Systems | Development | Analytics | API | Testing

Today we're releasing v26.1.0 of the Countly SDK for HarmonyOS — our first official SDK for Huawei's operating system, now available on GitHub. For most product teams outside Asia, HarmonyOS has been easy to overlook. That's no longer the case.

Automated load testing has reached a turning point in 2026. Artificial intelligence, once a gradual addition, now drives a clear shift in how organizations validate performance. Industry reports project a 15% compound annual growth rate (CAGR) for AI in software testing from 2023 to 2026, underscoring the urgency to modernize testing practices and keep up with rapid development cycles.

Here’s a problem I run into constantly: something breaks in production, I can see the 500 errors in my logs, but I can’t reproduce it locally. The trace shows me the dependency graph but not the actual request that failed. This is especially painful in microservices. I was looking at a CNCF example the other day (a simple demo app, like 4 pods) and it already had so many cross-service dependencies that understanding what broke required looking at the whole system at once.

At Assurity Consulting, our experts are deeply experienced in leveraging a variety of industry-leading Test Management tools, with Xray for Jira being a prominent example in our toolkit. Our commitment, however, extends beyond proficiency in a single platform. Our core approach is fundamentally tool-agnostic, meaning we prioritize the client’s unique context, goals, and technological ecosystem over allegiance to any specific vendor or solution.

When product teams discuss integrations in real estate software, the conversation usually stays technical: API endpoints, data normalisation, build timelines. Those questions matter — but they miss the more important one: what is this integration actually worth? Integrations with multiple listing service (MLS) feeds, property management systems (PMS), and CRM platforms are strategic product decisions.

Healthcare is sitting on a paradox. As per healthcare analytics statistics 2026 It generates more data than any other industry, nearly 30 percent of the world’s total data, yet 97 percent of hospital data still goes unused. That gap is exactly where AI and machine learning in healthcare data analytics are changing the game. We are no longer talking about dashboards or retrospective reports.

The fastest way to answer a business question used to be expensive: an analyst on staff, a clean warehouse, a dashboard for every recurring ask. Then AI tools made the fast answer free for everyone. The analytics line item, oddly, did not shrink. Speed became free. Defensibility became the line item.

BigQuery Graph allows you to build a digital twin of your entire supply chain by turning your physical world—items, recipes, and locations—into a searchable map of nodes and edges, providing a new level of clarity.

Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.

It's the day before your POC, and the embedded analytics demo still looks like it belongs to someone else. Your designer handed over a brand guide last week. Your developer has been buried in CSS variables ever since: cross-referencing token names, mapping changes across components, reloading the page after every tweak to see what broke. The UI is almost right. The nav color is close. The typography still isn't matching, but there's no time left.