Operations | Monitoring | ITSM | DevOps | Cloud

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.

Recently we introduced support for Failure Strategies, which allows developers to implement more powerful logic and control flow into their workflows. Today, we are excited to announce a new step failure strategy, retry, which can auto-retry your failed steps without requiring any input from the user.

Breaches in software artifact integrity can have severe consequences. Bad actors poison artifacts by injecting malicious code into software packages, libraries, or container images, tricking developers and users into downloading compromised artifacts. These attacks can lead to data breaches, system takeovers, and widespread supply chain disruptions. Continued artifact poisoning incidents highlight the increasing risk to software supply chains.

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.