# google cloud Published articles for google cloud. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## 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 ## Introducing Firebase spend caps DevFeed: [Introducing Firebase spend caps]() Original publisher: [Read original article]() Author: Yvonne Monterroso Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [API](), [Cloud](), [Google]() Tags: [ai-logic](), [api](), [app-hosting](), [billing](), [cloud](), [cloud-functions](), [configuration](), [financial](), [firebase](), [firebase-console](), [google-cloud]() ### AI overview Firebase introduces spend caps for eligible services, including Firebase AI Logic, Cloud Functions for Firebase, and Firebase App Hosting. Developers can set service-level limits, receive alerts at 50%, 80%, and 100% of the budget, and pause a service when the cap is reached. ### Source excerpt Spend caps are designed to act as a circuit breaker for services like the Gemini API and Cloud Functions, reducing the risk of a financial surprise from a simple coding error or an unexpected spike in traffic. ## Driving Developer Excellence: Inside the Program Sprints DevFeed: [Driving Developer Excellence: Inside the Program Sprints]() Original publisher: [Read original article]() Author: Anant Nawalgaria; Eric Schmidt; Sokratis Kartakis; Aman Khan Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-governance](), [developer-experience](), [devex](), [documentation](), [google-cloud](), [policy]() ### AI overview Google Cloud's Gemini Enterprise DevEx program tests developer workflows without internal shortcuts to find and resolve friction. This sprint focused on governed AI-agent deployment, covering identity provisioning, registry enrollment, gateway routing, policy enforcement, content safety, and auditable request verification. ### Source excerpt The Gemini Enterprise Developer Experience (DevEx) program conducts ongoing sprint testing of end-to-end developer workflows to identify and rapidly resolve friction points without relying on internal shortcuts. This recent sprint focused on optimizing enterprise AI governance, including refining setup prerequisites, securing extension configurations, and clarifying policy enforcement mechanics to ensure a smoother, more reliable deployment. Developers can now leverage updated documentation and standardized code samples to improve their experience with Agent Gateway and Semantic Governance configurations. ## 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 ## Supabase is now available in Gemini Enterprise DevFeed: [Supabase is now available in Gemini Enterprise]() Original publisher: [Read original article]() Author: Shaziya Bandukia; Elsa Heffernan Published: 2026-09-09T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Google](), [Model Context Protocol](), [Database](), [Back end]() Tags: [database](), [enterprise](), [gemini](), [google-cloud](), [mcp](), [mcp-server](), [partnership](), [real-time]() ### AI overview Supabase is available as a Gemini Enterprise connector, allowing teams to query and act on Supabase projects with natural language and live, real-time responses. ### Source excerpt Connect Supabase to Gemini Enterprise and query your Supabase projects with natural language, right inside the platform. ## Announcing ClickHouse Managed Postgres on Google Cloud DevFeed: [Announcing ClickHouse Managed Postgres on Google Cloud]() Original publisher: [Read original article]() Author: Kunal Gupta Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Google](), [Databases](), [cloud-infrastructure]() Tags: [analytics](), [clickhouse](), [gcp](), [google-cloud](), [postgres](), [replication]() ### AI overview ClickHouse announces a private preview of its managed Postgres service on Google Cloud, with NVMe-backed storage, native CDC into ClickHouse, and pg_clickhouse for a unified query layer. ### Source excerpt ClickHouse Managed Postgres is expanding to Google Cloud, bringing NVMe-backed storage, native CDC into ClickHouse, and a unified query layer via pg_clickhouse to GCP private preview customers. ## 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. ## KCD Around the World: San Francisco Bay Area DevFeed: [KCD Around the World: San Francisco Bay Area]() Original publisher: [Read original article]() Author: The Kubernetes Authors Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Kubernetes Contributors Blog]() Topics: [Kubernetes](), [Cloud Native Ecosystem](), [Google]() Tags: [2026](), [cloud-native](), [cncf](), [community](), [contributors](), [developer-relations](), [google-cloud](), [kubernetes](), [open-source]() ### AI overview An interview with organizers of the second KCD San Francisco Bay Area, scheduled for September 1, 2026, explores the event's origins, the local Kubernetes community, and the organizers' backgrounds in Kubernetes and cloud-native technology. ### Source excerpt Welcome back to KCD Around the World, a series where we explore Kubernetes Community Days from every corner of the globe. Behind every event is a community of volunteers, contributors, organizers and attendees who make it all happen. Through conversations with the people behind each KCD, we share what makes it unique. This stop: San Francisco Bay Area. Coming up on September 1, 2026 is the second KCD San Francisco Bay Area, held at the Computer History Museum in Mountain View. We spoke with three of its organizers, Rey Lejano, Lisa-Marie Namphy, and Jason (Jay) Smith, about what makes the Bay Area community tick. Meet the community Kashish Verma (KV): Could you each introduce yourselves and tell us how you got involved in Kubernetes? Rey Lejano (RL): I'm one of the KCD San Francisco Bay Area organizers, a Kubernetes SIG Docs co-chair and SIG Security subproject lead, and I work at Red Hat. I've served on seven Release Teams, including as the v1.23 Release Lead. A conversation with a Kubernetes contributor at the 2019 KubeCon in San Diego is what pulled me into contributing to the project. Lisa-Marie Namphy (LN): Hi! I'm Lisa-Marie Namphy (Lisa), I've been a CNCF Ambassador since the early days of the program and have run one of the largest Kubernetes/Cloud Native user groups for over 10 years, now called Cloud Native Silicon Valley. I also run the Developer Relations and Tech Learning team at Intuit, where open source is core to how we build: our flagship products like TurboTax and QuickBooks run on Kubernetes and other CNCF technologies, including Argo, which Intuit originally created and donated. Jason "Jay" Smith (JS): I'm a Staff Customer Engineer on the Application Ecosystem team at Google Cloud, though I like to call myself the "Cloud Alchemist," since I help customers move from legacy monolithic architectures to microservices. On the community side, I'm a co-lead for the Cloud Native San Francisco Community, an organizer for the 2025 and 2026 KCD San Francisc ## Instrument serverless apps with agentic onboarding DevFeed: [Instrument serverless apps with agentic onboarding]() Original publisher: [Read original article]() Author: Rohan Agarwal; Piyali Banerjee Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Serverless](), [AWS Lambda](), [azure container apps](), [Cloud Run](), [Command-line interface](), [MCP Server](), [Artificial Intelligence](), [Claude Code](), [cursor](), [Node.js](), [Terraform]() Tags: [agentic](), [ai](), [aws](), [aws-lambda](), [azure](), [azure-container-apps](), [claude-code](), [cli](), [cursor](), [google-cloud](), [google-cloud-run](), [mcp-server](), [node](), [serverless](), [serverless-monitoring](), [terraform]() ### AI overview Datadog's agentic onboarding helps instrument serverless applications across AWS Lambda, Google Cloud Run, and Azure Container Apps. Developers can use an AI assistant connected through the Datadog MCP Server or run the AI Setup CLI to inspect projects, adapt setup to existing deployment tools and runtimes, and prepare configuration changes for review. ### Source excerpt Use Datadog agentic onboarding to instrument AWS Lambda, Google Cloud Run, and Azure Container Apps from an AI assistant or CLI. ## 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. ## Normalize security logs to Google SecOps UDM with Observability Pipelines DevFeed: [Normalize security logs to Google SecOps UDM with Observability Pipelines]() Original publisher: [Read original article]() Author: Danielle Park Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [amazon-vpc](), [google-cloud](), [google-secops](), [logs](), [mapping](), [observability-pipelines](), [security](), [telemetry](), [threat-detection](), [vpc-flow-logs]() ### AI overview The article explains how Observability Pipelines Google SecOps packs normalize security logs into the Unified Data Model before they reach Google SecOps. ### Source excerpt Learn how Observability