Independent product
TastyPals
Restaurant discovery should learn what you like instead of handing everyone the same ranked list.
The product in hand
Taste is the product.
I am building the experience as one connected loop: discover a place, learn from people you trust, search with intent, understand your own taste, and make the decision with confidence.
I built the home experience around a simple promise: open the app and see places that feel chosen for you, with a clear explanation of why each one fits.
The feed brings trusted diner context into the product so discovery is shaped by real experiences, not just an anonymous aggregate score.
Search combines intent, location, editorial signals, and personal match scores to reduce the whole city to a set of options worth considering.
Taste ID turns restaurant ratings into something people can recognize and share—a living profile of the occasions, cuisines, and qualities they return to.
The restaurant page brings the practical and emotional evidence together—photos, atmosphere, price, dishes, and actions—so someone can move from interest to a decision.
01
The observation
Finding somewhere genuinely good still means bouncing between maps, listicles, ratings and group chats. The information exists; the decision support is weak.
02
What I built
- Native iOS and Android apps
- Live restaurant search
- Taste ID preference model
- Recommendations grounded in ratings and diner reviews
- City guides designed to be genuinely useful
03
What it is teaching me
Try the product
Find a stronger shortlist.
Search live restaurant data and get recommendations grounded in real ratings, reviews, and your own taste.