Platform (GCP)](), [Agent Framework](), [Security](), [Command-line interface]() Tags: [adk](), [agents](), [ai-agents](), [cli](), [coding](), [gemini](), [google-cloud](), [how-to](), [security]() ### AI overview Coding Challenge #129 guides developers through building an AI coaching agent for Coding Challenges. The project uses Google's Agent Development Kit and Gemini Enterprise Agent Platform, progressing from a local prototype to a production service on Google Cloud with state management, cost tracking, observability, and security auditing. ### Source excerpt This challenge is to build your own AI coach for Coding Challenges, learning how to build and deploy production grade AI agents. ## Announcing Serverless Workers for Google Cloud Run DevFeed: [Announcing Serverless Workers for Google Cloud Run]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Cloud Run](), [autoscaling](), [Serverless](), [Google Cloud Platform (GCP)](), [Deployment](), [container](), [IAM]() Tags: [autoscaling](), [cloud-run](), [deployment](), [docker](), [google-cloud-run](), [iam](), [product-news](), [serverless]() ### AI overview Temporal announces Serverless Workers support for Google Cloud Run. The service integrates with Cloud Run Worker Pools and dynamically adjusts capacity, including scaling to zero when appropriate, reducing the infrastructure planning and autoscaling work required from users. ### Source excerpt Temporal now supports Serverless Workers on Google Cloud Run, autoscaling Worker Pools for you so you never manage infrastructure or pay for idle compute. ## Connect Your Cloud Accounts to Pulumi in Minutes DevFeed: [Connect Your Cloud Accounts to Pulumi in Minutes]() Original publisher: [Read original article]() Author: Levi Blackstone Published: 2026-07-15T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [Cloud](), [Amazon Web Services](), [Azure](), [OpenID connect (OIDC)](), [Google Cloud Platform (GCP)](), [configuration](), [Authentication]() Tags: [aws](), [azure](), [cloud](), [configuration](), [esc](), [google-cloud](), [governance](), [identity](), [insights](), [openid-connect](), [policy](), [product](), [pulumi-cloud]() ### AI overview Pulumi introduces a Connect cloud accounts wizard for discovering and onboarding AWS accounts, Azure subscriptions, and Google Cloud projects in a single guided flow. It automates OIDC setup, creates Pulumi ESC environments, and applies discovery scans and policy packs. ### Source excerpt Pulumi Insights gives you visibility and governance across your entire cloud footprint, but that visibility is only as complete as the set of accounts you've connected. Until now, connecting an account meant repeating a manual setup for each one: OIDC configuration, hand-written Pulumi ESC environments, and per-account scan and policy setup. For an organization with dozens or hundreds of AWS accounts, Azure subscriptions, or Google Cloud projects, that per-account friction was the biggest obstacle to getting a complete picture. Today, the new Connect cloud accounts wizard removes it: discover every account in your cloud organization, select the ones you want, and onboard them all in a single guided flow. From hours of setup to a single flow The time savings are real: connecting a batch of accounts across AWS, Azure, and Google Cloud takes about three minutes end to end. Setting up those same accounts with the manual per-account workflow would take hours to days. The wizard handles the entire onboarding lifecycle for AWS, Microsoft Azure, and Google Cloud: Bulk discovery: Authenticate once with your cloud organization and the wizard lists every account, subscription, or project you can access. Accounts that are already connected are recognized and skipped. Automatic OIDC setup: The recommended flows configure each account with short-lived credentials based on OpenID Connect (OIDC) and workload identity federation. No long-lived cloud secrets are stored in Pulumi Cloud. ESC environments, created for you: The wizard generates Pulumi ESC environments that follow the best practices from the manual OIDC guides -- environments that previously had to be authored one by one. Scans and policies from day one: By default, scheduled discovery scans and a pre-built policy pack are applied to every account as part of setup: the Pulumi Best Practices pack on the Team and Enterprise editions, or a compliance pack (CIS, or NIST 800-53 for AWS and Google Cloud) on Business Critical. Ho ## Google Cloud: PostgreSQL community contribution updates DevFeed: [Google Cloud: PostgreSQL community contribution updates]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-07-10T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [PostgreSQL](), [Google Cloud Platform (GCP)](), [Google](), [Open Source](), [Replication]() Tags: [architecture](), [collaboration](), [community-contribution](), [conference](), [consensus](), [contribution](), [google](), [google-cloud](), [open-source](), [postgresql](), [replication](), [technical](), [updates]() ### AI overview Google Cloud recaps its participation in 2026 PostgreSQL community events, including discussions of logical replication, global index architecture, DDL replication, and