## Inspiration

"What do you want to eat tonight?" A timeless question that we seek to answer.

## What it does

Using preferences and history, we intelligently suggest places, new and old, that we're sure our users will enjoy.

## How we built it

*Convex* DB and backend using OpenAI, React/JS frontend using Google Maps API.

## Challenges we ran into

**Convex** was a steep learning curve for us. The Google Maps API didn't work with React, so we had to jerry rig some vanilla JS instead.

## Accomplishments that we're proud of

Being able to efficiently parallelize work in some cases, and work together to get over difficulty working with new technology.

## What we learned

Besides all the ideas and technologies we got to explore, we also learned a lot from the really talented students/judges around us.

## What's next for Eatinerary

Our website cuts corners in a lot of places. We will next flesh this out, improve on our restaurant selection algorithm, and finish our social functionality.
## Inspiration

Our inspiration came from our love of both food and technology. We noticed that while there are many food apps available, there was no app that allowed users to quickly and easily find new dishes and cuisines based on their taste preferences in a fun and engaging way. We were also drawn to the Tinder model because of its popularity and the way it allows users to make quick decisions about whether they're interested in something. We thought this model could be applied to food matching, where users could swipe right or left on dishes and quickly receive suggestions for new dishes that match their taste preferences. Ultimately, our goal was to create an app that made it easy for people to explore new cuisines and dishes, connect with others who share their love of food, and have fun while doing it.

## What it does

Welcome to the future of food exploration and human connection! Our app has turned the sometimes mundane task of checking your food preferences into a fun, engaging game.

With our app, you'll be presented with two delicious dishes from different cuisines within the same food category, and you get to choose which one matches your taste preferences the best. Along the way, you'll receive detailed information about the dish's flavor profile and recipe source, making every choice a learning opportunity.

What's more, the app uses an algorithm inspired by the Chess ELO system to update your food preferences based on your choices, ensuring that your personalized recommendations are always spot-on.

And the fun doesn't stop there! Add your friends and compare your food preferences to see who's the ultimate foodie in your group.

## How we built it

We've used React.js and TailwindCSS for the front end.

For the backend, we've employed the powerful Convex technology, which is designed to deliver lightning-fast response times and top-notch performance, ensuring that you get the most out of your food exploration experience.

## Challenges we ran into

Our team encountered some exciting challenges during the development of our app, which only made us more determined to create the best possible user experience.

One of the biggest challenges we faced was figuring out the best way to rank and categorize food preferences. But we rose to the challenge and developed a cutting-edge algorithm inspired by the Chess ELO system that adapts to your preferences in real-time, ensuring that your recommendations are always on point.

As with any complex project, we also faced some coding issues along the way. But our team worked hard to solve these challenges, to create an app that is not only exciting to use but also rock solid in its performance.

## Accomplishments that we're proud of

Our proudest accomplishment is creating an app that delivers on both fun and human connection. We set out to build a game that not only challenges and entertains you but also allows you to connect with your friends over your shared love of food.

With our app, you'll discover just how much you have in common with your friends, as you compete to see who has the best taste in dishes. Our app is the perfect combination of playfulness and social bonding, designed to bring people closer together while exploring the exciting world of food.

## What we learned

One of the biggest lessons we learned during the development of our app was how to create a seamless and engaging multi-user experience. We set out to build an app that could connect people around their shared love of food, and in doing so, we learned how to create a web application that can bring people together in new and exciting ways.

Through the development process, we've gained invaluable experience in building an app that can handle multiple users, while delivering top-notch performance and user experience. We've learned the ins and outs of creating a platform that is both intuitive and responsive, allowing our users to focus on the fun of discovering new dishes and connecting with their friends.

In short, we've learned how to create an app that brings people closer together, one that fosters connections, sharing, and community. We're excited to continue to improve and innovate, and to share our knowledge and experience with others

## What's next for FoodMatch

Looking towards the future, our team is more excited than ever to continue developing and innovating our app. Our ultimate goal is to create an experience that brings people closer together, fostering new connections and discoveries around the world of food.

To that end, we plan to enhance our algorithm and create new features that will make the app even more engaging and fun. We're constantly on the lookout for new ways to connect people through their shared love of food, and we're excited to explore new frontiers of innovation and creativity.

Our team is committed to delivering an experience that is both exciting and meaningful, one that inspires community and encourages discovery. We're excited to continue pushing the boundaries of what's possible with food and technology and to see where this journey takes us.
## Inspiration

It's lunchtime, you are looking for somewhere to go to eat so you open Yelp and look for recommendations. After scrolling through many pages, you are overwhelmed by the number of restaurants around you and can't decide where to eat so you end up going to the fast food restaurant you always go to. We've all been there. What others like may not be what you like, but you also do not want to waste time entering all your preferences on the app.
But wait, you always like photos on social media, so shouldn't your phone know what you like already?

## What it does

Doko will collect data about the photos foods/restaurants the user have liked on social media and next time the user pass by that location, we will notify you. The user can also see restaurants around him/her in a convenient map view. Since t you have shown interest already in these restaurants, we are confident in our recommendations.

## How we built it

We used Twitter API to query tweets a certain user has liked every 10 seconds. The backend is written in Python serving as the API connecting Mongo DB and iOS.

## Challenges we ran into

Getting trapped by MongoDB Stitch iOS SDK. It took us nearly 2 hours to find out the issue in our project (also the inexplicit of the document) after reading the source code of it.

## Accomplishments that we're proud of

## What we learned

## What's next for Doko

Our first step will be to support social media other than Twitter.
Then we can include additional features such as restaurant recommendations using machine learning algorithms, or making a reservation within the app.