Inspiration for Our Project

Our team was inspired by the opening presentation from OSC, which emphasized that even the silliest ideas can be impactful—and that brainrot was not just allowed, but encouraged. With that in mind, we decided to embrace the chaos and build something both fun and useful. The rise of Gen Z slang (or “brainrot”) on social media has created a real communication gap between generations. To bridge that gap, we designed a program that translates brainrot into clear, accessible language, for tangible increased intergenerational communication.

What the Program Does

Our program takes any user message containing brainrot slang and translates it into comprehensible terms. It offers three unique modes:

  • Brainrot Translator: converts brainrot into casual, everyday conversational language.
  • Professional Brainrot Translator: rephrases brainrot into workplace-appropriate communication.
  • Brainrot Professor: explains the meaning and origin of specific brainrot terms.

This tool helps younger generations communicate more effectively with parents, grandparents, and colleagues, while also giving older generations insight into modern slang. The result is stronger intergenerational understanding, smoother professional interactions, and more inclusive communication.

Measuring Tangible Impact

To quantify our program’s usefulness, we developed a metric called brainrot density—the ratio of slang terms to total words in a message. The percent density equals the percent increase in comprehensibility after translation, since the output always contains 0% brainrot.

Example: Original sentence: Yass queen, that fit lowkey slaps harder than Ohio memes. Translated: Yes, girl, that outfit is subtly impressive, even more so than Ohio memes. Original brainrot density: 0.70 Comprehensibility increase: 70%

Across test cases, our program improved comprehensibility by an average of 52.4%, showing that communication between generations can become more than 50% more effective. Spreadsheet of test cases here.

How We Built It

The program is powered by OpenAI’s GPT-3.5-turbo model, connected through the open-source liboai C++ library and the vcpkg dependency manager. We built a lightweight code structure that stores a custom .csv database of brainrot terms, passes it to GPT as context, and runs a menu-driven loop with a switch statement.

The terminal text interface allows users to select translation modes, while built-in input handling and error checking keep the experience smooth, reliable, and user-friendly.

Then, we built an additional graphic interface to allow for easier usage, which uses JavaScript, Node.js, and React. Users can now access the translator on any device from the this site.

Challenges We Ran Into

Most of the technologies—API endpoints, submodules, dependencies, Node.js—were brand new to us. Getting everything to work together involved hours of trial, error, and Googling. But with persistence and hard work, we pushed through each hurdle as a team.

Accomplishments We’re Proud Of

From 11am to 2am, our team of three freshmen managed to:

  • Implement liboai and its dependencies
  • Set up an OpenAI API key
  • Build a text-based interface that connects directly to GPT
  • Build a graphics user interface hosted on a site that connects to OpenAI's API

Given our limited experience, completing this end-to-end pipeline in just 15 hours was a major achievement.

What We Learned

We deepened our knowledge of C++, JavaScript, APIs, and dependency management while also learning how to collaborate effectively on a technical project. Most importantly, we gained the confidence to approach new tools, debug relentlessly, and keep building even when things break.

What’s Next for Brainrot Translator Chatbot

Our next step is updating our visual web interface to add more features and to make it more user-friendly, so users can access the Brainrot Translator from anywhere. We want to make it more intuitive, interactive, and accessible for everyone, to be able to bridge communication gaps with the click of a button.

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