AI-assisted development workflows. ### Source excerpt by Dilip Kumar, Cloud SQL for PostgreSQL & Matt Cornillon, Sales EMEA Google Cloud is deeply committed to the long-term success of the PostgreSQL ecosystem. Our involvement goes beyond providing PostgreSQL managed services; it's also about active participation in the open source communities through technical contributions, leadership in conference committees, and sharing architectural insights that benefit all users. Following is a recap of recent events Google Cloud participated in. PGConf.dev 2026 Serving as a vital developer-centric hub, PGConf.dev provides a unique opportunity for collaboration with the full assembly of senior PostgreSQL committers. This gathering is essential for aligning technical efforts and shaping the future project roadmap. Key Highlights Participation focused on strategic coordination with PostgreSQL committers regarding logical replication development, and a consultation on global index architecture. High community interest confirms the Global Index feature solves a vital architectural requirement for enterprises. Established community consensus to pursue a deparsing-based architectural approach for DDL replication. Google Cloud Sessions Dilip Kumar, a PostgreSQL contributor from Google Cloud, presenting "Experimenting with a Global Index in PostgreSQL" at pgconf.dev 2026 in Vancouver. He is speaking at a podium next to a presentation slide detailing the Global Index storage architecture and PartitionIdentifier management. Session Title Session Type Speakers/Led by Experimenting with a Global Index in PostgreSQL: Design, Implementation, and Challenges Technical Talk Dilip Kumar Unconference: Global Indexes Unconference Session Dilip Kumar Unconference: Logical Replication: Warts and Missing Pieces Unconference Session Hannu Krosing PGConf India 2026 Key Highlights The three-day conference was divided into a training day followed by two days of sessions. More than 580 participants attended the conference over three days. The conference se ## Don't forget Google SAS retires June 2027. DevFeed: [Don't forget Google SAS retires June 2027.]() Original publisher: [Read original article]() Author: j2sw Published: 2026-06-23T19:34:33Z Content type: news Language: en Sources: [Justin Wilson (j2sw)]() Topics: [SAS](), [Google](), [Google Cloud Platform (GCP)](), [migration](), [service]() Tags: [2026](), [3ghz](), [cbrs](), [cloud](), [deployments](), [existing](), [google](), [google-cloud](), [migration](), [net-11](), [network-operations](), [operational](), [retirement](), [sas](), [service](), [wireless-networking]() ### AI overview Google Cloud Spectrum Access System (SAS) is being retired. It stopped accepting new customers on June 10, 2026, will remain operational for existing customers during the transition, and is scheduled to shut down on June 10, 2027. ### Source excerpt In case you forgot: RETIREMENT NOTICE Google Cloud Spectrum Access System (SAS) is being retired. Starting June 10, 2026, we are no longer accepting new customers. The service will remain fully operational for existing customers during the transition period and will be shut down on June 10, 2027. Please review the migration steps below for instructions on ... Read more The post Don't forget Google SAS retires June 2027. appeared first on Justin Wilson (j2sw). ## DataAgents: How we turned 9 months of analysis into 10 days DevFeed: [DataAgents: How we turned 9 months of analysis into 10 days]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-09T22:42:18Z Content type: tutorial Language: en Sources: [Capital One Tech]() Topics: [Cloud](), [AI Agent](), [Amazon Web Services](), [Azure](), [Google Cloud Platform (GCP)](), [data]() Tags: [agentic-ai](), [ai](), [aws](), [azure](), [cloud](), [cloud-platform](), [data-analysis](), [false-positives](), [gcp](), [least-privilege](), [security]() ### AI overview This engineering deep dive describes the DataAgents pattern for analyzing heterogeneous cloud resources at scale. It focuses on cloud resource dormancy detection across AWS, Azure, and Google Cloud Platform, using entity-specific criteria, confidence-based prioritization, and documented reasoning. The article reports reducing the analysis effort from an estimated 6-9 months to 10 days. ### Source excerpt An engineering deep dive into the pattern that changed how we approach large-scale classification problems. Every engineering team has that project sitting in the backlog. The one where someone says, "We really should analyze all of these," and the room goes quiet. Everyone knows what "all of these" means -- hundreds of entities, complex rules, no clear starting point. For us, it was cloud resource dormancy detection. We had around 350 distinct cloud resource types spread across AWS, Azure and Google Cloud Platform (GCP). Each type has different behavior patterns. An EC2 instance sitting idle looks nothing like a dormant Amazon S3 (S3) bucket or an unattached Elastic IP. Detecting dormancy required understanding what "active" means for each specific resource, then writing detection logic that wouldn't flood operations teams with false positives. Traditional estimate: 6-9 months of expert analysis. Actual time: 10 days. Here's how we did it, and more importantly, here's the reusable pattern behind it. The problem with large-scale analysis: Before we get to the solution, it's worth naming the pattern that makes these projects so painful. It shows up everywhere: Cloud resources - Which of our 350 resource types are dormant? Data governance - Which of our 800 tables have quality issues we should monitor? Security - Which of our access entitlements violate least-privilege principles? Compliance - Which of our 500 policy controls need remediation? In every case, the structure is similar -- a large catalog of heterogeneous entities, entity-specific rules that don't generalize, unknown priorities and a high cost for getting it wrong. The traditional approach is not just slow. It's structurally limited. You get coverage of the "obvious" cases, inconsistent logic across analysts, and tribal knowledge that evaporates when people leave. What you need is something that can assess each entity, apply consistent criteria, prioritize by confidence and document its reasoning. The DataA ## Metering Object Storage and S3 Billing for SaaS DevFeed: [Metering Object Storage and S3 Billing for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Amazon S3](), [Software as a service](), [cloud-infrastructure](), [Azure](), [Cloud](), [Google Cloud Platform (GCP)]() Tags: [architecture](), [aws](), [aws-s3](), [azure](), [cloud](), [google-cloud](), [guide](), [how-to](), [object-storage](), [s3](), [saas](), [storage](), [usage-based-billing]() ### AI overview This article explains how SaaS products can meter object storage across uploads, stored capacity, egress, and storage operations. It describes an architecture for handling rolling storage measurements, retries, partial failures, high volume, and different pricing models across AWS S3, Google Cloud Storage, Azure Blob Storage, and compatible object stores. ### Source excerpt Bill customers for storage uploads, capacity, and bandwidth across S3, GCS, and Azure Blob. Architecture for accurate object storage metering. ## Announcing Dart support in Cloud Functions for Firebase DevFeed: [Announcing Dart support in Cloud Functions for Firebase]() Original publisher: [Read original article]() Author: Kevin Moore; Rody Davis Published: 2026-05-06T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Cloud Functions](), [Dart](), [Firebase](), [Flutter](), [Firebase CLI](), [Serverless](), [Google Cloud Platform (GCP)]() Tags: [admin-sdk](), [cloud-functions](), [cloud-run](), [dart](), [dart-on-the-server](), [emulator-suite](), [firebase](), [firebase-cli](), [firestore](), [flutter](), [google-cloud](), [launch](), [serverless]() ### AI overview Firebase announced experimental support for Dart in Cloud Functions for Firebase, allowing developers to use Dart and shared tooling across Flutter clients and backend functions. The article also describes AOT compilation, asynchronous I/O, Firebase CLI deployment, and local testing with the Firebase Emulator Suite. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Leveraging CPU memory for faster, cost-efficient TPU LLM training DevFeed: [Leveraging CPU memory for faster, cost-efficient TPU LLM training]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-04-10T18:30:00Z Content type: tutorial Language: en Sources: [Google Open Source Blog]() Topics: [cpu](), [Large Language Model](), [intel](), [Google Cloud Platform (GCP)](), [Cloud](), [implementation]() Tags: [accelerators](), [cloud-tpu](), [cost](), [cpu](), [host-offloading](), [intel-xeon](), [jax](), [llm-training](), [memory](), [performance](), [tpu]() ### AI overview This practical guide explains how to use host activation offloading with JAX on TPU platforms. It describes moving selected activations from TPU device memory to Intel Xeon CPU memory to reduce accelerator memory pressure and support larger models or batch sizes, while discussing potential throughput and cost benefits. ### Source excerpt by Keyur Ruganathbhai Ranipa, Qinglan Xiang, Vrushabh Sanghavi, Ramesh AG & Weilin Wang, Intel and Penporn Koanantakool, Google Host offloading with JAX on Intel® Xeon® processors As Large Language Models (LLMs) continue to scale into the hundreds of billions of parameters, device memory capacity has become a big limiting factor in training, as intermediate activations from every layer in the forward pass are needed in the backward pass. To reduce device memory pressure, these activations can be rematerialized during the backward pass, trading memory for recomputation. While rematerialization enables larger models to fit within limited device memory, it significantly increases training time and cost. Intel® Xeon® processors (5th and 6th Gen) with Advanced Matrix Extensions (AMX) enable practical host offloading of selected memory- and compute-intensive components in JAX training workflows. This approach can help teams train larger models, relieve accelerator memory pressure, improve end-to-end throughput, and reduce total cost of ownership--particularly on TPU-based Google Cloud instances. By publishing these results and implementation details, Google and Intel