Systems | Development | Analytics | API | Testing

We are noticing that some of our requests are starting to get slow and server load increases. Checking the process list of our server, for example with htop reveals that our FPM workers are taking up all of our CPU time. Checking the health with our basic toolset of lsof to show open network connections and strace to show syscalls does not reveal any activity. This means that the workers are spending time processing data without any externally visible activity.

During London Tech Week, our CEO, Mike Capone, joined CNBC to talk about the UK’s position in the global AI landscape, and why this is a moment the country should not waste. His message was simple. The UK already has many of the ingredients for leadership in AI. The question now is how fast it can move from innovation to scale.

In 2025, as AI models increasingly generate, refactor, and deploy code on their own, developers face a new challenge: how to safely run code they didn’t write. Sandboxes have become the backbone of this new workflow because they are lightweight, secure environments that let teams test, validate, and monitor code without risking production systems.

AI is evolving faster than the guardrails meant to validate it, leaving organizations exposed to compliance risk, model drift, opaque decision paths, and breakdowns in trust.

Data integration logging is the process of recording events and activities during data movement across systems, ensuring accuracy, troubleshooting, and regulatory compliance. In complex environments like those of Netflix and Airbnb, logging is critical for system reliability and performance. However, challenges such as managing large log volumes, inconsistent formats, and compliance requirements make effective logging difficult.

Why do some QA teams consistently deliver reliable and high-quality software, while others toil to identify bugs and experience unstable releases? The real difference often is related to how easily the team is able to use software testing metrics to make measurable decisions. Often, the testing process turns out to be a routine checklist activity – run the tests, publish the results, and move on. However, without useful test metrics, the QA teams simply keep guessing.

Getting the right answer to a technical question, right when you need it, can be the difference between a minor speedbump and a major roadblock. We all know the feeling of being stuck on a specific error or needing a quick "how-to" guide to keep a project moving. That's why we’re excited to announce that the new 24/7 Katalon Support Assistant is now live in production! It's an AI-powered technical support agent and the fastest way to get the help you need, anytime.

Insurance companies face unique challenges in delivering reliable software quickly. Policy updates, claims processing, and regulatory compliance all demand precision and speed. Manual testing alone can create delays, introduce errors, and increase operational risk. That's where test automation for insurance industry comes in. Repetitive regression tests that previously take up so much time can easily be automated.

Production errors are every developer's nightmare. You're enjoying your coffee when suddenly alerts start firing - users are experiencing crashes, and you need to find and fix the issue fast. Today, we'll walk you through how to use AI to diagnose and fix critical errors in an application using Rollbar's MCP (Model Context Protocol) server.

Procurement applications sit at the heart of government operations. These systems are prime targets for cyber attacks because they manage critical, high-value data, including: They also connect with core enterprise systems (e.g., ERP, HR, financial management), creating additional risk points. In the wrong hands, this data could allow adversaries to piece together strategic mission capabilities. In this environment, security isn’t optional—it’s foundational.