MCP Server Security Checklist: Protecting Your Test Infrastructure from Prompt Injection
A few months back, a friend on a platform engineering team told me something that stuck with me. His team had wired up an AI coding agent to a Model Context Protocol server that gave it access to their internal Jira instance, their test…
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Playwright Page Object Model with TypeScript: The Complete Guide
This guide is written for QA engineers, SDETs, and automation architects who already know the basics of Playwright and TypeScript and want to build something that survives contact with a real product team — one where the UI changes every sprint, three…
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MCP Servers for SDETs: What They Are & Why They Matter (2026)
Introduction: Why MCP Servers for SDETs Is the Conversation of 2026? If you have spent any time in a test automation Slack channel, a QA conference hallway, or a release-readiness standup in the first half of 2026, you have probably heard the phrase “MCP…
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AI-Powered Test Maintenance: Stop Fixing Broken Selectors Manually
Introduction: The 2 AM Slack Message Every QA Manager Dreads It is 2 AM. Your nightly regression suite has just finished running, and the CI dashboard is a wall of red. Forty-three tests failed. You already know, before you even open the report, that…
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From Manual QA to AI Quality Engineer: An Honest Transition Roadmap
If you have spent years running manual test cycles, writing test cases in Excel or TestRail, and reporting bugs in Jira, you have probably noticed the ground shifting under your feet. Job postings that used to say “Manual Tester” now say…
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AI Coding Assistant for Playwright: Claude vs Copilot vs Cursor: Which AI Coding Assistant Wins in 2026?
Three months ago I made a decision that saved my team approximately 12 hours per sprint: I stopped debating which AI coding assistant for Playwright was best and actually tested all three properly. Not a quick demo. Not a five-minute trial. I…
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Prompt Regression Testing 101: Catching AI Output Drift Before Production
If you have shipped anything backed by a large language model, you already know the feeling. Everything works in your demo. QA signs off. You deploy. Two weeks later, a prompt gets “improved” by a teammate, a model provider quietly updates a…
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