Our inspiration for this year’s HackNY challenge was weather; but more specifically “whether.” We’ve all found ourselves sluggishly waking up to an ambiguous New York sky and an equally ambiguous rationale behind choosing our clothes for the day. What if an app could be utterly mobile, but combine local weather information and scheduled social/professional obligations to curate your daily outfit?

That’s when we knew, we were going to be MO-LO-SO bro!

What it does

Mimic is a mobile application that continually scans local weather and your scheduled commitments to determine what outfit is most suitable for your day. Mimic curates a fashion conscious look for you to imitate with your own wardrobe. Additionally, using machine learning, Mimic analyzes New York Times articles to assess top fashion trends.

How I built it

Currently running on Android, Mimic’s backend is built using MongoDB. The platform curates fashion by parsing weather information based on current location. After recording the temperature, the application uses a custom built algorithm to generalize the weather (temperature, rain, snow, etc) to determine which particular clothing items are best suited for the environment. Meanwhile, the front-end XML/Javascript assembly focused on creating a minimal interface for users to quickly identify what to wear.

Challenges I ran into

Using several different APIs, we ran into quite a few compatibility issues. However, this challenge was fun to deal, as we dove deep into each package. Additionally, trying to obtain a HackNY t-shirt proved to be virtually impossible.

Accomplishments that I'm proud of

The fact that it looks good and works ok.

What I learned

Lots about Java, Android, MongoDB, and the various APIs that make the world go round.

What's next for Mimic

Clarifai! Users are able to currently add clothing items to our universal database, but ideally we would love to make the process seamless through camera scanning.

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