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Deploying Large Language Model (LLM) applications can transform ideas into valuable services. But, deployment challenges can slow down even experienced developers. In this tutorial, you will build and deploy a LangChain application using LangServe and CircleCI on Google Cloud Run. You will create a text summarization tool powered by Google’s Gemini model. You will use Langserve to expose it as an API. You will automate testing and deployment to Google Cloud Run using CircleCI.

Test failures are inevitable. A broken condition, a missed edge case, or a last-minute refactor can trip up even the most careful changes. That’s part of shipping software. What shouldn’t be part of the job is spending half your afternoon parsing logs and chasing down the root cause. Now, there’s a faster way. This guide shows how to use the CircleCI MCP server to identify, understand, and resolve failing tests in a CI/CD build without ever leaving your editor.

Monolithic and microservices architectures represent two fundamentally different approaches to software design. By understanding the benefits and drawbacks of each architectural style, developers can make informed decisions about which approach best fits their application needs. While monolithic architecture bundles all application functionality into a single deployable unit, microservices architecture breaks the application into smaller, independently deployable services.

We recently announced the new Codefresh GitOps Cloud, the easiest way to promote changes across Argo CD applications–even across different clusters. With Codefresh GitOps Cloud, you can model your own promotion flow with a graphical editor (although YAML is still available). You define exactly how an application reaches production, including all the requirements and approval gates your organization needs. With Codefresh, environment information gets modelled in the platform itself.

Enterprise Policy Management (EPM) is a programmable policy-as-code layer that controls the security, compliance, and flow of artifacts across the software supply chain. Teams can codify rules once and apply them continuously across repositories. With Cloudsmith’s platform, organizations extend policy enforcement across teams, environments, and geographies without introducing friction, including the open source packages that the chain depends on.

Cloudsmith built Enterprise Policy Management (EPM) on Open Policy Agent (OPA) and uses Rego to define policies as code. These policies control how packages move through your systems. They're versioned, reviewable, and enforceable. EPM is in early release, but it already draws on extensive metadata Cloudsmith collects from your artifacts: format, version, tags, license, vulnerability, malware scan results, and digital signatures.

Implementing robust data governance in microservices architectures presents unique challenges and opportunities. As organizations decompose monolithic applications into distributed services, traditional centralized data management approaches no longer suffice. Each microservice may manage its own data store, creating potential inconsistencies, compliance risks, and security challenges.

Moving from monolithic applications to microservices represents a significant architectural transformation. The Strangler Pattern offers a controlled, incremental approach to this migration, enabling organizations to gradually replace functionality while keeping systems operational throughout the transition. This methodology substantially reduces risk compared to complete rewrites, making it an invaluable strategy for organizations with business-critical applications.

Microservices migrations represent significant investments for organizations seeking greater agility, scalability, and development velocity. Yet without clear metrics to guide the journey and measure outcomes, these initiatives risk delivering technical change without meaningful business impact. Establishing appropriate success measures ensures that migration efforts stay aligned with organizational goals while providing visibility into progress and value delivery.

Software teams rely on CI/CD pipelines to build, test, and deploy code quickly. But when a build fails, it can disrupt the entire workflow. Digging through logs, chasing down errors, and switching between dashboards takes time you don’t want to waste. In this tutorial, you’ll learn how to use your AI coding assistant — powered by structured data from your CI system — to diagnose and fix build failures faster.