# CI/CD CI/CD is a build automation strategy and part of DevOps Continuous integration: Frequent merging of several small changes into a main branch. Continuous delivery: When teams produce software in short cycles with high speed and frequency so that reliable software can be released at any time, and with a simple and repeatable deployment process when deciding to deploy. Continuous deployment: When new software functionality is rolled out completely automatically. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AWS Elastic Beanstalk introduces Cluster Mode DevFeed: [AWS Elastic Beanstalk introduces Cluster Mode]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-09-17T19:20:21Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Amazon Web Services](), [Deployment](), [Amazon EKS](), [autoscaling](), [Monitoring](), [observability](), [Provisioning](), [Secrets Management](), [AWS Certificate Manager](), [CI/CD]() Tags: [amazon-eks](), [amazon-elastic-kubernetes-service](), [application](), [autoscaling](), [aws](), [aws-certificate-manager](), [aws-elastic-beanstalk](), [ci-cd](), [cluster](), [code](), [compute](), [container](), [deployment](), [dockerfiles](), [https](), [infrastructure](), [kubernetes](), [launch](), [monitoring](), [news](), [provisioning]() ### AI overview AWS Elastic Beanstalk introduces a fully managed Cluster Mode that runs multiple applications on shared infrastructure powered by Amazon EKS. It also highlights AI-assisted environment analysis, GitHub Actions deployment, observability, traffic-splitting deployments with rollback, autoscaling, secrets management, and HTTPS by default. ### Source excerpt Run an application on AWS Elastic Beanstalk Cluster Mode without provisioning or operating the compute underneath it. You provide a container image or source code; Elastic Beanstalk with service-operated compute creates and operates the environment that runs it. ## From hours to minutes: Optimizing Red Hat Developer Hub performance testing with immutable LDAP images DevFeed: [From hours to minutes: Optimizing Red Hat Developer Hub performance testing with immutable LDAP images]() Original publisher: [Read original article]() Author: Shashank Kestwal Published: 2026-09-17T13:16:48Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Performance Testing](), [LDAP](), [Keycloak](), [CI/CD](), [Concurrency](), [Deployment](), [rbac]() Tags: [ci-cd](), [concurrency](), [deployment](), [keycloak](), [ldap](), [migration](), [performance-testing](), [testing]() ### AI overview This Red Hat developer article describes a performance-testing bottleneck in Red Hat Developer Hub caused by generating users and groups at runtime through the Keycloak Admin REST API and synchronizing them over HTTP. It introduces pre-built LDAP container images as immutable infrastructure to reduce environment setup complexity and improve testing feedback loops. ### Source excerpt At Red Hat, our CI/CD pipelines are the heartbeat of our development process. However, as we scaled the Red Hat Developer Hub performance testing framework, we hit a wall. Our environment setup time was ballooning, turning what should have been a seamless verification step into a long waiting game. For performance engineering in catalog-dependent applications, deployment and catalog population speed directly dictate your feedback loops. The post From hours to minutes: Optimizing Red Hat Developer Hub performance testing with immutable LDAP images appeared first on Red Hat Developer. ## Catching poor LLM performance and accuracy before deployment DevFeed: [Catching poor LLM performance and accuracy before deployment]() Original publisher: [Read original article]() Author: Christopher Miyai Published: 2026-09-17T13:16:47Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [vllm](), [Inference Performance](), [LLM evaluation / benchmarking](), [benchmarking](), [CI/CD](), [model-deployment](), [Open Source](), [container]() Tags: [ai-inference](), [benchmarking](), [benchmarks](), [ci-cd](), [container-images](), [deployment](), [developer-tools](), [llm](), [open-source](), [performance](), [vllm]() ### AI overview This article describes a regression-testing system for Red Hat AI Inference that compares upstream and midstream vLLM builds. It benchmarks LLM performance and accuracy across hardware variants and uses CI/CD to detect regressions before release. ### Source excerpt vLLM is the leading open source inference and serving engine for LLMs. Averaging 64 commits a day with bi-weekly releases, the open source project goes through a significant amount of code changes rapidly. Red Hat AI Inference offers an integrated inference platform powered by vLLM, llm-d and vLLM's llm-compressor. The post Catching poor LLM performance and accuracy before deployment appeared first on Red Hat Developer. ## Build a unified CI/CD control plane with Red Hat Developer Hub DevFeed: [Build a unified CI/CD control plane with Red Hat Developer Hub]() Original publisher: [Read original article]() Author: Rishabh Singh Published: 2026-09-17T13:16:44Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [internal developer portal](), [Backstage](), [argo-cd](), [CI/CD](), [Platform Engineering]() Tags: [argo-cd](), [backstage](), [ci-cd](), [cognitive-load](), [developer-portal](), [developer-productivity](), [guide](), [how-to](), [onboarding](), [openshift](), [platform](), [platform-engineering]() ### AI overview This guide explains how platform engineers can deploy and configure Red Hat Developer Hub as a unified developer portal for Red Hat OpenShift, Argo CD, Tekton, Quay, and related deployment information. It describes aggregating pipeline runs, deployment health, and container image tags in one view, along with software templates for self-service project bootstrapping. ### Source excerpt If your engineering teams waste time context-switching between Red Hat OpenShift, Argo CD, Tekton, and Quay, your platform needs a unified developer portal. The post Build a unified CI/CD control plane with Red Hat Developer Hub appeared first on Red Hat Developer. ## How to build a Language Server Protocol (LSP) plugin for Claude Code DevFeed: [How to build a Language Server Protocol (LSP) plugin for Claude Code]() Original publisher: [Read original article]() Author: Roger Winter Published: 2026-09-17T07:00:00Z Content type: tutorial Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Claude Code](), [CircleCI](), [Protocol (disambiguation)](), [YAML](), [CI/CD](), [Code]() Tags: [ai-development](), [ci-cd](), [circleci](), [claude-code](), [code](), [how-to](), [protocol](), [yaml]() ### AI overview This tutorial explains how to wrap CircleCI's open-source YAML language server as a Claude Code plugin. The plugin provides real-time diagnostics, hover guidance, and best-practice feedback while Claude edits CircleCI configuration files. The article also discusses the implementation, reuse with other language servers, and a benchmark against editing without that feedback, while noting that the example plugin is unofficial and unsupported. ### Source excerpt Build custom Claude Code plugins to wrap the CircleCI language server. Improve your CI/CD configs with real-time diagnostics and best-practice guidance. ## New MCP tools help platform teams scale automation safely DevFeed: [New MCP tools help platform teams scale automation safely]() Original publisher: [Read original article]() Author: Rebecca Carter Published: 2026-09-17T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [Model Context Protocol (MCP)](), [GitLab](), [Automation](), [Artificial Intelligence](), [CI/CD](), [Security]() Tags: [agentic](), [agentic-ai](), [ai](), [automation](), [features](), [mcp](), [platform](), [platform-teams](), [product](), [security-policies](), [software](), [software-delivery]() ### AI overview GitLab 19.4 expands its MCP server with tools that let platform teams automate more of the software lifecycle, including running pipelines, managing merge requests, and triaging vulnerabilities. The release pairs broader agent access with governance controls: read-only actions can run automatically, while writes, merges, and deletes require human review. ### Source excerpt Agentic tools are moving fast past code completion into running pipelines, opening merge requests, and triaging work on a team's behalf. The Model Context Protocol (MCP) has become the common way these agents reach into a team's existing tools, which means the agents touching an organization's software delivery pipeline are no longer only the ones a platform team chose and configured themselves. That reach is also where automation typically stalls. The more an agent can do, the more a platform team has to think about what it should be allowed to do without asking first, and that's a governance problem that tends to get solved by restricting access rather than by extending it. Left unaddressed, it doesn't resolve on its own: Teams either lock agents down to a handful of safe, read-only actions, or they open things up and hope, neither of which scales. GitLab's answer is to make wide tool access and consistent governance travel together. As platform engineers roll out agents across their organization, whether that's a GitLab flow or a third-party client connected through the GitLab MCP server, it needs the same reach into GitLab. But broader reach raises the stakes on control: a human should decide which actions an agent can take on its own and which need a reviewer, so routine, read-only lookups move fast while anything that writes, merges, or deletes waits for the reviewer that team chose. GitLab Transcend returns in October Coding agents are increasing your speed of development, but your reviews, security policies, and release cycles still have to keep pace. Our Transcend event on October 6 will demonstrate how GitLab is helping teams close that gap and explore what it takes to carry the speed of agentic AI across the software lifecycle. Register for the livestream today! Built for how platform teams scale automation With GitLab 19.4, platform teams can automate more of the software lifecycle end to end: An agent can run a pipeline, open and update a merge request, ## Architecting a secure landing zone in the AWS European Sovereign Cloud DevFeed: [Architecting a secure landing zone in the AWS European Sovereign Cloud]() Original publisher: [Read original article]() Author: Pablo Pagani Published: 2026-09-16T21:20:48Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Cloud](), [Architecture & Design](), [AWS IAM](), [Infrastructure as code](), [CI/CD](), [SIEM, Security](), [Incident response]() Tags: [advanced-300](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [cloud](), [eu](), [europe](), [incident-response](), [infrastructure-as-code-iac](), [partition](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to architect a secure, scalable landing zone in the AWS European Sovereign Cloud. It describes the aws-eusc partition boundary and covers governance, identity, logging, data protection, network design, CI/CD, artifact distribution, and incident response. ### Source excerpt The AWS European Sovereign Cloud is a new, independent cloud for Europe, physically and logically separate from existing AWS Regions and operated within the European Union (EU). It provides the same services, features, and APIs as AWS commercial Regions, but runs as a distinct AWS partition (aws-eusc), with its own control plane, AWS Identity and [...] ## 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 ## Learn How to Deploy, Secure, and Automate Full-Stack Web Apps DevFeed: [Learn How to Deploy, Secure, and Automate Full-Stack Web Apps]() Original publisher: [Read original article]() Author: Beau Carnes Published: 2026-09-16T15:28:18Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [web applications](), [Deployment](), [CI/CD](), [FastAPI](), [GitHub Actions](), [Vue.js](), [nginx](), [firewalls](), [Python](), [Cloudflare](), [SSL](), [Ubuntu]() Tags: [analysis](), [authentication](), [ci-cd](), [deploy](), [fastapi](), [full-stack](), [github-actions](), [how-to](), [web](), [web-apps](), [youtube]() ### AI overview This article introduces an 11-hour freeCodeCamp course on deploying, securing, and automating a Vue.js and FastAPI web application. It covers Ubuntu server setup, SSH hardening, firewalls, application runtimes, Meilisearch, DNS, SSL, Nginx, Cloudflare, GitHub Actions, security testing, and observability. ### Source excerpt Taking a web application from local development to a live, secure production environment can be daunting if you've never looked under the hood. We just published a comprehensive course on the freeCode ## Test GitHub Actions Locally with Act DevFeed: [Test GitHub Actions Locally with Act]() Original publisher: [Read original article]() Author: Valerie Nielson Published: 2026-09-16T12:00:44Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [GitHub Actions](), [CI/CD](), [Docker](), [Testing](), [unit test]() Tags: [ci-cd](), [developer-tools](), [docker](), [github](), [github-actions](), [pull-request](), [testing](), [unit-test](), [workflow]() ### AI overview This tutorial explains how to test GitHub Actions workflows locally with act. It describes how act uses Docker to reproduce the workflow environment, including environment variables and the file system, and demonstrates testing pull-request workflows and unit tests before merging. ### Source excerpt GitHub Actions are a great way to implement CI/CD into your projects, whether personal or professional. GitHub has a lot of great tools to help with CI/CD, but GitHub Actions in particular is very helpful. However, writing actions can be tedious and a little annoying, especially if you don't know what you're doing. On top [...] The post Test GitHub Actions Locally with Act appeared first on Atomic Spin. ## Automate Docker Registry Creation with Harness and Terraform DevFeed: [Automate Docker Registry Creation with Harness and Terraform]() Original publisher: [Read original article]() Author: Shibam Dhar Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [docker registry](), [Kubernetes](), [Provisioning](), [Terraform](), [Infrastructure as code](), [CI/CD](), [Dockerfile]() Tags: [ci-cd](), [container-image](), [deployment](), [docker-registry](), [infrastructure-as-code](), [kubernetes](), [registry](), [terraform]() ### AI overview A tutorial shows how to use Harness Pipelines and the Harness Terraform Provider to provision a uniquely named Docker Registry, build and push a container image, and deploy it to Kubernetes. It covers Terraform execution in a CI step, passing output variables between stages, and rolling updates with automatic rollback. ### Source excerpt Provision a fresh Docker Registry with Terraform, build your container image into it, and deploy to Kubernetes in one | Blog ## Operationalizing least privilege: Automate IAM remediation through your CI/CD pipeline DevFeed: [Operationalizing least privilege: Automate IAM remediation through your CI/CD pipeline]() Original publisher: [Read original article]() Author: Luis Pastor Published: 2026-09-15T15:53:51Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AWS Identity and Access Management (IAM)](), [CI/CD](), [Infrastructure as code](), [AWS CloudFormation](), [Amazon Web Services]() Tags: [aws](), [aws-cloudformation](), [aws-cloudtrail](), [aws-iam](), [aws-identity-and-access-management-iam](), [aws-organizations](), [best-practices](), [ci-cd](), [expert-400](), [iam](), [infrastructure-as-code-iac](), [least-privilege](), [pull-request](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article describes an automated workflow for operationalizing least privilege in AWS. It explains how AWS IAM Access Analyzer identifies unused permissions and how remediation can be routed through different paths for infrastructure-as-code-managed, manually created, and unused IAM principals. ### Source excerpt The principle of least privilege is straightforward to articulate but challenging to maintain at scale. When teams first deploy applications to AWS, they often grant broader permissions than strictly necessary; it's faster to get things working, and the plan is always to tighten permissions later. But later rarely comes. Permissions accumulate, AWS Identity and Access [...] ## Honoring #IconsOfQuality: Gaurav Singh DevFeed: [Honoring #IconsOfQuality: Gaurav Singh]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-15T11:29:38Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Test automation](), [Microsoft](), [Microsoft Teams](), [CI/CD](), [Meta](), [Artificial Intelligence]() Tags: [ai-agents](), [ci-cd](), [icons-of-quality](), [microsoft](), [microsoft-teams](), [quality](), [software-testing](), [test-automation]() ### AI overview BrowserStack profiles Gaurav Singh, a Senior Software Engineer at Microsoft, as an Icon of Quality. The article highlights his work on scalable engineering systems, testing infrastructure, CI/CD pipelines, developer productivity, mentorship, and the role of determinism and feedback loops in automated testing. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## How to Implement Feature Flags for Safe and Gradual Rollouts DevFeed: [How to Implement Feature Flags for Safe and Gradual Rollouts]() Original publisher: [Read original article]() Author: Abisoye Alli-Balogun Published: 2026-09-14T14:58:55Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [feature flags](), [CI/CD](), [Deployment](), [Development](), [Tech Debt](), [Testing]() Tags: [architecture](), [best-practices](), [ci-cd](), [deployment](), [development](), [experimentation](), [feature-flags](), [how-to](), [tech-debt](), [testing]() ### AI overview This article explains how to implement feature flags for safe, gradual rollouts. It covers separating deployment from release, boolean toggles, percentage-based rollouts, user segmentation, experimentation, kill switches, lifecycle management, and common anti-patterns. ### Source excerpt Feature flags are one of the most powerful tools in a team's deployment arsenal. They decouple deployment from release, meaning your CI/CD pipeline can push code to production servers on every merge, ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## How to Evaluate Live & Voice Agents in ADK DevFeed: [How to Evaluate Live & Voice Agents in ADK]() Original publisher: [Read original article]() Author: Stephen Allen Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [AI Bots](), [CI/CD]() Tags: [agents](), [audio](), [ci-cd](), [cli](), [evaluation](), [gemini](), [how-to](), [json](), [llm](), [production](), [testing](), [tool](), [tools](), [transcripts](), [voice](), [workflows]() ### AI overview The article explains how to evaluate live voice agents in ADK with simulated audio conversations, automated scoring, and recorded results. It covers scenario-based and fixed-conversation test cases, multi-agent workflows, and running evaluations in CI/CD. ### Source excerpt Moving live voice agents from demo to production requires rigorous, automated testing to handle the unpredictability of real multi-turn conversations. ADK now provides native live evaluation, allowing developers to test graph-based agent workflows against LLM-driven simulated users that generate actual audio via Gemini TTS. By defining evaluation scenarios and natural-language rubrics, you can automatically score audio responses and tool executions, inspect the resulting transcripts in ADK Web, or run the CLI directly in your CI/CD pipeline. ## OpenTelemetry proposes environment variables for context propagation across processes DevFeed: [OpenTelemetry proposes environment variables for context propagation across processes]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-11T11:01:22Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [context](), [tracing](), [OpenTelemetry](), [Processes](), [CI/CD](), [data-processing]() Tags: [ci-cd](), [data-processing](), [environment-variables](), [opentelemetry](), [processes](), [spans](), [tracing](), [w3c]() ### AI overview The OpenTelemetry Specification has a release candidate for using environment variables to carry trace context and baggage between processes. The article explains how this can connect spans across workflow runners, shells, build tools, test processes, and similar workloads when protocol headers or message metadata are unavailable, and requests feedback before the specification becomes Stable. ### Source excerpt A trace does not always cross a network boundary. A workflow runner starts a shell, the shell launches a build tool, and the build tool starts test processes. Batch and data-processing systems create similar chains of child processes. Without a shared way to pass trace information across these boundaries, spans from each process can end up in separate traces. If context propagation is new to you, it is the mechanism that carries information from one service or process to the next. For tracing, this includes the trace and span identifiers that let new spans join the same trace. It can also carry baggage: application-defined key-value pairs that are passed to downstream work. ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## Stop AI code sprawl before it destroys your software design DevFeed: [Stop AI code sprawl before it destroys your software design]() Original publisher: [Read original article]() Author: Emmanuel Akita Published: 2026-09-10T12:30:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [CI/CD](), [ci]() Tags: [ai](), [ai-coding](), [ai-engineering](), [andela](), [architecture](), [ci-cd](), [post-contributed](), [python](), [sponsor-andela](), [sponsored-post-contributed](), [testing]() ### AI overview The article argues that AI-generated code can create "Comprehension Debt" by silently violating architectural boundaries. It advocates enforcing architecture through automated tests and CI/CD pipelines rather than relying on documentation and manual review. ### Source excerpt While AI code generators help teams ship faster than ever, that speed brings a hidden killer: Comprehension Debt. As soon The post Stop AI code sprawl before it destroys your software design appeared first on The New Stack. ## 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 ## Automated Worker Versioning with GitHub Actions DevFeed: [Automated Worker Versioning with GitHub Actions]() Original publisher: [Read original article]() Author: Adam Quan Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [GitHub Actions](), [CI/CD](), [Deployment](), [Orchestration]() Tags: [automation](), [ci-cd](), [ci-cd-pipeline](), [deployment](), [github-actions](), [how-to](), [temporal-voices]() ### AI overview This tutorial explains how to automate Temporal Worker Versioning in a CI/CD pipeline with GitHub Actions and Worker Controller. It covers deterministic Workflow code changes, deployment versions, traffic ramping, pinned and auto-upgrade Workflows, safer rollouts, and rollbacks. ### Source excerpt Learn how to automate Temporal Worker Versioning in CI/CD with GitHub Actions and Worker Controller for safer rollouts, traffic ramping, and rollbacks. ## Harness CLI: AI Code Reviews in the Terminal DevFeed: [Harness CLI: AI Code Reviews in the Terminal]() Original publisher: [Read original article]() Author: Mohit Suman Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Command-line interface](), [ai-coding](), [AI-assisted coding](), [Pull Request](), [CI/CD]() Tags: [agent](), [ai](), [ci-cd](), [cli](), [code-reviews](), [github]() ### AI overview This Harness article introduces Harness CLI 3.0, a terminal interface that unifies Harness Code, CI/CD pipelines, AI Code Reviews, and interactive terminal workflows. It describes a predictable grammar spanning more than 320 commands and 95 nouns, along with JSON output, self-describing schemas, agent detection, and HARNESS_API_KEY authentication for autonomous workflows. ### Source excerpt | Blog ## Deployment step now 10% faster DevFeed: [Deployment step now 10% faster]() Original publisher: [Read original article]() Author: Javi Velasco Published: 2026-09-08T22:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [CI/CD]() Tags: [manifest](), [routing]() ### AI overview Vercel made its deployment step about 10% faster by combining per-function routing metadata into one manifest upload. The improvement applies automatically to all builds. ### Source excerpt The deployment step is now about 10% faster, saving one second on average. Large applications can save up to 12 seconds. Previously, Vercel uploaded a separate routing metadata file for each function path. That metadata is now combined into a single manifest and uploaded once. This improvement applies automatically to all builds. No changes are required. Learn more about builds in the documentation. Read more ## Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2 DevFeed: [Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2]() Original publisher: [Read original article]() Author: Melanie Li Published: 2026-09-08T17:03:50Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [AWS IAM](), [CI/CD](), [Command-line interface]() Tags: [amazon-sagemaker-ai](), [announcements](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [intermediate-200](), [model](), [sync]() ### AI overview This article describes cross-account model-governance patterns for managed MLflow and Amazon SageMaker AI Model Registry sync. It covers hub-and-spoke and hybrid topologies, IAM-based roles, and CI/CD deployment of approved models. ### Source excerpt Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated. [Next page]()