# Alexander Tujicov

- **Event:** [fal x Sequoia 72-Hour Video Hackathon](https://cerebralvalley.ai/e/fal-x-sequoia-virtual-hackathon)
- **When:** Jul 17 at 12:00 PM – Jul 19 at 8:00 PM (PDT)
- **Where:** Online
- **Team:** [Alex Tujicov](https://cerebralvalley.ai/u/comrender)
- **GitHub:** https://github.com/comrender/wardrobe-advisor
- **Demo video:** https://youtu.be/80rvdcqVBBk
- **Gallery:** https://cerebralvalley.ai/e/fal-x-sequoia-virtual-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/fal-x-sequoia-virtual-hackathon/hackathon/gallery/77

Wardrobe Advisor is an AI-powered wardrobe assistant that helps people understand what they own and decide what to wear for their actual day. It addresses the familiar problem: “I have nothing to wear.”

Many people own plenty of clothes but repeatedly wear the same combinations, forget what is already in their wardrobe, or buy similar items unnecessarily. Traditional wardrobe apps organize clothing but still leave the hardest decision—what to wear today—to the user.

Wardrobe Advisor turns simple garment photos into a structured digital catalog. AI identifies useful attributes such as category, color, material, warmth, breathability, formality, availability, and fit status. Users do not need professional photographs: one everyday photo is enough to start building the wardrobe.

The demo combines this catalog with automatically detected weather, a synced demo calendar, and voice or text directions from the user. It then recommends complete outfits using only garments that are available and appropriate for the weather, occasion, comfort needs, and desired style.

Every recommendation includes clear reasons and an explainable score. Users can give feedback such as “too warm,” “too formal,” “more creative,” or “doesn’t fit,” and the system immediately reranks the wardrobe without restarting the process.

A fal.ai-powered image editing models with implementation of virtual try-on workflows, allows users to upload a selfie and preview one selected garment on themselves before getting dressed. The demo also provides a prepared preview fallback when live generation is unavailable.

The current prototype focuses on cataloging garments, understanding daily context, recommending outfits, learning from feedback, and visualizing a selected item. Future versions can use wardrobe history to identify duplicate purchases, recognize underused or outdated garments, and suggest what to keep, sell, replace, or avoid buying.

The goal is to reduce decision fatigue, help people wear more of what they already own, prevent unnecessary purchases, and make every wardrobe more useful through contextual intelligence.

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