# I-Spy

Learning A-Frame WebVR at QHACKS 2017

We wanted to play with the Samsung Gear VR as well as the A-Frame webVR framework to see if developing virtual reality on the web was more accessible than using more high end occulus rift to garner somewhat of the same effect.

The game is simple, just like the classic game of I Spy you are presented with a rhyme of a series of objects you have to locate before you can exit the room. Look by focusing the cursor at an object for more than 3 seconds.

Made entirely of JS and HTML, framework was super fun and easy to use. Would want to build out more robust score system and more levels :)
## Inspiration

TripAdvisor and RoadTrippers

## What it does

Our project gives community-based and calculated plans to users based on points of attraction and personal preferences, like staying longer in nature spots or museums. The app blends information of popular spots and visualizations to provide easy interpretation of the plan, how long it would take, and how long each attraction usually takes. Users can upload their own plans and the most popular ones go up the list so that efficient but enjoyable trips are seen by the most people.

## How we built it

React Native, Figma, JSON schemas

## Challenges we ran into

Time :(. We weren't able to construct the backend or parts of the UI because we didn't enough time to implement the many good ideas that we threw around.

## Accomplishments that we're proud of

Building a semi-functional application that is styled well and has solid scalability if worked on for longer than a day. We tried to prioritize the planning instead of the development so that we could be able to truly build this out at a future time.

## What we learned

React Native app development, product development, forming ideas and refining them, researching existing services and identifying opportunities,

## What's next for Daytrips

Possible improvements in the UI and actual implementation of backend. Most likely, nothing will be done for the rest of this semester, but who knows
## Inspiration

We were inspired by the challenge of identifying future visionary leaders in tech using AI. We wanted to use the openAI's API and other sources to create various models that can analyze the attributes and impact of potential tech leaders.

## What it does

We built several different models for interpreting which aspiring entrepreneur/visionnaire is most likely to lead a successful startup. They take in a name of a person who is involved in the tech ecosystem and outputs a score and further details on 33 features in total, including their . The scores are based on various factors such as personality traits, experience, emotional and social skills, and leadership skills. The README.md contains the detailed report of our general approach.

## How we built it

We built GeorgianAI using various Python programs and the openAI API.

## Challenges we ran into

Some of the challenges we faced were:

* Finding reliable and relevant data sources for each person
* Dealing with noisy and incomplete data
* Balancing the trade-off between creativity and accuracy
* Ensuring the scalability and feasibility of our solution

## Accomplishments that we're proud of

Some of the accomplishments we are proud of are:

* Creating a novel and innovative solution for the challenge
* Utilizing the GenAI repository effectively
* Generating insightful and comprehensive reports for each person
* Demonstrating our solution with a captivating demo

## What we learned

Some of the things we learned are:

* How to use the openAI repository and its models and datasets
* How to scrape and process data from various sources
* How to apply NLP and computer vision techniques to analyze data
* How to present our solution effectively

## What's next for GeorgianAI

Some of the next steps for GeorgianAI are:

* Improving the accuracy and robustness of our model
* Adding more data sources and features to our model
* Testing our model on more people and scenarios
* Deploying our model as a web app or a chatbot