PRODUCT DESIGNER · DESIGN ENGINEER · PRODUCT
Product designer and engineer, 11 years shipping mobile and web products across regulated fintech: mortgage, credit, payments. Now designing and building consumer iOS apps end-to-end, with three on the App Store and two more in development. Building with AI has unlocked a new level of learning for me, and I'm excited to bring it to a team.
Full résumé, in design, product and build readings.
CARDYA daily cardology app that shows its working: every reading arrives with the date, the cycle and the card it came from.
RESULT 30% of downloads convert to paid, several times the usual rate for a subscription app: 125 paying customers in the first two months. Every reading shows the date, the cycle and the card it came from, and the app shipped after App Review first blocked its category.
Read the case study →open cardy.today ↗download on the app store ↗
ATHLYTICA two-sided NIL marketplace: brands bought reach, student athletes and creators supplied it, and our own team ran the campaigns brands did not want to run themselves. About $150K of brand deals ran through it.
RESULT About $150K of brand deals ran through the platform, and cold pitches sent from My Hive got a reply 5–10% of the time.
ROCKET HOMESA home search platform. It guides buyers who are not yet sure they are buyers, and held a 4.9★ rating through the merger.
RESULT Two apps, two websites and two teams became one, design and development together, and the app held a 4.9+ star rating through the transition.
ROCKET LANDINGThe pages before you sign in. Four in five new accounts were first-time buyers who had no idea what they were looking at.
RESULT Visit-to-application-start on the purchase pages rose 16% (34.2% to 39.8%) at the largest mortgage lender in the US, and 7.5% on refinance (20% to 21.5%).
ROCKET REBRANDFour sibling brands rebranded at once, working alongside the agencies Lippincott and Metalabs.
RESULT In testing, nobody struggled to find their next step in the new client-driven dashboard, and several participants asked for an account so they could keep it.
WHO2SEEPoint a camera at a festival poster and it tells you which of the 200 names are actually for you.
LEARNED Pick models on evidence, not feel. The cheaper model ran in shadow behind GPT-4o on live traffic, and the logged overlap and cost set the 150-artist line where GPT-4o takes over.
Read the case study →open who2see.app ↗download on the app store ↗
SNIFFITPoint a phone at a piece of furniture in a thrift store and learn what it is worth before you carry it home. It helped us earn over $4K in flipping profit in six months.
RESULT Over $4K in flipping profit in six months for the side hustle my wife and I run on it. Every price weighs Claude's visual estimate against comparables from 50,000+ product listings across five marketplaces, so it rests on market evidence, not just the model's judgment.
Read the case study →open sniffit.xyz ↗download on the app store ↗
LIGHTNING RODAn open-source Lightning point-of-sale for small restaurants. Four volunteers, a pizza place in a town of 1,200, and a server who had never heard of Bitcoin taking the order.
RESULT Live at Five Star Pizza in Colon, Michigan, and in test at a second restaurant when I left the project. What I learned: a new way to pay succeeds or fails at the register, not in the code.
STRIKE (Fan Concepts)A bitcoin app I use, redrawn twice: once around the one thing it already owns, and once around the thing it asks you to trust it with.
RESULT A loan flow that answers, on screen, the three questions every borrower has: where is my bitcoin, what happens if the price falls, and what happens next.
