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We reframe user interviews as assumption-killing tools that reduce risk and reveal real behavior. We walk through desirability, viability, and feasibility, then show how to design hypotheses, ask better questions, avoid bias, and synthesize patterns that matter.

• reframing interviews from validation to risk reduction
• the big three risks desirability, viability, feasibility
• pipeline from belief to hypothesis to question
• behavior over opinions as the core signal
• four evidence types past, present workflow, emotion, decision criteria
• direct vs indirect questions and when to use each
• avoiding leading language, hypotheticals, and founder energy bias
• synthesis signals repeated pain, workarounds, delay, resistance
• designing interviews that expose gaps rather than soothe egos

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We trace the shift from passive tools to proactive AI agents and show why design now means building trust, boundaries, and clear goals. From memory controls to uncertainty cues, we map the guardrails that turn raw capability into a reliable collaborator.

• hammer versus helper framing of agents
• autonomy, adaptability, goal orientation as pillars
• risks of fluent but unreliable systems
• designer’s role as steering, brakes, and safety
• memory scope, retention limits, and a forget button
• designing for uncertainty, clarification, and escalation
• moving from prompt tweaks to goal architecture
• becoming a pilot, not a passenger

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We share a practical framework for building trustworthy AI products and apply it to Joy, an agent that plans a full concert trip. Five pillars—competence, transparency, predictability, alignment and resilience—turn hesitant users into confident delegators.

• the shift from usability to trust as the primary interface
• the origin story of the trust builder framework
• competence through clarification, confirmation and visible progress
• transparency with leading reasons, process breadcrumbs and trade-offs
• predictability via stable flows and user control
• alignment with honest upsells and labeled sponsorship
• resilience with plain-language errors, recovery paths and saved state
• a final provocation on radical honesty as competitive edge