AI Labs
I use AI as a design partner to accelerate research, ideation, prototyping, and validation. Rather than replacing design thinking, AI helps me explore more possibilities, test ideas faster, and spend more time making product decisions that improve the user experience.
The projects below show how I use tools like Claude and Cursor to move from concept to validation in days instead of weeks.
Marlin Hunter
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Why: Job search felt overwhelming and isolating. I wanted to build something that felt manageable and fun.
How: I used Claude to brainstorm features, explore interaction patterns, evaluate edge cases, and accelerate development. I then iterated on the product based on my own UX decisions and deployed the application through Netlify.
What I Built:
Streak and level system to encourage consistency and momentum
Chrome extension that auto-captures job title and link from job postings
Daily affirmations and pixel art aesthetic to make the experience feel fun (even though job hunting is not)
Application tracker for accountability and organization
Networking tracker to maintain connections (statistically, the best way to land a job)
What It Shows: I moved from a problem- a *disorganized, demoralizing job search* to a delightful, functional solution. This project demonstrates how AI can accelerate the path from problem identification to user validation. By combining product thinking, UX design, and AI-assisted development, I was able to move from concept to a working product in two days.
Why: Job search felt overwhelming and isolating. I wanted to build something that made it feel manageable and fun.
How: I used Claude to brainstorm features, explore interaction patterns, evaluate edge cases, and accelerate development. I then iterated on the product based on my own UX decisions and deployed the application through Netlify.
What I Built:
Streak and level system to encourage consistency and momentum
Chrome extension that auto-captures job title and link from job postings
Daily affirmations and pixel art aesthetic to make the experience feel fun (even though job hunting is not)
Application tracker for accountability and organization
Networking tracker to maintain connections (statistically, the best way to land a job)
What It Shows: I moved from a problem- a *disorganized, demoralizing job search* to a delightful, functional solution. This project demonstrates how AI can accelerate the path from problem identification to user validation. By combining product thinking, UX design, and AI-assisted development, I was able to move from concept to a working product in two days.
Why: Job search felt overwhelming and isolating. I wanted to build something that made it feel manageable and fun.
How: I used Claude to brainstorm features, explore interaction patterns, evaluate edge cases, and accelerate development. I then iterated on the product based on my own UX decisions and deployed the application through Netlify.
What I Built:
Streak and level system to encourage consistency and momentum
Chrome extension that auto-captures job title and link from job postings
Daily affirmations and pixel art aesthetic to make the experience feel fun (even though job hunting is not)
Application tracker for accountability and organization
Networking tracker to maintain connections (statistically, the best way to land a job)
What It Shows: I moved from a problem- a *disorganized, demoralizing job search* to a delightful, functional solution. This project demonstrates how AI can accelerate the path from problem identification to user validation. By combining product thinking, UX design, and AI-assisted development, I was able to move from concept to a working product in two days.
Try it out →
Ripple
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Why: Ripple began as a traditional UX case study, but I wanted to validate how key interactions would perform on real devices. Rather than relying solely on Figma prototypes, I built a fully functional responsive version using Cursor to test assumptions in a realistic environment.
How: I built a fully functional, responsive prototype using Cursor. I used Claude to think through mobile interaction patterns and edge cases before building. The prototype let me conduct real usability testing instead of getting feedback on static Figma designs.
Result: Building the prototype surfaced usability issues that were not apparent in static designs:
3 out of 5 users overlooked the expense breakdown on mobile despite finding it easily on desktop.
Goal management interactions felt cramped on smaller screens and required additional refinement.
These findings directly informed subsequent design iterations.
The prototype helped me iterate quickly and surface insights that would've been invisible in Figma. The prototype demonstrated how AI-assisted development can accelerate UX validation by allowing designers to test real interactions before engineering implementation.
Why: Ripple began as a traditional UX case study, but I wanted to validate how key interactions would perform on real devices. Rather than relying solely on Figma prototypes, I built a fully functional responsive version using Cursor to test assumptions in a realistic environment.
How: I built a fully functional, responsive prototype using Cursor. I used Claude to think through mobile interaction patterns and edge cases before building. The prototype let me conduct real usability testing instead of getting feedback on static Figma designs.
Result: Building the prototype surfaced usability issues that were not apparent in static designs:
3 out of 5 users overlooked the expense breakdown on mobile despite finding it easily on desktop.
Goal management interactions felt cramped on smaller screens and required additional refinement.
These findings directly informed subsequent design iterations.
The prototype helped me iterate quickly and surface insights that would've been invisible in Figma. The prototype demonstrated how AI-assisted development can accelerate UX validation by allowing designers to test real interactions before engineering implementation.