Joseph noticed he wasn’t cooking much anymore and wanted a practical way to change that. Over December break, he decided to apply the AI/ML concepts he’d learned at Stanford by building a full-stack application that generates feasible, personalized meals based on a user’s food habits, preferences, and available fridge ingredients. While exploring existing AI tools, he realized they often pushed him toward predefined solutions: losing context, introducing irrelevant ideas, and nudging his thinking in a single direction. They decided the destination before he had even started the journey. This led to the core motivation behind the project: addressing AI homogenization, where reliance on the same models can narrow creative exploration and converge independent ideas. Instead of outsourcing thinking, this project emphasizes deliberate, hands-on integration of AI—using it as a component, not a crutch—to preserve divergence, understanding, and intentional problem-solving.
Built With
- backboard
- fastapi
- javascript
- mongodb
- node.js
- python
- tailwind
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