NoBeesWax

• 

  Landing page

• 

  Coupon generator

• 

  Dashboard

• 

  Community

• 

  Leaderboard

Inspiration

To take a stand against the treacherous giant that steals creators' affiliate data.

What it does

• Dynamically fetches coupon and gift card data from the internet (currently using r/coupons).
  
• Features an AI-powered coupon generator (fine-tuned LLaMA 3.1 on a custom dataset) that generates coupon codes for your favorite brands.
  
• Includes a community feature that allows users to upload coupons to our site in exchange for points.
  
• Provides a dashboard to track your coupon finds and usage.
  
• Offers a leaderboard to compare user points.
  

How we built it

• Frontend: React + Vite
  
• Backend: Flask
  
• Authentication: Clerk
  
• Database: Firebase
  
• Data Fetching: Reddit API + Qwen/Qwen2-VL-7B-Instruct via Nebius for scraping coupons for the dataset and dynamically accessing coupons.
  
• Model Fine-tuning: Unsloth's Colab notebook to fine-tune LLaMA 3.1
  
• Model Hosting: Ollama for locally hosting the fine-tuned model
  

Challenges we ran into

• Data scraping: There was no pre-existing dataset of coupon codes/gift cards for fine-tuning an LLM. Scraping coupon websites proved difficult as they use dynamic pages (making pytesseract unusable), and frequent requests led to IP bans (ruling out Selenium).
• Fine-tuning the LLaMA 3.1 model on a completely custom dataset presented challenges, including adjusting hyperparameters for optimal performance.
• Limited time to implement all features meant prioritizing the most critical components of the project.

Accomplishments that we're proud of

• Actually somehow tying up all the loose ends of the project and delivering a working project.
  
• Despite the complexities of scraping dynamic pages and handling IP bans, we found creative solutions to gather valuable coupon data.
• Seamlessly integrated multiple APIs (Reddit, Qwen, Clerk) to build a cohesive and dynamic system.

What we learned

• Gained experience working with unfamiliar technologies.
  
• Improved our skills in web scraping.
  
• Learned how to create API calls and integrate multiple components (frontend, backend, and model) into a cohesive system.
  

What's next for NoBeesWax

• Building a Retrieval-Augmented Generation (RAG) system using fetched data from the internet and user-submitted coupons, feeding it back to the LLM via Firebase as a vector database.
  
• Collaborating with brands to source coupons directly for our site.
• Develop a browser extension to automatically apply the best coupon codes at checkout.
• Identify additional sources for extracting coupons apart from reddit.

Built With

• clerk
• firebase
• flask
• google-colab
• llama
• ollama
• qwen
• react
• reddit
• vite