Systems | Development | Analytics | API | Testing

Note: The SmartBear MCP Server is under active development and features may change. Check our GitHub repository for the latest updates and compatibility information. At SmartBear, we show how our MCP Server enables a secure and intelligent bridge between SmartBear platform data and AI-powered development workflows.

Human Resources is rich in data but often struggles to make it accessible and actionable. As a profession we also live in the ‘grey’ – of course recognizing that People are by very definition different, unique and often require individual approach.

I recently tried using AI to create a digital twin of myself. I uploaded a photo, expecting a futuristic, slightly improved version of me… and what did I get in return? A picture of Kim Jong Un. Clearly, AI has a sense of humor—or a very different definition of “twin.” Forget Arnold Schwarzenegger and Danny DeVito.

GPU underutilization costs enterprises millions annually, with expensive accelerators frequently running single workloads at a fraction of their capacity. According to ClearML’s 2025-2026 State of AI Infrastructure at Scale report, almost half (49.2%) of IT leaders at F1000 companies identified maximizing GPU efficiency across existing hardware, including shared compute and fractional GPUs, as their top priority for expanding AI infrastructure over the next 12-18 months.

TL;DR: We created Remote Execution and Computer Use APIs for macOS, packaged them as a MCP server, and now your AI assistant can spin up Virtual Machines (VMs), run shell commands, transfer files, and even click around the Graphic User Interface (GUI). It's a prototype. It's a bit wild. We'd love to know what you'd build with it.

Refresh the page, lose signal, switch tabs - the AI conversation just keeps going. That’s what reliable, resumable token streaming makes possible. No restarts, no lost context, just the same response picking up right where it left off. It keeps users in flow and builds trust, making conversations feel seamless. Even better, it unlocks things like switching devices mid-stream without missing a beat.

The biggest trend in software quality? It’s not AI but the organizational failure to recognize testing as a value driver.

AI’s impact on quality engineering has been widely discussed, with some predicting a crisis for software testers. The more dire forecasts have narrowed in on the junior tester, as some anticipate that AI’s ability to perform routine tasks will eliminate entry-level roles. As Tricentis has explored, AI will not replace junior testers but will rather remake their jobs, enabling them to engage in strategic work earlier in their careers. But what about the senior tester?

The rise of intelligent agents and AI prompts is transforming APIs from mere infrastructure into the capability ecosystem of the agentic era, driving a new model of ecosystem-led growth. To prepare for the inevitable explosion of agent-driven API traffic, enterprises are prioritizing a strategic approach to API governance, developer experience, and scalable infrastructure.

Companies are rapidly adopting AI, but it's not all roses. The excitement comes with significant risks, such as shadow AI, runaway costs, and security nightmares. This post explores the real challenges organizations face in AI governance today and highlights how forward-thinking companies are beginning to tackle them. Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents.