Quern.dev is an open-source AI tool that simplifies mobile app testing for iOS and Android by automating UI interactions, debugging, and network analysis. It offers a cost-effective alternative to commercial solutions, integrating AI agents for streamlined testing and reducing manual overhead.

The EvilTester Show

The Evil Tester Show - hosted by Alan Richardson. Occasional guest episodes. Show notes have comprehensive descriptions and resource links. Released as audio and video.

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Episode Details

• Show Notes: https://www.eviltester.com/show/033-quern-jerimiah-ham/
• Published: 2026-09-04T10:06:58Z
• Duration: 01:18:57
• Author: Alan Richardson

Overview

Quern.dev is an open-source, AI-powered tool designed to streamline mobile development and testing for both iOS and Android platforms, operating as an API server with an MCP (Mobile Code Protocol) interface. It was created to address inefficiencies in mobile testing, such as manual simulator management, lack of observability, and cumbersome debugging processes that require external tools like ProxyMan or Charles. Unlike existing commercial solutions - some costing up to $700/month - Quern provides an open-source alternative that supports automation, UI interaction, log analysis, and network debugging, with a focus on integration with AI agents rather than a visual interface.

The tool enables AI-driven app testing by abstracting platform differences, allowing unified control across devices, simulators, and emulators. Key features include UI automation, screenshot and UI tree interpretation, real-time log filtering, and per-application proxy configuration using tools like MITM Proxy to avoid system-wide disruptions. A structured knowledge base using markdown files helps AI understand app navigation, screens, and constraints, reducing redundant reasoning. The system supports script generation and execution in multiple languages, facilitates deep link navigation, and explores real-time error detection using secondary monitoring agents. Future plans include enhancing the core open-source platform and potentially developing a human-friendly interface with dashboards.

What If

• What if you built a personal AI-powered mobile testing knowledge base for your app using markdown files?

  • Move: Create a .quern folder in your project with one .md file per screen, documenting UI labels, navigation paths, and conditional logic (e.g., “Subscription screen only appears after login + premium upgrade”).
  • Why Now?: Without structured context, AI resets on every session - wasting time re-explaining flows. With markdown, you reduce token use and increase AI accuracy starting today.
  • Expected Upside: Cut AI onboarding time by 70%, enable reliable test scripting, and create version-controlled documentation that grows with your app.

• What if you automated proxy setup for mobile API testing using MITM Proxy instead of commercial tools?

  • Move: Replace ProxyMan/Charles with MITM Proxy CLI; configure it to target only your simulator/emulator, not your entire system, and integrate it into your local dev script.
  • Why Now?: Commercial proxies break other apps with system-wide settings and cost $700/month at scale. MITM Proxy is free, scriptable, and isolates traffic per app.
  • Expected Upside: Eliminate proxy-related workflow interruptions, reduce costs to zero, and gain programmatic control over network inspection in automated test runs.

• What if you turned your AI’s test execution into reusable scripts via Quern.dev’s API?

  • Move: After a successful AI-driven test session (e.g., “login + post creation”), export the sequence of API calls to a Python or JavaScript script using Quern’s MCP logs.
  • Why Now?: AI sessions are non-deterministic - what works once may fail next time. Scripts lock in working flows for repeatable, fast validation.
  • Expected Upside: Shift from flaky AI-driven exploration to stable regression suites; run tests locally or in CI without relying on AI for every execution.

Takeaway

• Set up an open-source mobile testing tool like Quern.dev on macOS to automate iOS and Android testing workflows using its API and MCP interface.
• Create a structured knowledge base using markdown files to document your app’s screens, navigation flows, and UI elements, enabling AI agents to interact with your app more efficiently.
• Integrate deep links into your mobile app and use them in automated scripts to allow AI or testing tools to bypass manual navigation and reach specific states quickly.
• Implement per-application proxy tools like MITM Proxy (instead of system-wide proxies) to debug mobile app network traffic without disrupting other system connections.
• Build lightweight, version-controlled automation scripts in Python or JavaScript that interface with your local API server for repeatable testing, reducing reliance on AI for deterministic tasks.