Systems | Development | Analytics | API | Testing

How we utilized all of our customer data to identify churn trends and prevent future loss.

Perforce Delphix vs. K2View — which one is better for your data management and compliance needs? Each provider has strengths and weaknesses, so it’s important that you find the right one that checks your boxes, prioritizes your top needs, and fits your use cases. In this blog, we’ll detail compare Delphix vs. K2view, including their key differences, use cases, integrations, and Delphix customer testimonies.

Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant that understands repository context, proposes changes before applying them, and generates test assets across Playwright, Selenium, and API testing workflows.

AI agents do not just respond. They interpret goals, use tools, follow workflows, apply context, and sometimes change real systems. That means they need a different kind of testing.

Security vulnerabilities in production environments rarely arrive one at a time. Recently, one of our enterprise Node.js support customers identified a collection of security advisories affecting their Node.js infrastructure. The affected environments were running Node.js v20 and v22 and included vulnerabilities not only within runtime-adjacent tooling but also in components distributed alongside Node.js deployments.

Enterprises are buying AI infrastructure faster than their platform teams can operationalize it. Dell and ClearML are working together to close that gap, giving enterprises a faster, simpler path from Dell AI Factory hardware to a production-grade AI platform. Dell carries the hardware. ClearML provides the AI infrastructure layer on top. Together, the two give platform teams a way to deliver AI as a service to their organization without a multi-year integration project.

This has been the reality of clinical decision-making for years: healthcare reacts after the signal becomes visible. Traditional clinical decision support systems helped standardize care and reduce errors, but most systems relied on static rules and issued alerts only after an event had occurred. They identify danger when it is already happening, not when it is quietly forming underneath the surface. That delay is expensive clinically, operationally, and financially.