## Inspiration

As avid readers, we wanted a tool to track our reading metrics. As a child, one of us struggled with concentrating and focusing while reading. Specifically, there was a strong tendency to zone out. Our app provides the ability for a user to track their reading metrics and also quantify their progress in improving their reading skills.

## What it does

By incorporating Ad Hawk’s eye-tracking hardware into our build, we’ve developed a reading performance tracker system that tracks and analyzes reading patterns and behaviours, presenting dynamic second-by-second updates delivered to your phone through our app.
These metrics are calculated through our linear algebraic models, then provided to our users in an elegant UI interface on their phones. We provide an opportunity to identify any areas of potential improvement in a user’s reading capabilities.

## How we built it

We used the Ad Hawk hardware and backend to record the eye movements. We used their Python SDK to collect and use the data in our mathematical models. From there, we outputted the data into our Flutter frontend which displays the metrics and data for the user to see.

## Challenges we ran into

Piping in data from Python to Flutter during runtime was slightly frustrating because of the latency issues we faced. Eventually, we decided to use the computer's own local server to accurately display and transfer the data.

## Accomplishments that we're proud of

Proud of our models to calculate the speed of reading, detection of page turns and other events that were recorded simply through changes of eye movement.

## What we learned

We learned that Software Development in teams is best done by communicating effectively and working together with the same final vision in mind. Along with this, we learned that it's extremely critical to plan out small details as well as broader ones to ensure plan execution occurs seamlessly.

## What's next for SeeHawk

We hope to add more metrics to our app, specifically adding a zone-out tracker which would record the number of times a user "zones out".
## Inspiration

We know the struggles of students. Trying to get to that one class across campus in time. Deciding what to make for dinner. But there was one that stuck out to all of us: finding a study spot on campus. There have been countless times when we wander around Mills or Thode looking for a free space to study, wasting our precious study time before the exam. So, taking inspiration from parking lots, we designed a website that presents a live map of the free study areas of Thode Library.

## What it does

A network of small mountable microcontrollers that uses ultrasonic sensors to check if a desk/study spot is occupied. In addition, it uses machine learning to determine peak hours and suggested availability from the aggregated data it collects from the sensors. A webpage that presents a live map, as well as peak hours and suggested availability .

## How we built it

We used a Raspberry Pi 3B+ to receive distance data from an ultrasonic sensor and used a Python script to push the data to our database running MongoDB. The data is then pushed to our webpage running Node.js and Express.js as the backend, where it is updated in real time to a map. Using the data stored on our database, a machine learning algorithm was trained to determine peak hours and determine the best time to go to the library.

## Challenges we ran into

We had an **life changing** experience learning back-end development, delving into new frameworks such as Node.js and Express.js. Although we were comfortable with front end design, linking the front end and the back end together to ensure the web app functioned as intended was challenging. For most of the team, this was the first time dabbling in ML. While we were able to find a Python library to assist us with training the model, connecting the model to our web app with Flask was a surprising challenge. In the end, we persevered through these challenges to arrive at our final hack.

## Accomplishments that we are proud of

We think that our greatest accomplishment is the sheer amount of learning and knowledge we gained from doing this hack! Our hack seems simple in theory but putting it together was one of the toughest experiences at any hackathon we've attended. Pulling through and not giving up until the end was also noteworthy. Most importantly, we are all proud of our hack and cannot wait to show it off!

## What we learned

Through rigorous debugging and non-stop testing, we earned more experience with Javascript and its various frameworks such as Node.js and Express.js. We also got hands-on involvement with programming concepts and databases such as mongoDB, machine learning, HTML, and scripting where we learned the applications of these tools.

## What's next for desk.lib

If we had more time to work on this hack, we would have been able to increase cost effectiveness by branching four sensors off one chip. Also, we would implement more features to make an impact in other areas such as the ability to create social group beacons where others can join in for study, activities, or general socialization. We were also debating whether to integrate a solar panel so that the installation process can be easier.
## Inspiration

Ever found yourself struggling to keep up during a lecture, caught between listening to the professor while scrambling to scribble down notes? It’s all too common to miss key points while juggling the demands of note-taking – that’s why we made a tool designed to do the hard work for you!

## What it does

With a simple click, you can start recording of your lecture, and NoteHacks will start generating clear, summarized notes in real time. The summary conciseness parameter can be fine tuned depending on how you want your notes written, and will take note of when it looks like you've been distracted so that you can have all those details you would have missed. These notes are stored for future review, where you can directly ask AI about the content without having to provide background details.

## How we built it

* Backend + database using Convex
* Frontend using Next.js
* Image, speech, and text models by Groq

## Challenges we ran into

* Chunking audio to stream and process it in real-time
* Summarizing a good portion of the text, without it being weirdly chopped off and losing context
* Merge conflicts T-T
* Windows can't open 2 cameras simultaneously

## Accomplishments that we're proud of

* Real-time speech processing that displays on the UI
* Gesture recognition

## What we learned

* Real-time streaming audio and video
* Convex & Groq APIs
* Image recognition

## What's next for NoteHacks

* Support capturing images and adding them to the notes
* Allow for text editing within the app (text formatting, adding/removing text, highlighting)