HackStack

Project Info

Pare

Devpost

Inspiration

As students, we see it every day: untouched broccoli, half-eaten pasta, and full servings thrown away in dining halls. Multiplied across thousands of students, this adds up to an enormous environmental and operational problem. In fact, on average, universities waste 650,000 pounds of food annually. The core issue is visibility. Once food enters the trash bin, dining halls lose all insight into what was wasted, how much, and why. Without data, dining halls can’t adjust supply, leading to systemic overproduction and unnecessary waste. We wanted to make food waste measurable, actionable, and preventable.

What it does

Pare has two core components: Computer Vision Waste Detection Using computer vision, Pare scans a student's plate before food is discarded. Our model identifies and estimates the quantity of uneaten food, automatically logging waste data to a real-time dashboard. This transforms previously invisible waste into structured, actionable data. Computer Vision Waste Detection Using computer vision, Pare scans a student's plate before food is discarded. Our model identifies and estimates the quantity of uneaten food, automatically logging waste data to a real-time dashboard. This transforms previously invisible waste into structured, actionable data. Predictive Waste Analytics Pare uses a gradient boosted decision tree model to analyze historical waste patterns and identify trends. Our system can answer questions such as: Predictive Waste Analytics Pare uses a gradient boosted decision tree model to analyze historical waste patterns and identify trends. Our system can answer questions such as: Which foods are most frequently wasted? How does waste vary by day, weather, or academic schedule? How much of each food should be prepared to minimize waste? These insights enable dining hall staff to make data-driven decisions, reducing both food waste and operational costs.

What's next

Ultimately, we hope to deploy Pare in real dining halls to help universities reduce food waste, lower costs, and operate more sustainably.

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • AnthropicIn code
  • FastAPIIn code
  • HTMLIn code
  • JavaScriptIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code

7 of 7 appear in the indexed code.

AI coding agents

  • Claude CodeCommits

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

149 KB

Source files

25

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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