Jamon brings a useful mix to this conversation: founder of Infinite Red, longtime consultant, React Native specialist, and now indie game developer. That perspective makes the episode unusually practical. He has spent years watching where projects go wrong when product thinking is weak: bad requirements, unclear stakeholder alignment, UX details nobody owned, and engineers optimizing the wrong thing too early.

The thread through the whole episode is durability. Product engineering is not just about shipping faster with agents or getting better at a specific tool. It is about understanding people, shaping better requirements, recognizing when the human side of the workflow matters more than the code, and making decisions that keep paying off as the technology changes around you.

• Sit down with a non-technical person and watch them try to use a feature you built.
• Write down every hesitation, workaround, double-click, or confusing step you notice, then use that list to reprioritize what you fix next.

• Infinite Red
• Jamon Holmgren — site
• Night Shift Agentic Workflow
• Gunship Origins on Steam

• Company: Infinite Red
• GitHub: @jamonholmgren
• 𝕏: @jamonholmgren

• Website: kentcdodds.com
• 𝕏: @kentcdodds
• GitHub: @kentcdodds
• YouTube: Kent C. Dodds

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Don's career makes this episode unusually wide-ranging: early computing, human error, aviation safety, Unix, Apple product decisions, digital cameras, color TV, and the long arc from usable products to systems that shape society. The through-line is straightforward but demanding: if you want better products, watch what people actually do, notice the workarounds they no longer complain about, and treat clusters of small usability problems like real product debt.

The second half brings that thinking into the present. Don and Kent talk about AI coding tools as force multipliers that still need direction, architecture, and supervision, then zoom out to Design for a Better World and the Don Norman Design Award. The result is a conversation about product sense that spans decades without feeling dated: the tools change, but the responsibility to understand people, systems, and consequences does not.

• Spend time watching people do real work before you ask them for solutions; observation reveals the hidden setup, workarounds, and friction they now assume are just "how it works."
• After a release, step back and fix clusters of small usability issues as a system instead of waiting for one confusing bug to become catastrophic.
• Treat AI as a force multiplier you must instruct and supervise; stay responsible for the problem definition, architecture, and review.

• Don Norman Design Award (DNDA)
• Design for a Better World
• The Design of Everyday Things
• Nielsen Norman Group — Don Norman
• United Nations Sustainable Development Goals

• Company: Don Norman Design Award (DNDA)

• Website: kentcdodds.com
• 𝕏: @kentcdodds
• GitHub: @kentcdodds
• Youtube: Kent C. Dodds

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Will's path runs from designing bucket trucks to self-taught software engineering, education products, and database tooling, and that background gives this episode a distinctive lens: software is still a product people use with bodies, habits, emotions, and mental models. The conversation makes product sense concrete through examples like onboarding timing, course complexity, support workflows, and the small confidence signals that separate stable-feeling products from merely functional ones.

You'll hear why watching users work keeps surfacing across this series, how to tell broken experiences from merely unpopular ones, why user feedback usually improves polish more than strategy, and how product engineers can stay valuable in an agent-heavy future by understanding both the user and the constraints of the software medium.

• Use AI agents more for gathering than executing: explore multiple solution paths, adjacent domains, and missing context before you ship.
• Give agents richer context like user demographics, constraints, and likely mental models, then use your own judgment to evaluate what comes back.
• Slow down long enough to question assumptions before implementation; use AI as a creativity and critique tool, not just a code accelerator.

• Will King - site
• Deploy Empathy (Michele Hansen)
• The Mom Test (Rob Fitzpatrick)
• Interface Craft (Josh Puckett)

• Company: Crunchy Data
• GitHub: @wking-io
• 𝕏: @wking__

• Website: kentcdodds.com
• 𝕏: @kentcdodds
• GitHub: @kentcdodds
• Youtube: Kent C. Dodds

Watch this episode on YouTube