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The pace of software development has changed. With AI coding assistants now embedded into engineering workflows, developers are building faster, shipping sooner, and writing more code than ever before. But as velocity increases, so does the complexity of keeping that code running. When builds fail, developers need answers fast. They need clarity, context, and actionable feedback right where they’re working.

Penetration testing is a type of software testing where skilled security professionals attempt to breach an organization’s systems and networks to identify vulnerabilities before malicious actors can exploit them.

The way software is written is changing fast. In the past few years, AI coding assistants and large language models (LLMs) have gone from novelty to necessity for many developers. Tools like Cursor, ChatGPT, and custom in-house models are helping teams generate boilerplate, scaffold features, and even build entire apps within minutes. It’s exciting. But it also raises the stakes. When code is written faster, it’s deployed faster.

A benchmark can be interpreted as a standard of comparison used to assess something. In everyday life, for example, when we want to buy a new cellphone and want to know which one is faster, we can see the speed test (benchmark) by measuring how fast the cellphone opens applications or runs games. From there, we can compare which cellphone is better based on the numbers produced.

As microservices architectures dominate modern application development, the ability to test, secure, and automate their deployment has become a vital skill. In this guide, you’ll learn how to: Let’s first set the stage by briefly exploring the foundational concepts of CI/CD and DevOps, which underpin the automation and agility required in development workflows.

Argo CD is a declarative continuous delivery (CD) tool for Kubernetes. Argo CD pulls Kubernetes configurations (such as manifests, Helm charts, and Kustomize overlays) from a Git repository and applies them to a Kubernetes cluster. With Argo CD, developers can automatically deploy changes to their Kubernetes environments by updating their Git repository. Argo CD continuously monitors Kubernetes deployments and ensures their state matches the configuration declared in Git.

Machine learning (ML) and artificial intelligence (AI) are often used interchangeably in tech discussions, yet they represent distinct concepts with important differences. While AI refers to the broader field of creating machines capable of intelligent behavior that mimics human capabilities, machine learning is a specific subset of AI focused on developing algorithms that allow computers to learn from and make predictions based on data.

As AI becomes increasingly central to modern software development, the ability to craft effective system prompts has emerged as a crucial skill. Whether you’re building a code generation tool, creating a chatbot, or developing AI-powered features, your success largely depends on how well you can communicate with AI models through prompts. At CircleCI, we’ve spent countless hours working with developers who are integrating AI into their applications.

Serverless computing simplifies app development by abstracting away server management. Azure Functions provides a robust platform for event-driven, on-demand code execution. In this tutorial, we’ll create and deploy a Python-based Azure Function—one that parses incoming JSON—using CircleCI. For a more granular and enable programmatic access to Azure resources, we’ll use service principal for secure authentication and the Azure CLI orb to streamline our CI/CD pipeline.

Cascading Style Sheets (CSS) is the language used by developers to apply styles to documents written in a markup language. In front-end development, enforcing consistent CSS code quality is crucial: poorly written CSS can lead to issues ranging from poor maintainability, unexpected bugs, and inconsistent designs. One effective way to ensure CSS code quality is using a linter such as Stylelint.