Systems | Development | Analytics | API | Testing

AI tip: For pull requests, let your AI agent take the first pass before your team ever sees it. That's the workflow Ilia Mogilevsky, Software Engineering Manager at SmartBear, built. By packaging prompts into a skill loaded with Git history, Jira context, and CI checks, he turned a basic AI assistant into a reviewer he trusts. The skill then generates a structured report with approval-ready fixes. Accept the changes, adjust what's off, and push – review cycles shrink and deploys move faster.

A new Google white paper suggests that as AI writes the majority of our code, the role of a software engineer is shifting from writing applications to performing rigorous validation and QA.

This video demonstrates how to use the ProxyMock web tool to record API traffic from a remote Kubernetes cluster and automatically scan it for hidden PII to ensure the data is safe for developers to use.

We demonstrate how to stream realistic test data directly from a production environment to developers and AI models without exposing any personally identifiable information (PII).

Your developers — and your AI agents — need a safe way to test against production-like behavior. Keploy records real API traffic and replays it as a digital twin sandbox, so you can catch regressions before they ship. No manual mocks. No production access. No complex test environment setup. Record → generate tests and mocks → replay in CI.