Hannah Lee covers product design, user experience, and the impact of AI on interfaces. Her background is in human–computer interaction, followed by several years as a product designer in both consumer and B2B companies. She watched AI move from an invisible backend feature to a visible, central layer in user-facing tools. Her central question: how do you design AI products that are neither magical black boxes nor unusable toys, but predictable, controllable instruments?

Hannah analyzes emerging UX patterns for copilots, autonomous agents, conversational assistants, and "augmented" dashboards. She focuses on feedback loops, communicating model uncertainty, preventing algorithmic dark patterns, and drawing clear boundaries between human and machine responsibilities. She carefully documents interface failures that turn powerful models into confusing or dangerous products.

She also interviews PMs, designers, and research engineers to understand how teams choose between flexibility and control, automation and explainability. At AI-Telegraph, Hannah runs the "Product & UX" section, aimed at build teams responsible for turning generic models into tools people will actually rely on. Her core thesis: a serious AI product starts with interaction design, not temperature parameters.