## Inspiration

One of our main inspirations was looking through some of the most engaging presentations that we've seen in the past. One of the best ways to engage an audience is through laughter, and making it easier for users to implement comedic elements is what we are setting out to do. Giggle Gear might sound like a silly name, but we aren't joking around when it comes to optimizing content creation for all users.

## What it does

Giggle Gear is an Adobe Express add on that streamlines the gap between image generation and the creation of graphics, presentations and much more. Giggle Gear allows users to quickly import and caption memes, reaction images, and everything in between straight from an extensive database of internet content. We also have a fully functional AI feature that allows users to enter simple prompts and receive a newly generated meme relevant to the content of their slides.

## How we built it

Giggle gear is based on React, a library that pairs very well with Adobe Express and allows for quick responsiveness. We use imgflip, a large meme database that allows users to to create their own templates on the fly. We bridge the gap using their premium API, and as a result we're able to access their large database of images while making it as efficient as possible for all Express users.

## Challenges we ran into

Our greatest challenge was figuring out the Adobe Express add on process, and implementing a well designed front end took time as we tried our best to make our app look as accessible and easy to use as possible. Thankfully due to the wealth of resources that Adobe provides when it came to documentation, we were able to create a polished end product.

## Accomplishments that we're proud of

We are proud of the ease of use that comes with our add on, considering the amount of steps that it cuts out for the average content creator. We're also very proud of how efficiently our program is able to parse through a wide variety of images while giving a wealth of choices to all of our users. We were able to keep the cost of queries low and when the app is officially pushed to the adobe add on section, most features associated with Giggle Gear will be completely free to use for all users.

## What we learned

We learned that when we are creating content of our own, speed and ease of use comes first and that's exactly what we aimed to do. Allowing users to be as creative as possible while giving them access to the power of generative AI can unlock a treasure trove of content that not only engages audiences more, but lets creators explore areas of their presentation that they may never have thought of utilizing before.

## What's next for Giggle Gear

We're aiming to monetize Giggle Gear in partnership with Adobe Express' Premium feature. This will allow us to keep producing AI prompts at lower cost to us, and hopefully allow us to turn a profit on their platform. Giggle Gear will always be a free to use add-on, but there are some features that will allow Adobe Express Premium members to get the absolute out of our software when it comes to digital content creation.
## **Inspiration:**

Our inspiration stemmed from the realization that the pinnacle of innovation occurs at the intersection of deep curiosity and an expansive space to explore one's imagination. Recognizing the barriers faced by learners—particularly their inability to gain real-time, personalized, and contextualized support—we envisioned a solution that would empower anyone, anywhere to seamlessly pursue their inherent curiosity and desire to learn.

## **What it does:**

Our platform is a revolutionary step forward in the realm of AI-assisted learning. It integrates advanced AI technologies with intuitive human-computer interactions to enhance the context a generative AI model can work within. By analyzing screen content—be it text, graphics, or diagrams—and amalgamating it with the user's audio explanation, our platform grasps a nuanced understanding of the user's specific pain points. Imagine a learner pointing at a perplexing diagram while voicing out their doubts; our system swiftly responds by offering immediate clarifications, both verbally and with on-screen annotations.

## **How we built it**:

We architected a Flask-based backend, creating RESTful APIs to seamlessly interface with user input and machine learning models. Integration of Google's Speech-to-Text enabled the transcription of users' learning preferences, and the incorporation of the Mathpix API facilitated image content extraction. Harnessing the prowess of the GPT-4 model, we've been able to produce contextually rich textual and audio feedback based on captured screen content and stored user data. For frontend fluidity, audio responses were encoded into base64 format, ensuring efficient playback without unnecessary re-renders.

## **Challenges we ran into**:

Scaling the model to accommodate diverse learning scenarios, especially in the broad fields of maths and chemistry, was a notable challenge. Ensuring the accuracy of content extraction and effectively translating that into meaningful AI feedback required meticulous fine-tuning.

## **Accomplishments that we're proud of**:

Successfully building a digital platform that not only deciphers image and audio content but also produces high utility, real-time feedback stands out as a paramount achievement. This platform has the potential to revolutionize how learners interact with digital content, breaking down barriers of confusion in real-time. One of the aspects of our implementation that separates us from other approaches is that we allow the user to perform ICL (In Context Learning), a feature that not many large language models don't allow the user to do seamlessly.

## **What we learned**:

We learned the immense value of integrating multiple AI technologies for a holistic user experience. The project also reinforced the importance of continuous feedback loops in learning and the transformative potential of merging generative AI models with real-time user input.
## Inspiration

We took inspiration from the multitude of apps that help to connect those who are missing to those who are searching for their loved ones and others affected by natural disaster, especially flooding. We wanted to design a product that not only helped to locate those individuals, but also to rescue those in danger. Through the combination of these services, the process of recovering after natural disasters is streamlined and much more efficient than other solutions.

## What it does

Spotted uses a drone to capture and send real-time images of flooded areas. Spotted then extracts human shapes from these images and maps the location of each individual onto a map and assigns each victim a volunteer to cover everyone in need of help. Volunteers can see the location of victims in real time through the mobile or web app and are provided with the best routes for the recovery effort.

## How we built it

The backbone of both our mobile and web applications is HERE.com’s intelligent mapping API. The two APIs that we used were the Interactive Maps API to provide a forward-facing client for volunteers to get an understanding of how an area is affected by flood and the Routing API to connect volunteers to those in need in the most efficient route possible. We also used machine learning and image recognition to identify victims and where they are in relation to the drone. The app was written in java, and the mobile site was written with html, js, and css.

## Challenges we ran into

All of us had a little experience with web development, so we had to learn a lot because we wanted to implement a web app that was similar to the mobile app.

## Accomplishments that we're proud of

Accomplishment: We are most proud that our app can collect and stores data that is available for flood research and provide real-time assignment to volunteers in order to ensure everyone is covered in the shortest time

## What we learned

We learned a great deal about integrating different technologies including XCode, . We also learned a lot about web development and the intertwining of different languages and technologies like html, css, and javascript.

## What's next for Spotted

Future of Spotted: We think the future of Spotted is going to be bright! Certainly, it is tremendously helpful for the users, and at the same time, the program improves its own functionality as data available increases. We might implement a machine learning feature to better utilize the data and predict the situation in target areas. What's more, we believe the accuracy of this prediction function will grow exponentially as data size increases. Another important feature is that we will be developing optimization algorithms to provide a real-time most efficient solution for the volunteers. Other future development might be its involvement with specific charity groups and research groups and work on specific locations outside US.