Systems | Development | Analytics | API | Testing

Selecting load testing tools is rarely a matter of picking the most feature-rich option. Instead, it’s about finding the right fit for your team’s skills, workflows, and performance goals. Open source solutions act like a Swiss Army knife: versatile, adaptable, and packed with options for those who know how to use them. Commercial platforms, by contrast, function more like a precision instrument: focused, reliable, and designed to deliver results with minimal friction.

When a build fails, the tests are the easy part. Here's how to implement AI root cause analysis so engineers stop losing hours to logs and diffs.

Sovereignty is no longer a compliance debate. It is the operating condition for running data and AI in production. 89% of organizations in our 2026 survey of 320 enterprises rate data sovereignty as very or rather important. Only 38% have governance mature enough to survive a real AI production incident. That is the gap. It sits exactly where data and AI architecture meet enterprise control, and it is the central tension of the year.

SmartBear BearQ, an agentic QA system, has won a 2026 CRN Tech Innovator Award. CRN, a brand of The Channel Company, named BearQ a winner in the Application Development and DevOps category, calling it out among the most innovative technologies shaping the IT channel this year. AI has changed how fast software gets built, but for many organizations, testing hasn’t kept pace.

Android app crashes are a nightmare for developers. But they rarely come out of the blue. Most crashes stem from structural causes like leaky memory or sloppy resource management, and most failures will have a root cause that surfaces in either logcat or the stack trace. This post will give you a true Android crash course, showing you the tell-tale signs and the tried-and-tested fixes that work across devices, territories and app utilities.

Organic social reporting collapses into vanity metrics whenever the reporter runs out of time to dive deeper, which is most of the time. Follower counts get compared because they’re comparable at a glance. Engagement rates get quoted because they sound comparable, even though each platform calculates them differently.

AI agents make thousands of decisions per day, at volumes no human-centered governance model can realistically supervise. According to IBM's 2026 Tech Leader Study of 2,000 CIOs/CTOs in 33 geographies across 19 industries: The study concludes that organizations face a trap: prioritize speed, and governance falls behind; or prioritize safety, and deployment stalls, weakening the organization’s competitive position.

Your team already knows when a campaign starts to underperform, when spend spikes, or when web traffic shifts. What you don't have is the speed to turn that insight into action. Someone still has to investigate, decide what to do, pull in the right people, and coordinate the work. That takes time. As a data-driven CMO, I've lived this every day. I can know the instant something changes in the data, but there's still a large gap between insight and action. Today, we're closing that gap.

The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI. When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption.