Systems | Development | Analytics | API | Testing

API managers are often used to expose REST APIs to an internal or external group of developers. An internal group of enterprise developers might use a catalog of API services as a starting point for software development projects. An external group of partners might use the APIs for integration with their backend systems. In some cases, partners might use the APIs to purchase goods, services, or information.

Play Quality People - Huy Tieu: The Evolution of Agentic QA: From scripts to Harnesses 17: 18 A conversation with Huy Tieu, Senior Product Manager at Katalon, on why the scripted testing model broke, what replaces it, and the one experiment every QA leader should run this week. The term "Agentic QA" is everywhere in 2026. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year's end, up from under 5% in 2025.

Resource governance is primarily an operational discipline, but it has direct security implications that are usually overlooked. This post covers what those implications are, what Kubernetes provides natively, where it falls short for AI workloads, and how ClearML addresses both dimensions. This is the third post in our four-part series on Kubernetes Security for Enterprise AI Environments.

Five diagnostic patterns. One decision tree. A senior practitioner's triage playbook for Playwright flakiness in 2026. Flakiness is architectural, not framework-borne: Almost every flake traces back to async state, locator drift, session pollution, environment variance, or AI-agent non-determinism — not to Playwright itself.

Earlier this year we started asking a simple question to finance leaders we met at events, on calls, and in roundtables: where do you actually start with AI in finance? The answers were consistent enough that we decided to do something about it. We invited 30 CFOs, finance directors, and finance managers to a business breakfast at our Prague office. A morning with peers who are all trying to answer the same question: where do I actually start?

Python is a robust and powerful programming language. In addition to machine learning, Python can be used for tasks such as web scraping, image processing, scientific computing, and much more. A framework such as Django, which is built on top of Python, enables you to build beautiful web applications—top websites such as Dropbox, Instagram, and YouTube use Django.

Enterprises are pouring millions into agentic AI. New findings from Fivetran's “Agentic AI readiness index 2026” reveal why most won’t see the return.

Conversational analytics leveraging Fivetran, Jira, and Claude saves analytical effort and boosts productivity.

In my experience working with enterprise leaders, the journey to the cloud rarely follows a straight line. Many organizations set ambitious goals to move all operations to the cloud. They quickly find that certain legacy systems must remain on-premises. This reality results in a complex, hybrid multicloud environment. That means they need to adopt a new strategy for managing test data.

PHP applications are often tricky to debug. A combination of loose typing, complex logic and a lack of runtime visibility can make it hard to catch errors before they reach our users. But if you’re using PHP, there’s no need to stress. This guide will equip you to understand why PHP applications break, return the wrong data or behave differently across environments. We’ll cover logs, runtime checks, Xdebug, IDE tools, request debugging, and production visibility.