aim to promote transparency and share practical guidance with the community. This post describes how to enable activation offloading for JAX on TPU platforms and outlines considerations for building scalable, cost-aware hybrid CPU-accelerator training workflows. Figure 1. Google Cloud TPU Pod commonly used in LLM training. Host offloading Traditional LLM training is usually done on device accelerators alone. However, modern host machines have much larger memory size than accelerators (512GB or more) and can offer extra compute power, e.g., TFLOPS in case of Intel® Xeon® Scalable Processor with AMX capability. Leveraging host resources can be a great alternative to rematerialization. Host offloading selectively moves computation or data between host and device to optimize performance and memory usage. Host memo ## Next.js 16.2 introduces a stable Deployment Adapter API for Firebase App Hosting DevFeed: [Next.js 16.2 introduces a stable Deployment Adapter API for Firebase App Hosting]() Original publisher: [Read original article]() Author: James Daniels Published: 2026-03-26T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Next.js](), [Google Cloud Platform (GCP)](), [Deployment](), [Netlify](), [Cloudflare](), [Amazon Web Services]() Tags: [app-hosting](), [cloud-run](), [cloudflare](), [deployment](), [firebase](), [google-cloud](), [hosting](), [javascript](), [netlify](), [next-js](), [release](), [security](), [web]() ### AI overview Firebase announces that Next.js 16.2 includes a stable, documented Deployment Adapter API, intended to give hosting providers an official integration path and support more predictable framework updates. The article presents this as an initial milestone; advanced features still pose architectural challenges. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Monitoring Android Vitals with the Play Developer Reporting API DevFeed: [Monitoring Android Vitals with the Play Developer Reporting API]() Original publisher: [Read original article]() Author: Julien Salvi Published: 2026-03-19T12:41:51Z Content type: tutorial Language: en Sources: [Stories by Julien Salvi on Medium]() Topics: [Android](), [API](), [Google Play](), [Gradle](), [CI/CD Pipeline](), [Google Cloud Platform (GCP)](), [Slack]() Tags: [aircall](), [android](), [android-app-development](), [anr](), [api](), [ci-cd-pipeline](), [crash](), [google-play-developer](), [gradle](), [reporting](), [slack]() ### AI overview This tutorial explains how Aircall uses the Google Play Developer Reporting API in a Gradle task to retrieve Android Vitals, including ANR and crash metrics, and deliver daily health reporting to Slack. It covers metric-set queries, timeline aggregations, service-account authentication, data modeling, and health thresholds. ### Source excerpt At Aircall, we built an automated reporting tool that fetches our Android Vitals metrics thanks to the Play Developer Reporting API and delivers a daily health dashboard straight to Slack. All of this powered by a simple Gradle task. Discovering the API The Google Play Developer Reporting API gives programmatic access to the same quality metrics you see in the Google Play Console. It covers the core Android Vitals: ANR rate, crash rate, slow start rate, and stuck background wakelocks, the signals Google uses to determine whether your app meets the bad behavior thresholds. The API is organized around metric sets that you query with a timeline specification. Each metric set returns daily, 7-day, and 28-day user-weighted aggregates, giving you the full picture from short-term regressions to long-term trends. The key advantage over scraping the Console UI is that you can automate the entire flow and integrate it into your existing CI/CD pipeline. You will find the complete API reference in the official documentation. Build with Play VitalsSetting up the dependency We chose to implement this as a Gradle task inside our build-logic module. This keeps the reporting logic close to the project without polluting the app code. First, let's add the dependency: # libs.versions.toml googleApiReporting = "v1beta1-rev20230803-2.0.0" buildLogic-plugin-google-play-developer-reporting = { module = "com.google.apis:google-api-services-playdeveloperreporting", version.ref = "googleApiReporting" } Which can then be added to the build.gradle.kts of your build-logic module: dependencies { // API to get Google Play store vitals metrics implementation(libs.buildLogic.plugin.google.play.developer.reporting) }Authenticating with a Service Account To access the API, you need a Google Cloud Service Account with the Play Developer Reporting scope. We pass the credentials as an environment variable to keep secrets out of the repository. The Reporting client initializes the API with these credentia ## Unlock custom observability by exporting Crashlytics data to Cloud Logging DevFeed: [Unlock custom observability by exporting Crashlytics data to Cloud Logging]() Original publisher: [Read original article]() Author: Ernan Anguiano; Andrew Brook Published: 2026-01-30T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Crashlytics](), [Logging](), [observability](), [Google](), [Google Cloud Platform (GCP)](