Systems | Development | Analytics | API | Testing

CMS-0057-F is a set of regulations introduced by the Centers for Medicare & Medicaid Services (CMS), a division of the U.S. Department of Health and Human Services (HHS). It was created to address the urgent need for better interoperability across the healthcare ecosystem in the U.S. For a deeper understanding of this regulation, we encourage you to read Enhancing Interoperability and Streamlining Prior Authorization: A Look into CMS-0057-F.

The Model Context Protocol (MCP) is an open standard designed to overcome the inherent limitations of large language model (LLM)-powered agentic applications. While these agentic applications excel at reasoning, summarizing, and content generation, they are fundamentally disconnected from the real world. They cannot access live data, interact with private systems, or execute tools on behalf of users.

Kafka delivers massive value for real-time businesses — but that value comes at a cost. As usage grows, so does complexity: more clusters, more topics, more partitions, more ACLs, more custom tooling. But it doesn’t have to be that way. If your team is managing Kafka at scale, here are five concrete steps you can take to immediately reduce cost and operational complexity — without sacrificing performance.

Today’s savviest business organizations rightfully recognize data as an essential asset. However, measuring data value across the entire data estate is challenging due to its inherent complexity. By breaking data down into smaller units that are business-outcome centric — data products — organizations can more accurately and reliably assess their value.

Tricentis has just introduced three industry-first innovations that mark a major leap forward in autonomous software testing. Whether you’re already using Tricentis solutions or exploring smarter ways to scale your testing strategy, these advancements unlock a new level of flexibility, intelligence, and productivity for enterprise software quality.

With the rapid adoption of AI-driven coding tools, software development is experiencing a seismic shift. Increasingly, developers rely on tools like GitHub Copilot and other generative AI solutions (collectively termed “vibe-coding”) that now account for roughly 30% of code output in leading organizations. This trend raises significant questions for QA teams: What happens when AI significantly contributes to the codebase, and how does this reshape the landscape of software testing?

Regression testing is indeed one of the most time-consuming part of software testing: repetitive, tedious, and requiring high volume of executions. And yet, you can't ignore regression testing. It is the guardrail preventing bugs from slipping into production. But if you don't try to reduce the time it takes to do regression testing, it becomes counter-productive, very soon.

At Confluent, we’re committed to building the world's leading data streaming platform, which gives you the ability to stream, connect, process, and govern all of your data and make it available wherever it’s needed—however it’s needed—in real time. Today, we're excited to announce the release of Confluent Platform 8.0! This release builds on Apache Kafka 4.0, reinforcing our core capabilities as a data streaming platform.

Have you ever paused to think about how fast money moves today? No paperwork, no waiting in line, just a few taps, and it’s done. For industry leaders, this raises a crucial question: Are we doing enough to keep pace with the evolving consumer expectations in the P2P payments space? Global’s peer-to-peer (P2P) payments landscape has seen an extraordinary transformation over the last few years.

What’s the difference between a software launch that builds customer confidence and one that becomes a costly disaster? Often, it comes down to how thoroughly the product was tested internally before anyone outside the company ever saw it. he irony is that alpha testing is often the most cost-effective phase for catching serious issues, yet it’s frequently the first thing cut when timelines get tight.