Pipelines normalizes your telemetry to Google SecOps UDM, enabling both consistent investigations across sources and precise upstream control over your SIEM ingest. ## Three hidden GKE optimization opportunities unlocked by Google Cloud VM modernization DevFeed: [Three hidden GKE optimization opportunities unlocked by Google Cloud VM modernization]() Original publisher: [Read original article]() Author: Federico Iezzi Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Kubernetes](), [Optimization](), [benchmarking](), [Cloud](), [Google](), [cloud-infrastructure](), [Terraform]() Tags: [autoscaling](), [benchmarking](), [cloud](), [code](), [cpu](), [efficiency](), [google-cloud](), [infrastructure](), [kubernetes](), [modernization](), [optimization](), [performance](), [terraform]() ### AI overview This article examines hidden GKE optimization opportunities from modernizing Google Cloud VM infrastructure. It highlights moving node pools from older N1 or E2 machine types to N4 instances, benchmarking equivalent workloads, and evaluating node consolidation, throughput, storage efficiency, and total cost of ownership. ### Source excerpt Unlock hidden GKE optimizations by modernizing your Google Cloud VM infrastructure. Discover how switching to N4 instances and leveraging Hyperdisk storage pools can boost performance, increase storage efficiency, and reduce your total cost of ownership ## Accelerating the frontiers of scientific discovery: Google's $40M commitment to the Genesis Mission DevFeed: [Accelerating the frontiers of scientific discovery: Google's $40M commitment to the Genesis Mission]() Original publisher: [Read original article]() Author: Pushmeet Kohli; Karthik Narain Published: 2026-07-22T13:38:54Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Frontier AI](), [Artificial Intelligence](), [Cloud](), [data](), [coding]() Tags: [ai](), [frontier-ai](), [google](), [google-cloud](), [government](), [platform](), [public-sector](), [research](), [science](), [tokens](), [work]() ### AI overview Google commits $40 million in AI tokens and cloud credits to support researchers and the U.S. Department of Energy's Genesis Mission. The commitment provides access to Google DeepMind's AI-for-science tools and Gemini for Government across DOE national laboratories. ### Source excerpt Google commits $40M in AI tokens and credits for the Genesis Mission ## 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 ## Monitor Apigee X API traffic and security with Datadog DevFeed: [Monitor Apigee X API traffic and security with Datadog]() Original publisher: [Read original article]() Author: Sydney McLaughlin; Cody Murray-Bruce; Eddie Cai Published: 2026-07-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry](), [dashboards]() Tags: [api](), [dashboards](), [google-cloud](), [infrastructure-monitoring](), [integration](), [latency](), [logs](), [metrics](), [monitoring](), [security](), [telemetry](), [traces]() ### AI overview This tutorial explains how to use Datadog's Google Cloud Apigee X integration to monitor API traffic, latency, anomalies, security scores, and related logs and traces. ### Source excerpt Learn how to monitor Apigee X with Datadog so you can track API traffic, latency, anomalies, and security posture and catch issues before clients do. ## Knowledge as Code: The Memory File Just Got a Spec DevFeed: [Knowledge as Code: The Memory File Just Got a Spec]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-07-14T00:00:00Z Content type: opinion Language: en Sources: [Pulumi]() Topics: [Wiki](), [Markdown](), [Retrieval Augmented Generation (RAG)](), [context window]() Tags: [agent](), [ai](), [ai-agents](), [google](), [google-cloud](), [knowledge-base](), [memory](), [perspectives](), [platform-engineering](), [rag](), [schema]() ### AI overview The article argues that agent memory files need a shared format so knowledge bases can be exchanged between people and agents. It describes Google's LLM wiki pattern as an approach using interlinked Markdown pages, summaries, change logs, and entity pages, contrasting it with repeatedly retrieving and re-deriving information from raw documents. ### Source excerpt Five weeks ago I wrote that the least glamorous piece of an agent loop is also the one that decides whether it compounds: memory. A markdown file outside the context window that holds what is done, what is next, and what was learned, because