## Inspiration

Going out to grab a meal with friends is always a good time until you can't agree on one place. We have collectively experienced this multiple times, and some solutions that arose included creating Google docs to suggest and vote on restaurants. However, this proved to be too cumbersome to set up and non-intuitive. We realized that this is a common issue we faced, and creating a dedicated application just for this would solve this problem.

## What it does

WhereWeEatin' is a web application that allows for the creation of a room and url that can be shared to a group. Each group member can input suggestions for restaurants. If none come to mind, restaurants can be explored with Google Maps right in the suggestions page! After all suggestions have been made, each group member can vote for his or her top pick, and the winning restaurant is displayed on the following page, along with the number of votes each restaurant received.

## How we built it

We created a Github repository for our project. Using Django, we fleshed out the back-end and database functionality. We employed Bootstrap for most of the styling of our front-end, with jQuery for the scripting. We then deployed our app using Google Cloud Services.

Technologies used:

* python
* django
* google cloud services
* jquery
* bootstrap
* github
* javascript
* html

## Challenges we ran into

One of the biggest challenges was learning the ropes with Django. We had the ideas of what we wanted to do, but we struggled a bit with how to get things set up and working correctly with the technology. However, we persevered and managed to realize a final product!

## Accomplishments that we're proud of

We are proud of being able to build a functional product within the time limit and deploy it to a live server.

## What we learned

We learned that web development requires a great deal of specific knowledge about the tools being used, not just general programming skills.

## What's next for WhereWeEatin'

Some future features and additions include:

* Yelp/reviews integration
* new and improved voting systems, and
* aesthetic redesign.

After that, it’s time to try it out in real life!
## Inspiration

With powerful, collaborative tools like Figma, designers have become more productive than ever, however, they still constantly have to create and search for visual assets as they craft software designs, including illustrations, icons, stock photos, backgrounds, and other visuals that communicate their design intent to developers and other stakeholders.
To search for these assets, they currently use a variety of resources including stock photo sites, google image search, manually crafting assets themselves in tools like photoshop, or taking screenshots of other websites. This process is tedious, not much fun, and takes them out of their preferred design OS - Figma.
Though it seems obvious to us, designers today aren’t using AI image generation tools as they design because it’s quite difficult to get the precise results that a designer needs from existing AI image user interfaces. The generated images feel too random for designers to rely on to save time. In short, prompt engineering seems too complex and technical for designers to adopt these tools. We see this as an obvious opportunity to make high-quality image generation effortless for designers.

## What it does

Conjure.ai brings the power and creativity of AI-generated digital assets directly into Figma through a UX that is tailor-made for designers. They don’t have to worry about complex prompt engineering because Conjure.ai’s guided UX allows them to simply communicate what they’re trying to create, like a background, icon, or illustration, and add a few keywords describing their aesthetic goal to get high-quality assets directly in Figma.

## How we built it

In our tech stack, Conjure.ai is three systems working in tandem. We have a React application running within the Figma plugin ecosystem, a typescript app running as a Figma controller, and a python backend that integrates with Cohere’s stable diffusion API.

## Challenges we ran into

The main challenges we faced were defining the user experience that would provide the right level of abstraction for designers to generate the assets they need without worrying about the technical details of prompt engineering and the relatively rough UX of a raw text-to-image API. In addition, we overcame some tricky information related to image compression and data format translation between the Figma react app and Figma document controller, making sure that we could handle multiple requests in repeated succession so users can generate multiple images, all the while making sure we had a slick UI that designers would feel at home using.

## Accomplishments that we’re proud of

We’re so proud that everyone on the team has been constantly using the tool since it was finished. In short — it’s really fun! We’ve generated over 400 images in just the last few hours, and we can’t wait to allow more designers to enjoy the creative power of AI-generated visual assets directly within Figma.

## What we learned

The main lesson is that sometimes all-nighters are unavoidable and software estimates are ALWAYS wrong. We estimated we could get a working prototype finished by 6 pm Saturday night. Conjure.ai didn’t complete its first end-to-end image generation within Figma until 6 am this morning! It was a team effort and we loved the process, but we look forward to a good night of sleep tonight.

## What’s next for conjure.ai

We want to launch in the Figma community plugin store! We’re big believers that software should be free for anyone building in public, so we look forward to taking a community-first approach to bringing Conjure.ai to a focused set of designers as we continue to refine the prompt development interface so that designers can effortlessly create exactly the asset they need quickly and creatively. Nothing would please us more than having designers adopt Conjure.ai as part of their day-to-day workflows.
## Inspiration

The inspiration for Seegull came from a simple yet profound realization: in a world brimming with visual information, those who cannot see are often left at a significant disadvantage. Observing a close friend navigate daily challenges with visual impairment sparked a desire to harness technology in a way that could offer a semblance of sight, enhancing autonomy and confidence for the visually impaired community.

## What it does

Seegull is a groundbreaking web platform that employs AI to enable blind and visually impaired individuals to "see" their surroundings. Using a camera and AI voice assistance, Seegull interprets visual data and communicates this information in real-time, providing descriptions, directions, and alerts to help users navigate their environment safely and independently.

## How we built it

We built Seegull by integrating the GPT-4 text, and vision API with a user-friendly web interface developed using Next.js and styled with Tailwind CSS. The core technology relies on computer vision to analyze images from the user's camera, coupled with natural language processing (NLP) to convert these visual inputs into descriptive audio feedback, utilizing ElevenLabs for text-to-speech to ensure the feedback is natural and accessible. To bolster security, especially for user authentication and data protection, we incorporated Bayun Systems, leveraging its robust encryption and security protocols. Mapbox enhances our platform by providing precise map displays and location tracking, essential for real-world navigation.

## Challenges we ran into

One of the main challenges was ensuring real-time performance while maintaining accuracy in object recognition and spatial descriptions. Balancing the computational demands of AI processing with the need for a responsive and accessible user experience required innovative solutions and optimizations. Additionally, designing an intuitive UI that caters to non-visual interaction posed its own set of design and usability challenges.

## What we learned

Throughout this project, we learned the immense potential of AI to solve real-world problems and the importance of user-centered design, especially when creating solutions for individuals with disabilities. The iterative process of building, testing, and refining Seegull taught us valuable lessons in perseverance, teamwork, and the power of technology to drive positive change.

## What's next for Seegull

Looking ahead, we plan to expand Seegull capabilities, including more languages and regional dialects to serve a global audience. We're exploring partnerships with organizations serving the visually impaired to refine our technology further and increase its accessibility. Additionally, we aim to incorporate more advanced AI features, such as predictive navigation and personalized setting adjustments, to make Seegull even more intuitive and helpful for our users.