Know what’s there.
Add and edit wardrobe items, browse their images, and narrow the collection by category or name.
PRODUCT DEVELOPMENT / PERSONAL PROJECT
A wardrobe,
with a system.
My girlfriend had plenty of clothes, but no system for organizing them. I built Makefits to bring the wardrobe into one place, make outfits easier to put together, and plan what to wear.
Explore MakefitsLive web application · Guest mode available · Source repository private
FROM A PERSONAL PROBLEM TO A PRODUCT
Use what you own.
See what goes together.
A digital wardrobe connects individual pieces to outfits, saved combinations, and a calendar.
Illustration of the workflow, not an app screenshot.
01 / THE STARTING POINT
The clothes were there.
The structure wasn’t.
This project began with someone I knew and a problem I could see. A large wardrobe is difficult to use when its contents are hard to browse and outfit combinations have to be remembered.
The idea was to build a simple sequence: collect the pieces, make them searchable, combine them into looks, and save those looks for later. That everyday workflow became the foundation of Makefits.
Build a look.
Select pieces, explore complementary combinations, and save outfits with a title, notes, and category.
Make it part of the day.
Revisit saved outfits and use the calendar to plan looks, with a weather integration to provide context.
02 / BUILDING THE APPLICATION
More than a gallery.
A connected workflow.
The frontend uses React and TypeScript, with Vite, Tailwind, and shadcn components. Supabase integrations support the account and data layers. The public app includes a guest entry point so visitors can explore before signing in.
The wardrobe view includes search, category filters, item editing, and selection controls. In the outfit builder, selecting a piece adds it to a combination; saved outfits carry their selected items, notes, and category into the next part of the workflow.
Calendar planning connects outfits to dates. The implementation includes mobile-specific calendar layouts and a city setting for its weather service.
03 / SUGGESTIONS AND IMAGE FEATURES
Helpful combinations.
Clear distinctions.
The outfit shuffler uses rules for clothing types, color compatibility, and seasonal filters. The suggestions panel can complement selected pieces or fill a missing category, such as footwear. These are heuristic suggestions rather than predictions from a trained recommendation model.
Separate backend functions implement integrations for background removal and AI try-on. Those image features are distinct from the outfit-matching rules. Their presence in the code does not establish that the external services are currently configured or available.
Keeping these distinctions clear matters: organizing clothes, recommending a combination, and generating a try-on image solve different problems and need different evaluation.
04 / REFLECTION
Start with a person.
Build around the habit.
Makefits represents a different kind of project in this portfolio: turning a personal frustration into an application someone can open and use. The central design question is how to make adding clothes, finding them again, and saving a useful combination feel like one continuous process.
Future evaluation could focus on the effort required to add a wardrobe, how often saved outfits are reused, and whether suggestions help users find combinations they would keep. No measured adoption or time-saving claims are made here.