AggieEats

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  Home page where you input preferences

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  Outputted Meal Plans

Inspiration

Students in college typically struggle with time and financial management. This combination leads to little thought being given to a healthy, budget-friendly diet, as students turn towards cheap, fast alternatives. Most meal planners are focused on cooking with many specifics that students usually aren't interested in reading through, rendering those planners obsolete.

What it does

AggieEats is a simplified meal planner that takes into account students' time when planning their meals. With desired macronutrients, dietary preferences, and location, AggieEats provides the exact meals to eat and where (many students lack time to cook), alongside their nutritional contents. Instead of having to sit down and plan, students can figure out what to eat in between classes.

How we built it

We used Figma for design with the help of Claude and Gemini. In addition, we used Vite and Tailwind CSS to enhance the design further and implement and decorate our UI.

Challenges we ran into

The main challenge that we faced was sourcing the nutritional data for all of the meals on campus. While dining halls were somewhat formatted consistently, retail locations wildly differed in their formatting, and often missed out on key pieces of information, such as adherence to dietary preferences (such as Halal preparation techniques) or the inclusion of allergens.

Accomplishments that we're proud of

We're proud of the simplicity in our user experience, as the user only needs a few seconds to go from their goals to a full-fledged meal plan, allowing students to plan out everything efficiently, even between classes.

What we learned

Throughout the development process, we learned that there was a huge gap in easily available nutrition data, especially for smaller and localized retail food locations. This can not only cause issues in the programming area, but it can also cause larger issues when it comes to students with strict dietary preferences trying to figure out what to eat. Besides this, we also learned more about Typescript and using Next.js for front and back-end, as we debugged many errors with AI code generation, which leads into the last point of us learning about the gap between human and AI programming.

What's next for AggieEats

With the simplicity of our web-app, this can be integrated into other platforms like the Texas A&M app or Navigate 360, to create a more simplified access to the web-app. Furthermore, as we dive deeper into the information of local restaurants' nutritional facts, especially the facts that were not publicly listed online, we can create a more inclusive database with more options for students.

Built With

• figma
• node.js
• npm
• tailwind
• typescript