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Read more: https://speedscale.com/blog/imaginary-test-data-real-token-bill/

"MCP burns too many tokens" is old news. Lazy MCP tool loading and page snapshots on disk fixed the token problem. We benchmarked Playwright MCP vs the CLI, and they're roughly the same. Pick your tools based on workflow, not token anxiety.

Learn more: speedscale.com.

Learn more at speedscale.com.

Since Playwright 1.59, the new "trace" command makes your test traces fully accessible in the terminal. All plain text, and your agent will love it. And if you're running Playwright with Checkly, our AI agent, Rocky, already reads your traces and delivers root-cause analysis before you even start digging.

Learn how to get instant observability into your Kubernetes cluster by installing the Speedscale operator and proxymock tool. In this step-by-step tutorial, we walk you through setting up the operator to capture live network traffic (including encrypted traffic, API calls, and database calls) without complex instrumentation or manual configuration.

Instead of waiting for an alert to trigger a long, stressful triage and replication process, catch the issue before it reaches production by replaying real traffic against your code first.

An AI agent that can't open a browser is just guessing. Stefan from Checkly shows how giving AI coding agents a real browser via Playwright enables reliable end-to-end test generation and debugging, closing the quality gap created by faster, AI-driven shipping. The session compares Playwright MCP vs the Playwright CLI for agent workflows, showing that thanks to MCP spec changes, lazy tool loading, and skills, the two are now effectively just different interfaces to the same tool, with no real token advantage either way.