Inspiration
To simplify tech shopping by matching real user needs with the ideal laptop specs—no more guesswork or endless comparison charts.
What It Does
Parses your requirements (e.g. “AutoCAD 3D modeling under $1,500”), fetches recommended feature thresholds, and delivers the best-fit laptop plus a clear two-line rationale.
How We Built It
Backend: Flask API
Data: DynamoDB for need→feature mappings, CSV catalog for laptop specs
Frontend: Plasmo + React + Mantine
AI: Gemini LLM for natural-language parsing and concise explanations
Challenges We Ran Into
Ensuring robust numeric & categorical merging across varied feature formats
Handling edge cases when user phrasing didn’t map cleanly to known categories
Accomplishments We’re Proud Of
Smooth end-to-end flow from free-text input to actionable recommendation
Two-line, confidence-toned explanations that users consistently rate as clear and helpful
What We Learned
LLM parsing can be surprisingly reliable—but always needs fallback checks for malformed JSON
DynamoDB batch operations scale well for our small-to-medium rule set
What’s Next
Add support for user reviews & real-time price APIs
Expand to cover other categories (gaming PCs, desktops, workstations)
Build a lightweight web UI for drag-and-drop want selection
Log in or sign up for Devpost to join the conversation.