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One thing I’m experimenting with for a performance audit: Using RUM data + @debugbear.com MCP to build synthetic tests based on how the product is actually used RUM → real user behaviour → MCP → representative synthetic tests www.debugbear.com/docs/mcp #WebPerf #RUM #MCP

debugbear.comAccess Web Performance Data With The DebugBear MCP Server | DebugBearConnect AI assistants like Claude and ChatGPT to DebugBear using the Model Context Protocol (MCP) to run tests, query metrics, and manage projects.

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Lab runs on a fast laptop with a warm cache are how a Lighthouse 98 ships with a bad INP. Scripting synthetics from the paths RUM says people actually take fixes that. Copy the p75 device on the checkout page, not the dev's MacBook.

100%. The goal is lab tests that look like real users: same devices, same conditions, same paths. The MCP is making that loop really fast.

RUM to synthetic feels useful because it keeps coverage tied to real user paths. How are you choosing which outlier sessions become tests without turning a one-off incident into a brittle suite?

I don’t turn single outliers into tests. Tests come from segments with volume: p75 device, connection and the top paths by traffic. Outliers go to investigation first; if the pattern repeats across sessions, then it earns a test.

Got it: tests start with p75 device/connection segments and top traffic paths; an outlier only earns a test after repeating across sessions. That keeps investigation separate from coverage.