# Cloud APIs Application programming interfaces for programmatic access to cloud computing services and infrastructure. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Co-Create: Building GitLab with our users DevFeed: [Co-Create: Building GitLab with our users]() Original publisher: [Read original article]() Author: Isa Huerga Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [GitLab](), [CI/CD](), [Scala](), [Claude](), [Application Security](), [Cloud APIs]() Tags: [api](), [ci-cd](), [co-created](), [community](), [contributors](), [duo](), [engineering](), [open-source](), [rest-api](), [scala](), [security]() ### AI overview GitLab users collaborated through the Co-Create program on product improvements delivered in the first half of 2026, including API extensions, CI/CD capabilities, Scala support for AI-powered code understanding, security controls, and integration flexibility. ### Source excerpt GitLab users bring firsthand experience of the workflows they want to improve. Through our Co-Create program, they collaborate directly with us to design, build, and deliver product improvements that benefit more teams. In the first half of 2026, our users helped us extend APIs, add CI/CD capabilities, expand language support for AI-powered code understanding, strengthen security controls, increase the flexibility of integrations, and make GitLab easier to use and understand. Co-Create gives customers a direct path from a real-world challenge to a product improvement. Customers collaborate with us on the problem and solution, and help deliver the change. Unlike a feedback-only model, this puts real workflow context into the product development process. It creates a shorter path from need to value while helping us validate improvements in real conditions and extend their benefits to more GitLab users. "Over ten years ago, we set out to build code.siemens.com, our internal coding platform based on GitLab, which today serves close to 100,000 people. We've run and scaled the platform with a tiny team, guided by a 'for developers, from developers' philosophy. We co-created hundreds of features such as x509/gpg signed commits, protected container/packages, and project and social features such as follow users, allowing us to strengthen the security and collaboration culture at the scale of Siemens." -- Roger Meier, Distinguished Engineer, Siemens From a specific need to a shared improvement Some of the best product improvements start with a specific, real-world need. GitLab contributors helped us address the following needs: Making Scala codebases understandable to Orbit and GitLab Duo Giving security administrators a way to enforce Secret Push Protection across an entire instance Letting high-throughput teams configure merge train pipeline limits Showing the platforms supported by multi-architecture images Providing screen reader users with more context on documentation pa ## Pulumi Context API: query your infrastructure as a graph DevFeed: [Pulumi Context API: query your infrastructure as a graph]() Original publisher: [Read original article]() Author: Levi Blackstone Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [API](), [Graphs](), [Cloud APIs](), [infrastructure as code (IAC)](), [Command-line interface](), [JSON](), [pulumi-neo]() Tags: [agent](), [ai-agents](), [api](), [cli](), [features](), [graph](), [iac](), [infrastructure-as-code-iac](), [json](), [product](), [pulumi](), [pulumi-cloud](), [pulumi-neo](), [rest-api]() ### AI overview Pulumi is launching the Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. Available in public preview for Enterprise and Business Critical organizations, it supports infrastructure-impact, coverage, and cleanup queries through the Pulumi CLI or REST API. Pulumi Neo uses it out of the box, and other authenticated agents can access its schema and query guidance. ### Source excerpt Every platform team fields the same questions: What is running? What breaks if we change this? What can we safely delete? The answers exist, but they're scattered across state files, cloud consoles, and the memories of whoever set things up. Today we're launching the Pulumi Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. It's designed agent-first: Pulumi Neo, our infrastructure agent, uses it out of the box, and other agents can fetch the current graph vocabulary and query guidance on demand. It's available in public preview for organizations on the Enterprise and Business Critical editions. Answers that follow infrastructure relationships Pulumi already records the resources your programs manage, their dependencies, how stacks consume each other's outputs, and the resources Pulumi Discovery finds outside infrastructure as code (IaC). The Context API connects this data so you can ask questions that depend on the relationships: Impact: Which stacks are affected if we upgrade this provider? If this stack changes, what consumes its outputs? Coverage: How much of our infrastructure lives outside IaC, and in which accounts? Cleanup: Which stacks have no dependents and are candidates for retirement? A query is a JSON document with a handful of clauses. anchor names the starting nodes, traverse follows relationships from there, and return chooses