Systems | Development | Analytics | API | Testing

I spend a lot of time with customers and partners, and the pattern is consistent. Everyone wants the benefits of AI, faster decisions, more automation, better productivity. But the thing that slows them down is not the model. It’s the data underneath it. Not just any data, but trusted data to drive trustworthy business outcomes. As soon as you move from AI that explains to AI that influences workflows, ambiguity stops being an inconvenience. It becomes a liability.

OpenClaw adoption is accelerating fast, and so are the security incidents. Within two weeks of broad adoption, over 42,000 gateway instances were found exposed to the public internet with no authentication. Nearly all of them had authentication bypasses. Eight were completely open with full shell access. Meanwhile, 341 malicious skills were confirmed on ClawHub, and infostealers like RedLine and Lumma are already targeting OpenClaw installations to harvest API keys.

High-Performance Computing (HPC) has traditionally been an exclusive club. If you wanted to run massive simulations or crunch petabytes of data, you had to leverage the predominant languages used on supercomputing hardware—usually C, C++, or Fortran. Although fast and efficient, these languages demand strict memory management and complex syntax that require strong software development skills. Without them, development time can slow down significantly. But the landscape is shifting.

Provisioning data for software testing requires balancing realism against security. Teams need production-like data to validate applications effectively. But they also have to adhere to strict privacy regulations. Two of the leading methods for creating and securing test data are data masking and synthetic data generation. Data masking de-identifies sensitive production data, preserving its scale, realism and referential integrity.

Your service doesn’t crash. It just gets slower. Latency creeps up. Requests that used to take 20ms now take 120ms. p99 drifts. Throughput drops slightly. Nothing is obviously broken — but the system feels congested. You open your dashboards. And yet, something is clearly off. In many production systems, this is what Event Loop pressure looks like. Not a failure. Not an outage. But a runtime that is struggling to make forward progress. The JavaScript thread is not dead. It’s busy.

As AI becomes more powerful and easier to use, more and more software will be built and consumed on the fly.

I've lost count of how many times I've heard some version of this story: A development team needs to spin up a new environment for testing, but the request often sits in a queue for days — sometimes weeks — while infrastructure teams wrestle with storage constraints and provisioning bottlenecks. By the time the environment is ready, priorities have shifted, sprint deadlines have been missed, and the team that requested it is already firefighting the next production issue. The kicker?

Just a month into the year, and a few weeks since the launch of Qlik Trends 2026, we’ve already seen just how fast the AI landscape can evolve. The emergence of Claude Cowork and Moltbook reflect the two ends of the spectrum when it comes to agent collaboration. After taking a breath to digest Dan Sommer’s fascinating webinar – check it out if you haven’t already – I’ve been reflecting on which trends are set to make the most impact this year.

In the traditional banking world, “Quality” was defined by the perimeter. If the core banking system was stable and the customer portal didn’t crash, QA had done its job. We operated in a world of controlled environments. We owned the code, the server and the user experience. Then came Open Banking. Suddenly, the perimeter has vanished. Today, a bank’s value is determined by how well it communicates with external fintechs, payment aggregators and retail ecosystems.

The volume of data businesses process is surging exponentially, while budgets for human capital remain constrained. For many CTOs and Data Leaders, a default response to escalating data demands can be an accelerated hiring cycle; get more people. Yet, relying on recruitment to solve challenges around scaling analytics is no longer easily feasible; it can be a significant bottleneck.