## Inspiration

I really related to the problem the career councilors described. Reflecting on things can be hard. Especially when you're reflecting on yourself. Our goal is to make this process fun and easy, with a little help from our friends.

## What it does

Meerar compares HOW OFTEN you demonstrate a skill, and how much you ENJOY demonstrating it. To elaborate:

* When ever you engage in an event, Meerar will prompt you take a little note, describing your experience and how you felt.
* We then use the Lexalytics API to decide how much you enjoyed the event.
* Your reflection is posted anonymously on ExperienceMine, along with other people's reflections.
* Here's the twist: other students read your reflection and assign a "skill" that they feel you demonstrated.
* The more you review and post reflections, the more points you earn. You can earn achievements and compete with your friends like this!
* At this point, we have 2 pieces of info: how you felt about the event, and what skill you demonstrated at the event. Once you have enough data, we plot a graph that compares HOW OFTEN you demonstrate a skill, and how much you ENJOY demonstrating it, and provide an analysis on you strengths and weaknesses, and recommends events to attend to play to your strengths.

## How I built it

I built Meerar with a PHP backend, which is used to store and process user information. The front end was built on the trusty Twitter Bootstrap framework, with bunch of JavaScript libraries sprinkled in (i.e. materialize.js, chart.js). I used the Lexalytics API in order to gauge the sentiment in user's descriptions. I also wrote a little scraper to scrape from LinkedIn profiles of users which I execute with a bash script (what can I say, I'm learning). It's a bit patchwork-ish, but it works!

## Challenges I ran into

I came to McGill not knowing anyone, and being a beginner, it was challenging to find a team that would take me in. I managed to find another team of beginners but unfortunately, my team decided to leave the hackathon just after dinner was served on Saturday. As a result I had to do the project all myself: a pretty daunting task! This is my first major hackathon so I was a little bummed out. Still, I followed through because I thought I had an interesting idea. Hopefully the judges agree!

## Accomplishments that I'm proud of

I almost ran into a dead end with the LinkedIn API. Essentially, I wanted to get job information about a user from their profile. However, you needed to register your app with Linkedin and must have an official website (which I did not have) in order to qualify to recieve the access key to use their API. Since I didn't have this, I found a workaround: scrape the page manually with Python, and then exec() a bash script to scrape! Though it may not seem like much, I proud of finding the "way" with my "will".

## What I learned

Tech wise, I learned how to use chart.js. It's a really cool way to visualize data with beautiful graphs in the browser. Will definitely be using it again in the future. But what I really learned is that I have a lot left to learn. Everyone at McGill really knows there stuff. Next year, that'll be me.

## What's next for Meerar

Hopefully, if the judges like it, we can work on making it a full fledged app available online for student at McGill.
## Inspiration

Currently, in war-torn and disaster-struck areas, first responders are risking their lives unnecessarily, as they lack the resources needed to accurately and safely assess a disaster zone. By using robotics, we can prevent the risk of human life.

## What it does

The SPOT robot has been enabled to be an independent rescue machine that understands human emotion and natural language using AI with a noted ability to detect a language and adjust output as such.

## How we built it

We built it using a variety of tools including Hume for AI/transcription, OpenCV for ML Models, Flask for the backend, and Next.js for the frontend.

## Challenges we ran into

Connecting to and controlling SPOT was extremely difficult. We got around this by building a custom control server that connects directly to SPOT and controls its motors. The Hume API was relatively friendly to use and we connected this to a live stream of data via the Continuity Camera.

## Accomplishments that we're proud of

Fixing SPOT's internal linux dependencies. This is something that blocked all teams from using SPOT and took up most of the first day. But by solving this, we enabled SPOT to be used by all teams.

## What we learned

We learned it is quite complex to combine various tech stacks across a variety of products both hardware and software. We learned to approach these problems by introducing levels of abstraction that would allow parts of the team to work parallely.

## What's next for Spotter - Revolutionizing Disaster Relief

We hope to fully autonomize SPOTTER so that SPOT can traverse and navigate disaster environments completely independently. In this way, SPOT can locate survivors and assess the situation globally.
## Inspiration

Everyone in this team has previously been to post-secondary and noticed that their large group of friends have been slowly dwindling since graduation, especially after COVID. It's already well known that once you leave school it's a lot harder to make friends, so we got this idea to make FriendFinder to match you with people with similar hobbies in the same neighbourhood as you.

## What it does

**Find friends!**

When making an account on FriendFinder, you will be asked to input your hobbies, whether you prefer chatting or hanging out, whether you enjoy outdoor activities or not, and your neighbourhood. It then gives other users a relative score based on your profile, with more matching hobbies and preferences having a higher score. Now when ever you log in, the front page will show you a list of people near you with the highest score, allowing you to send them friend requests to start a chat.

## How we built it

**With friends!**

We used HTML, CSS, and Javascript for the frontend and Firebase and Firestore for the backend.

## Challenges we ran into

**Our friends...**

Just kidding. One of the biggest challenges we faced was the short amount of time (24 hours) of this hackathon. Being first year students, we made a project of similar scale in school but over 4 months! Another challenge was that none of us knew how to implement a real time chat app into our project. At first we wanted to learn a new language React and make the chat app beautiful, but due to time constraints, we researched a simpler way to do it just to give it base functionality.

## Accomplishments that we're proud of

**Our friendship survived!**

After the initial scramble to figure out what we were doing, we managed to get a minimum viable product in 24 hours. We are really proud that we incorporated our knowledge from school and learned something new and integrated it together without any major issues.

## What we learned

**Make good friends**

The most important thing we learned is that team work is one of the most important things needed for a good development team. Being able to communicate with your team and dividing work up by each team member's strengths is what made it possible to finish this project within the strict time limit. The hackathon was a really fun experience and we're really glad that we could form a team together.

## What's next for FriendFinder

**More features to find more friends better**

* beautify the app
* add friend / pending friend requests feature
* security/encryption of messages
* report user function
* more detailed hobby selection list for better matching
* update user's profile / hobby selection list at any time
* let users add photos
* group chat function
* rewrite sections of code to become more efficient