what comes back. You can run a query through the Pulumi CLI or REST API. Here's a selector that starts from AWS provider instances older than version 7.0.0 and follows incoming provided_by relationships back to the visible resources they manage: { "anchor": { "nodeType": "resource", "match": { "type": "pulumi:providers:aws", "fields": { "provider_version": { "op": "lt", "value": "7.0.0" } } } }, "traverse": [ { "edgeTypes": ["provided_by"], "direction": "in", "alias": "managed" } ], "return": { "select": ["anchor", "managed"] } } A response for one m ## Introducing wasi-grpc for Spin DevFeed: [Introducing wasi-grpc for Spin]() Original publisher: [Read original article]() Author: Brian Hardock Published: 2025-09-08T12:00:00Z Content type: release Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [gRPC](), [Cloud Native Ecosystem](), [Cloud APIs]() Tags: [2](), [apis](), [components](), [http](), [service](), [wasi]() ### AI overview Spin 3.4 adds outbound HTTP/2 support, allowing components to function as gRPC clients and enabling Spin applications to connect directly to existing gRPC systems, cloud APIs, and service meshes. ### Source excerpt Spin 3.4 introduces outbound HTTP/2 support, enabling components to act as first-class gRPC clients. Spin applications can now call into existing gRPC-based systems, cloud APIs, and service meshes directly, without workarounds. ## Analyze Your Nx Cloud Runs With Your AI Assistant DevFeed: [Analyze Your Nx Cloud Runs With Your AI Assistant]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-06-25T00:00:00Z Content type: tutorial Language: en Sources: [Juri Strumpflohner]() Topics: [ci](), [Model Context Protocol (MCP)](), [Cloud APIs](), [Conversational AI](), [Architecture & optimization](), [build times]() Tags: [ai](), [ai-assistants](), [build-times](), [ci](), [devops](), [mcp](), [model-context-protocol-mcp](), [nx](), [nx-cloud](), [optimization](), [resource]() ### AI overview This article explains how Nx Cloud MCP connects AI assistants to live CI data so technical teams can analyze pipeline runs conversationally, identify recurring build and test failures, compare cache performance, and optimize development workflows. ### Source excerpt Learn how to use Nx Cloud MCP to analyze CI data conversationally with AI assistants, identify failure patterns, and optimize your development pipeline through data-driven insights. ## See how ML Kit and ARKit play together DevFeed: [See how ML Kit and ARKit play together]() Original publisher: [Read original article]() Author: Ibrahim Ulukaya Published: 2018-12-17T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Firebase](), [iOS](), [Mobile](), [Cloud APIs](), [object-detection]() Tags: [android](), [api](), [apple](), [arkit](), [cloud-apis](), [firebase](), [image](), [ios](), [machine-learning](), [ml-kit]() ### AI overview This tutorial explains how to combine ML Kit and ARKit in an iOS project. It describes processing camera frames with ML Kit image labeling, using on-device results for responsiveness and cloud-based labeling for higher accuracy, then displaying the detected label in a 3D scene. ### Source excerpt News, tutorials, and updates from the Firebase team. ## SendGrid cn1lib DevFeed: [SendGrid cn1lib]() Original publisher: [Read original article]() Author: Shai Almog Published: 2018-08-13T00:00:00Z Content type: release Language: en Sources: [CodeName One]() Topics: [SendGrid](), [migration](), [Cloud](), [API](), [Cloud APIs](), [App]() Tags: [api](), [app](), [cloud](), [email](), [migration](), [rest-api](), [sendgrid](), [servers]() ### AI overview Codename One introduces a new cn1lib integrating SendGrid's REST API for transactional email. The article recommends migrating from the cloud email feature because the previous API could lead to blacklisting and delivery failures, while clarifying that the standard send email APIs remain unaffected. ### Source excerpt When we announced the migration to the new cloud servers one of the casualties was the cloud email API. This was a well intentioned API for sending an email from an app. Unfortunately we didn't understand the complexities of modern mail systems well enough when we came up with this API. It turns out that this is pretty problematic. Mail servers get blacklisted and emails fail to deliver. ## Introducing ML Kit for Firebase DevFeed: [Introducing ML Kit for Firebase]() Original publisher: [Read original article]() Author: Sachin Kotwani Published: 2018-05-10T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Machine learning](), [SDKs](), [Mobile](), [Android](), [iOS](), [Cloud APIs](), [Google]() Tags: [android](), [api](), [apis](), [cloud-apis](), [firebase](), [google](), [google-i-o](), [ios](), [launch](), [machine-learning](), [ml](), [ml-kit](), [mobile](), [news](), [updates]() ### AI overview Firebase introduces ML Kit in beta, an SDK that helps Android and iOS developers add machine-learning features without requiring extensive machine-learning expertise. It includes ready-to-use APIs for text recognition, face detection, barcode scanning, image labeling, and landmark recognition, with both on-device and cloud-based options. ### Source excerpt News, tutorials, and updates from the Firebase team.