[Example brainrot output](https://youtube.com/shorts/vmTmjiyBTBU)

[Demo](https://youtu.be/W5LNiKc7FB4)

## Inspiration

Graphic design is a skill like any other, that if honed, allows us to communicate and express ourselves in marvellous ways. To do so, it's massively helpful to receive specific feedback on your designs. Thanks to recent advances in multi-modal models, such as GPT-4o, even computers can provide meaningful design feedback.

What if we put a sassy spin on it?

## What it does

Adobe Brainrot is an unofficial add-on for Adobe Express that analyzes your design, creates a meme making fun of it, and generates a TikTok-subway-surfers-brainrot-style video with a Gordon Ramsey-esque personality roasting your design. (Watch the attached video for an example!)

## How we built it

The core of this app is an add-on for Adobe Express. It talks to a server (which we operate locally) that handles AI, meme-generation, and video-generation.

Here's a deeper breakdown:

1. The add-on screenshots the Adobe Express design and passes it to a custom-prompted session of GPT-4o using the ChatGPT API. It then receives the top design issue & location of it (if applicable).
2. It picks a random meme format, and asks ChatGPT for the top & bottom text of said meme in relation to the design flaw (e.g. "Too many colours"). Using the memegen.link API, it then generates the meme on-the-fly and insert it into the add-on UI.
3. Using yt-dlp, it downloads a "brainrot" background clip (e.g. Subway Surfers gameplay). It then generates a ~30-second roast using ChatGPT based on the design flaw & creates a voiceover using it, using OpenAI Text-to-Speech. Finally, it uses FFmpeg to overlay the user's design on top of the "brainrot" clip, add the voiceover in the background, and output a video file to the user's computer.

## Challenges we ran into

We were fairly unfamiliar with the Adobe Express SDK, so it was a learning curve getting the hang of it! It was especially hard due to having two SDKs (UI & Sandbox). Thankfully, it makes use of existing standards like JSX.

In addition, we researched prompt-engineering techniques to ensure that our ChatGPT API calls would return responses in expected formats, to avoid unexpected failure.

There were quite a few challenges generating the video. We referenced a project that did something similar, but we had to rewrite most of it to get it working. We had to use a different yt-dl core due to extraction issues. FFmpeg would often fail even with no changes to the code or parameters.

## Accomplishments that we're proud of

* It generates brainrot (for better or worse) videos
* Getting FFmpeg to work (mostly)
* The AI outputs feedback
* Working out the SDK

## What we learned

* FFmpeg is very fickle

## What's next for Adobe Brainrot

We'd like to flesh out the UI further so that it more *proactively* provides design feedback, to become a genuinely helpful (and humorous) buddy during the design process. In addition, adding subtitles matching the text-to-speech would perfect the video.
## Inspiration

In a world full of so much info but limited time as we are so busy and occupied all the time, we wanted to create a tool that helps students make the most of their learning—quickly and effectively. We imagined a platform that could turn complex material into digestible, engaging content tailored for a fast-paced generation.

## What it does

Lemme Learn More (LLM) transforms study materials into bite-sized TikTok-style trendy and attractive videos or reels, flashcards, and podcasts. Whether it's preparing for exams or trying to stay informed on the go, LLM breaks down information into formats that match how today's students consume content. If you're an avid listener of podcasts during commute to work - this is the best platform for you.

## How we built it

We built LLM using a combination of AI-powered tools like OpenAI for summaries, Google TTS for podcasts, and pypdf2 to pull data from PDFs. The backend runs on Flask, while the frontend is React.js, making the platform both interactive and scalable. Also used fetch.ai ai agents to deploy on test net - blockchain.

## Challenges we ran into

Due to a highly-limited time, we ran into a deployment challenge where we faced some difficulty setting up on Heroku cloud service platform. It was an internal level issue wherein we were supposed to change config files - I personally spent 5 hours on that - my team spent some time as well. Could not figure it out by the time hackathon ended : we decided not to deploy today.

In brainrot generator module, audio timing could not match with captions. This is something for future scope.

One of the other biggest challenges was integrating sponsor Fetch.ai agentverse AI Agents which we did locally and proud of it!

## Accomplishments that we're proud of

Biggest accomplishment would be that we were able to run and integrate all of our 3 modules and a working front-end too!!

## What we learned

We learned that we cannot know everything - and cannot fix every bug in a limited time frame. It is okay to fail and it is more than okay to accept it and move on - work on the next thing in the project.

## What's next for Lemme Learn More (LLM)

Coming next:

1. realistic podcast with next gen TTS technology
2. shorts/reels videos adjusted to the trends of today
3. Mobile app if MVP flies well!
## Inspiration

More than 4.5 million acres of land have burned on the West Coast in the past month alone
Experts say fires will worsen in the years to come as climate change spikes temperatures and disrupts precipitation patterns
Thousands of families have been and will continue to be displaced by these disasters

## What it does

When a wildfire strikes, knowing where there are safe places to go can bring much-needed calm in times of peril
Mildfire is a tool designed to identify higher-risk areas with deep learning analysis of satellite data to keep people and their families out of danger
Users can place pins at locations of themselves or people in distress
Users can mark locations of fires in real time
Deep learning-based treetop detection to indicate areas higher-risk of forest fire
Heatmap shows safe and dangerous zones, and can facilitate smarter decision making

## How I built it

User makes a GET request w/ latitude/longitude value, which is then handled in real time, hosted on Google Cloud Functions
The request triggers a function that grabs satellite data in adjacent tiles from Google Maps Static API
Detects trees w/ RGB data from satellite imagery using deep-learning neural networks trained on existing tree canopy and vegetation data (“DeepForest”, Weinstein, et al. 2019)
Generates a heat map from longitude/latitude, flammability radius, confidence from ML model
Maps public pins, Broadcasts distress and First-Responder notifications in real-time
Simple, dynamic Web-interface

## Challenges I ran into

Completely scrapped mobile app halfway through the hack and had to change to web app.

## Accomplishments that I'm proud of

Used a lottt of GCP and learned a lot about it. Also almost finished the web app despite starting on it so late. ML model is also very accurate and useful.

## What I learned

A lot of GCP and ML and Flutter. Very fun experience overall!

## What's next for Mildfire

Finish the mobile and web app