Systems | Development | Analytics | API | Testing

Thinking about learning Kubernetes in 2026? You’ll need GitOps, kubectl, and CI/CD pipelines... OR you can just use Speedscale. See how a single operator replaces a million dependencies and gives you the traffic insights you actually need to survive production.

Is Kubernetes actually hard, or are we just using the wrong tools? In 2026, the Kubernetes ecosystem has become a "dependency jungle." Between GitOps, YAML configuration, kubectl mastery, and complex CI/CD pipelines, developers are spending more time managing infrastructure than writing code. In this video, Ken breaks down the "hard parts" of K8s and introduces a more efficient workflow using Speedscale. Learn how to gain instant visibility into your cluster, pull logs without the headache, and turn real-world traffic into actionable load tests.

See Sauce AI for Insights in action and discover how you can eliminate hours of manual work, accelerate release cycles, and ship with confidence. This on-demand demo walks you through our suite of purpose-built AI Agents that transform complex test data and workflows into instant, actionable intelligence. In this video, you will see how Sauce AI.

Many customers were struggling with BDD-style test processes — writing structured, English-language scripts that business and tech teams could agree on but couldn’t easily automate. Before Katalon, they lacked a smooth way to turn those scripts or raw requirements into automated tests, which Katalon (and AI features like Studio Assist) finally made efficient. — Coty Rosenblath, Chief Technology Officer at Katalon.

In this fireside chat with Jaydeep, we’ll dive into how AI is changing the way we measure success in both QA processes and live generative AI bots. On the QA side, we’ll look at cycle time reduction—the “time goalie” metric that shows how quickly we move from discovering a bug to fixing it. We’ll also talk about predictive quality accuracy, which shifts QA from being reactive to proactive by predicting which code changes are most likely to introduce bugs. And of course, we’ll touch on test creation velocity—how much faster teams are able to create meaningful automation with AI’s support.

Model Context Protocol (MCP) lets AI agents use real tools safely. In this talk, we’ll see how Playwright MCP bridges agents and a real browser by leveraging the accessibility tree (not screenshots) to navigate pages, locate elements, perform actions, and extract data—then export stable Playwright tests you can commit.