About
Pixel-by-pixel UI testing is too brittle for modern release cycles, yet LLMs introduce a dangerous lack of determinism. In this practical roundtable, you will learn how to engineer a visual testing strategy that combines the flexibility of AI with the strict repeatability required for continuous deployment.

Join QA veterans John Howland, Jack Lukomski, and Taylor Fouts as they strip away the AI hype and look at real-world architectures that mimic human vision without sacrificing test stability.

What we’ll dissect:

  • The Math Behind the Maintenance: Why pixel comparisons fail and how to drastically reduce the "self-healing" script burden.

  • The Non-Deterministic AI Trap: Where LLMs break down in regression pipelines and how to enforce predictable outcomes.

  • Hybrid Automation Architecture: How to successfully merge image-based and object-based testing into a single framework.

  • Live Architectural Walkthrough: A first look at the algorithmic approach Qt is using to bridge the visual testing gap (and how to apply these concepts to your own stack).

This is a conversation, not a presentation. Bring your questions for a live Q&A with the panel.
When
Thursday, July 16, 2026 · 2:00 p.m. Eastern Time (US & Canada) (GMT -4:00)
Presenters
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John Howland
Senior Solutions Engineer
John is an experienced Software Engineer specializing in building and scaling complex software systems using Python and C/C++. Passionate about leveraging Machine Learning and AI, he has experience applying these technologies for task automation and enhancing software development workflows. He initiated and led the development of an AI-powered product for UI/UX analysis using Python and ML frameworks. He’s currently applying his development and AI skills as a Senior Solutions Engineer at Qt Group, helping clients enhance their software development processes.
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Jack Lukomski
Software Engineer
Jack is a Software Engineer at Qt Group specializing in computer vision and deep learning for production systems. He built the visual inspection technology at the core of Squish’s new image-based testing capabilities, bridging the gap between research-grade computer vision models and the reliability demands of real-world test automation.
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Taylor Fouts
Principal Solutions Engineer
Taylor is a Principal Solutions Engineer at Qt Group that works with enterprise engineering teams across the Qt portfolio and has built MCP integrations and AI skills spanning the Qt software development lifecycle. He brings a practitioner’s perspective on what AI tooling actually looks like in production workflows.
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