Operations | Monitoring | ITSM | DevOps | Cloud

When a page pauses for even a quarter-second users feel it, and many will tab away before the spinner stops. Front-end performance testing lets us spot those delays on our own machines instead of reading about them in support tickets. The browser runs JavaScript, layout, painting, and every user interaction on a single main thread. If one task takes too long, everything else queues up behind it.

The 2025 Market Guide for Digital Adoption Platforms (DAPs) marks an important point in the evolution of the category. Digital adoption has matured from a supporting role into a central part of enterprise strategy. Organizations are no longer asking if they need a DAP—they’re asking which one. In this latest research, Gartner establishes DAPs as essential to business transformation, efficiency, and employee experience. The takeaway is clear: digital adoption is no longer optional.

Tired of complex cloud setups? Explore the top 10 Fargate alternatives and discover how Qovery can simplify your container deployment, saving you time and money.

The popular narrative around AI economics is changing. At one time, Moore’s Law conditioned us to expect that smarter, faster computing would steadily get cheaper. When it comes to AI, that expectation holds true at the unit level. Per-token costs are indeed declining. But the number of tokens consumed per task is growing exponentially, making total costs spike. The tension here is important: on paper, inference is getting cheaper.

Are you tired of digging through cryptic logs to understand your Kubernetes network? In today’s fast-paced cloud environments, clear, real-time visibility isn’t a luxury, it’s a necessity. Traditional logging and metrics often fall short, leaving you without the context needed to troubleshoot effectively. That’s precisely what Calico Whisker’s recent launch (with Calico v3.30) aims to solve. This tool provides clarity where logs alone fall short.

Deploying a Node.js application may feel straightforward at first. Everything checks out in tests, staging runs smoothly, and early users run into no problems. But as real traffic ramps up, hidden problems start to appear in unexpected ways. Requests fail intermittently, latency spikes without warning, memory usage climbs silently, and logs are scattered across multiple processes making it nearly impossible to trace the root cause.

In my travels, I constantly hear about plans that promise to “unlock the full power of AI” down the road. The usual advice is to start small with a few pilots, then gradually scale up from there. It looks good on paper, but in practice, it becomes a months-long slog of one-off experiments that burn a lot of capital, but usually generate little impact on their own.

Performance testing is critical to build reliable applications, but testing at scale, especially inside modern Kubernetes environments, can be a challenge. For example, how do you coordinate tests across multiple nodes, test private services without compromising security, or even do both at once? And most importantly, how do you do all this without adding too much operational complexity to your stack?