the model forgets all of it between runs. Write the memory file before the loop. What I left open, because there was nothing to point at, was the format. My memory file looked nothing like yours, and neither of our agents could read the other's. Three days after that post went live, Google shipped an answer. The pattern everyone copied Andrej Karpathy published a gist in April he called the LLM wiki, and it collected thousands of stars and forks. It's meant to be pasted straight into a coding agent. Instead of indexing your documents for RAG and re-deriving answers from raw text on every query, the agent builds a wiki and keeps it current: interlinked markdown pages, an index.md with a one-line summary per page, a log.md recording every change, entity pages that grow as sources come in. Drop in a meeting transcript and the agent reads it, updates a dozen existing pages, fixes the cross-references, and appends to the log in one pass. It took off for the same reason wikis usually die. A knowledge base is valuable in exact proportion to the bookkeeping nobody wants to do: summarizing, linking, reconciling contradictions, pruning stale claims. Karpathy's line, which Google now quotes back in its own announcement, is that LLMs "don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass." The human curates sources and asks questions. The agent does the janitorial work that made every previous wiki rot. A wiki only your agent can read Then everyone built one, and every one is a dialect. Mine links related pages in the frontmatter; yours links them at the bottom of the body. Mine has a tags field; yours calls it categories. None of this matters while the wiki serves one person, because the schema lives ## Introducing the Hyperscaler GTM Playbook DevFeed: [Introducing the Hyperscaler GTM Playbook]() Original publisher: [Read original article]() Author: flashdba Published: 2026-07-13T21:11:00Z Content type: article Language: en Sources: [flashdba]() Topics: [Cloud](), [cloud-infrastructure](), [Amazon Web Services](), [Azure](), [Google]() Tags: [articles](), [aws](), [azure](), [cloud](), [cloud-alliances](), [cloud-co-sell](), [cloud-marketplace](), [google-cloud](), [guide](), [hyperscaler](), [isv](), [partner](), [practitioner]() ### AI overview A vendor-neutral 16-article Hyperscaler GTM Playbook explains cloud alliances, co-sell, and marketplace operations for ISVs selling alongside AWS, Azure, and Google Cloud. It emphasizes the practical differences among the hyperscalers and the importance of whether software runs in the provider's or the customer's cloud account. ### Source excerpt A vendor-neutral practitioner guide on cloud alliances, co-sell and marketplace - 16 articles covering everything from partner programs to private offers, for ISVs selling with AWS, Azure and Google Cloud. ## 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 ## Best Infrastructure as Code (IaC) Tools for 2026 DevFeed: [Best Infrastructure as Code (IaC) Tools for 2026]() Original publisher: [Read original article]() Author: Isaac Harris Published: 2026-07-05T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [Provisioning](), [Terraform](), [opentofu](), [Software Engineering](), [Programming](), [AWS CloudFormation](), [Development]() Tags: [2026](), [aws](), [aws-cloudformation](), [azure](), [devops](), [general](), [google-cloud](), [iac](), [infrastructure-as-code](), [kubernetes](), [opentofu](), [programming](), [provisioning](), [pulumi](), [software-engineering](), [terraform]() ### AI overview A 2026 guide compares infrastructure as code tools including Pulumi, Terraform, OpenTofu, AWS CDK, AWS CloudFormation, Azure tools, Google Cloud Infrastructure Manager, Kubernetes YAML, and Crossplane. It discusses their approaches, licensing, pricing, AI-agent readiness, and tradeoffs, while explaining how IaC applies software engineering practices such as version control, testing, code review, and CI/CD to infrastructure management. ### Source excerpt The best infrastructure as code (IaC) tools in 2026 are Pulumi, Terraform, OpenTofu, AWS CDK, AWS CloudFormation, Azure ARM, Azure Bicep, Google Cloud Infrastructure Manager, Kubernetes YAML, and Crossplane. Each takes a different approach to defining and provisioning infrastructure, from general-purpose programming languages to declarative templates, and each carries distinct licensing, pricing, and AI-agent readiness