Memetoric

Our Inspiration: Memes are everywhere, yet we still understand surprisingly little about them. We kept noticing the same questions in our own lives: why older generations struggle to relate to our memes, why certain phrases suddenly feel cringe, how the same meme can mean completely different things across communities, and what actually drives a meme’s evolution. Memes influence culture, politics, and marketing, but they move too fast for traditional research methods. We built Memetoric as a first step in making that rapid evolution visible and measurable.

What is our project: Memetoric is an interactive evolutionary tree that reconstructs how a phrase spreads and mutates over time, showing origin posts, reply and quote branches, and how meaning shifts over time. Users are able to browse and select current memes in a sidebar, and easily explore their full family tree. In this way, Memetoric can be used to analyze memes. Our platform displays metrics such as daily volume and saturation phases (rising --> peak --> cooling --> saturated), which can reveal insights such as when a meme begins to feel “cringe” even while people are still posting it. Users can scrub through time, inspect individual nodes, ask Grok about mutations, generate a similar meme with Grok Imagine, and download any meme family’s data as CSV.

How we built this project: Vibes. We built Memetoric primarily with Cursor (which handled the large majority of the codebase) and Grok (for cultural analysis, reading mutations, and image generation via Grok Imagine). The backend processes a large Twitter/X firehose sample to construct evolutionary trees and precompute saturation phases.

Challenges we ran into: We ran into many challenges, such as how to visualize complex evolutionary data in a way that felt intuitive rather than overwhelming. We also spent significant time figuring out which dataset would actually support meaningful analysis (settling on a large recent X firehose sample) and identifying which trends were worth extracting, such as saturation. We also had to continually refine how we defined the impact our platform, and explain how our platform has the potential beyond just being an interesting visualization.

Accomplishments we are proud of: We are proud of how we addressed a gap we identified, which was the lack of accessible ways to study how memes actually evolve and saturate, and shape Memetoric into the first step toward something that can meaningfully support better understanding of internet culture and human cognition. Additionally, as first-time hackers, we’re proud that we were able to go from an idea to a working product in a short time.

What we learned: On the technical side, we gained experience turning messy real-world social data into structured lineages, designing an interface that makes complex evolutionary information feel intuitive, and using AI tools (Cursor + Grok) as genuine force multipliers rather than just autocomplete. We also learned how to develop in a team in such a short time.

What's next for Memetoric:

Improve the saturation model by incorporating sincere-vs-ironic classification
Add richer community detection so users can see how the same meme evolves differently across groups
Allow users to submit new phrases for tracking and pull phrases from other credible sources (possibly Know Your Meme)
Turn the lineage data into a lightweight research API or public dataset, expand to multiple relevant platforms, and move toward continually updated real-time data
Continue polishing the UI/UX based on real user feedback