Systems | Development | Analytics | API | Testing

Combining MCP Servers and LLMs with MS SQL Server simplifies database management with natural language queries, automation, and robust security. Here’s what you need to know.

In the fast-paced world of mobile engineering, the ability to move quickly is often hampered by the very tools meant to help us. While traditional mobile clouds provide essential scale and coverage, they are often optimized for standard, short-lived regression scripts that follow a predictable path.

Test automation frameworks are shaping how modern teams ship software in 2026. Automation alone is not enough anymore. The structure behind automation decides whether your tests scale or collapse under maintenance pressure. I’ve seen teams struggle when they invest in automation but lack a structured system. Let’s explore how test automation frameworks can make automation reliable and scalable.

We’re excited to announce that Speedscale’s proxymock is now available as an OpenClaw skill on ClawHub, bringing powerful API traffic replay capabilities directly into your Claude workflows.

A global leader in entertainment, gaming, and hospitality partnered with WSO2 to take the organization’s first step to becoming an agentic enterprise by building an intelligent solution that would transform how support teams operate. The solution, a virtual support engineer, automated a manual issue resolution process and reduced resolution times from 2 hours to 1 minute, helping to provide a better support experience for both customers and internal operations teams alike.

Modern applications depend on multiple services, APIs, databases, and third-party systems working together. While unit tests validate individual components, most production issues occur at integration points. That’s why integration testing tools are essential for ensuring system reliability. In this guide, we cover the top 10 integration testing tools for 2026, a quick comparison to help you choose the right one, and how these tools fit into modern CI/CD and DevOps workflows.

Creating AI writing tools is messier than you’d think. You start with this grand vision of an assistant that actually helps people write better, not just spits out generic text. Then reality hits. Models hallucinate. Users have wildly different needs. And suddenly you’re facing questions about responsibility, accuracy, and whether you’re building something genuinely useful or just another gimmick.