Systems | Development | Analytics | API | Testing

API tests are faster and more reliable than UI tests. So why aren’t more testers using them? In this session, we’ll learn what kinds of API tests to run, and how to easily configure them in Cypress. Your API tests can live alongside your UI tests, providing valuable fast feedback for the quality of your application!

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.

Check out what’s new in ThoughtSpot’s latest release!

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.

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.

AI in automation is rapidly emerging as a powerful enabler for testers. From automation script generation utilities to simplifying API testing and framework development, these capabilities promise to accelerate productivity. But as testers embrace assistants like Copilot and Cursor, big questions emerge - What does this mean to the future of open-source frameworks like Selenium and Playwright? How should testers balance the speed of AI-generated code with the need for reliability and maintainability? ⁠How do we measure productivity gains from AI-paired programming? And AI doesn't stop at coding.