tradeoffs worth weighing before you commit. Pulumi's own package registry lists hundreds of packages -- first-party providers, bridged Terraform providers, and community components -- illustrating how far multi-cloud coverage has expanded across the ecosystem. Since this post was published, Pulumi has added first-class support for HashiCorp Configuration Language (HCL). You can now write Pulumi programs in HCL directly, alongside general-purpose languages like TypeScript, Python, Go, and C#. To see how it works, see Pulumi HCL. As infrastructure complexity grows, teams increasingly seek approaches that provide the same developer productivity tools they use for application development. While template-based and domain-specific language approaches serve many use cases effectively, teams with complex requirements or programming backgrounds often find that general-purpose programming languages offer advantages in testing, abstraction, and collaboration. This comprehensive guide examines the most effective infrastructure as code tools available today, providing detailed analysis of core IaC platforms, complementary tools, and related technologies through the lens of software engineering best practices. Whether you're starting fresh with IaC or evaluating alternatives to overcome limitations in your current toolchain, we'll help you navigate this complex landscape and choose solutions that truly bring software engineering to infrastructure. Ready to try one of these approaches yourself? Get started with Pulumi for free and provision your first resource in minu ## Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi DevFeed: [Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Ollama](), [Cloud](), [Infrastructure as code](), [model-serving](), [GPU](), [Artificial Intelligence](), [OpenID connect (OIDC)](), [Ubuntu]() Tags: [ai](), [aws](), [azure](), [cloud](), [gcp](), [google-cloud](), [gpu](), [inference](), [infrastructure-as-code](), [model-serving](), [oidc](), [ollama](), [pulumi-esc](), [tutorials](), [typescript](), [ubuntu]() ### AI overview This tutorial shows how to use Pulumi to provision GPU virtual machines on AWS, Azure, and Google Cloud, install drivers, run Ollama, and pull an open-source model without manual setup. It also uses Pulumi ESC with OIDC instead of a long-lived access key and avoids treating model-download completion as an infrastructure resource. ### Source excerpt Putting Ollama on a cloud GPU is something I keep coming back to. A while ago I wrote up running open-source LLMs on an AWS EC2 box with Ollama and Pulumi, and the shape never really changes: a GPU instance, a model server, and a firewall rule in front. Infrastructure as code earned its place by making that kind of setup predictable and repeatable, and AI infrastructure is no exception. A GPU box serving a model is still a VM, a disk, and a firewall rule, and it should be declared like one. Thorsten Hans made exactly that case in his Akamai post, Fully Automated AI Infrastructures with Terraform and Akamai Cloud, which stands up a single GPU instance on Linode, installs the drivers, runs Ollama, and pulls a model, with no manual steps after terraform apply. I liked the shape of it, so this post ports the same idea to Pulumi and runs it across AWS, Azure, and Google Cloud instead of one. The result is one program shape per cloud: a single pulumi up brings up a GPU box that installs its own driver, runs Ollama, and pulls a model with no manual steps, and a single pulumi destroy takes it back down. Along the way it drops the two imperative bits the Terraform version leans on: a static access token sitting in an environment variable, and a null_resource running a shell loop to wait for the model. The first becomes an OIDC login from a Pulumi ESC environment, so no long-lived key lives anywhere. The second turns out not to be a resource at all. What you are building Strip away the per-cloud naming and every version of this is the same three things: a GPU virtual machine, a firewall in front of it, and a cloud-init script that turns a bare Ubuntu box into a running inference server. The model serving runs on Ollama, which exposes an HTTP API on port 11434 and keeps the model resident in GPU memory between requests. flowchart LR Dev([Your machine / curl]) -->|"HTTP :11434"| FW["Firewall / security group (allow 11434, optional 22)"] FW --> VM["GPU VM (Ubuntu 24.04) [Next page]()