*"There are these two young fish swimming along and they happen to meet an older fish swimming the other way, who nods at them and says “Morning, boys. How’s the water?” And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes “What the hell is water?”"*
-David Foster Wallace, *This is Water*

Often, the most important things about our lives are the ones that become hidden in plain sight. Like water to a fish, our perceptions of ourselves, our loved ones, and the world around us are often steeped in intentions we have trouble recognizing.

## We want to change how journaling works.

We want to help users reflect by applying LLM AI technology to their journaling. Afterthought picks up on your intention (whether you're speaking about a goal, an experience, a relationship, or something coming up) as well as your mood in order to give you a benchmark to better understand yourself. Pairing this analysis with a summary of recent journals and reflection prompts, we intend to make journaling go deeper. Not only does Afterthought inform you of patterns, it actively prompts you to question the things you've been telling yourself.

## How we built it

We started out in the cohere playground, brainstorming what kind of categories would be valuable. We went into our own notes apps and journals to see how we might use the app. From there, we began constructing a web app, a categorize script, and a summarize script. We later married these elements together.

Our dataset was first formed by coming up with out own examples of a variety of possible notes. Then, we asked chatGPT to come up with a variety of similar notes, varying the way these sentences were structured and the names mentioned. We pruned this list to make sure it was a balanced representation of possible notes.

## Challenges we experienced

Nobody on our team had used all of these technologies before, meaning that there was something we all learned. In addition to the typical hackathon blues (drowsiness and sore tummies), we spent a lot of time getting around learning curves.

## What's next

We'd like to add features that allow for people to track specific relationships/concepts, rather than these general categories. Additionally, creating a way to classify and reflect upon ideas/art is a direction we'd be interested in exploring.
We learned lots.
## Inspiration

The inspiration behind Tell Me More is how everyone around us seems to be preoccupied with their lives, whether because of school or their job, and they often forget to prioritize their mental health. It was also created to help those who have a hard time qualifying or understanding their emotions. It's so easy to get caught up with deadlines that most people often forget to take a second and reflect on their day and process how they felt during that day. There is also people who do take the time reflect and write down about their days, but don't know what to do about the way they are feeling, or they simply can't express the feelings they have. We wanted to find a way to encourage people to do the mental exercise of writing about their day and also helping them be analytical with the things they write, to lead them to better and happier lives !! :)

## What it does

Tell Me More gives you the option of writing your journal entries directly through our web-app or taking a picture of your hand written journal entries (or both). It will take the text you submitted, or extract the text from the picture submitted, and will understand what are the main emotions you were feeling in that entry. It'll also extract key words from the text to then help you associate different activities or even people to the emotion you were feeling to be able to remind them what triggered those emotions (good or bad) and offer you suggestions. You can easily track your past journal entries and see how you've been feeling and why. Our webapp will suggest different activities that seem to relate to happy journal entries, hence happier days.

## How we built it

We built the backend with Node.js and Express.js with a MongoDB Atlas database and we built the frontend with Vue.js.
We used IBM's Watson to extract the emotions from the journal entries (NLU) and used Azure's Cognitive API to extract text from the photo submissions.

## Challenges I ran into

APIs not playing nice with each other.

## Accomplishments that I'm proud of

We built a really cool webapp that works in only 24hrs! Also, sooo many APIs.

## What I learned

Mental health is a real issue and tackling it is hard, however it's important to do so.

## What's next for Tell Me More

We would want to communicate these data points to the person's psychologist, so that the psychologist has as much information on their patient to be able to better support them during their sessions. For those who don't have a psychologist, the user can decide to share their emotion status for each of their journal entries to their close friends. This can be a way to check up on your friends and see if everyone is in a good head space. We also want to detect trigger words that are associated to suicidal thoughts (or other dark thoughts) and also be able to track if you've been feeling sad for multiple days in a row to be able to send the user the right resources whenever needs be.