honeybear

honeybear works to provide innovative food solutions targeted to low income families. We try to gather as little invasive data as possible, and use ai to aggregate data, targeting its usage.

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Inspiration

We were inspired by real-life problems that don't yet have accessible solutions.

What it does

honeybear gathers users' household size, weekly food budget, and location to direct them towards coupons in their area, and grocery stores within close range that have applicable benefits. Honeybear also uses location data to suggest food pantries nearby.

How we built it

We built it using next.js, figma, and split work up, pushing changes through github.

Challenges we ran into

Some challenges we ran into was scaling, and coming up with too many ideas in the beginning. It's important to start small and feasible. In addition, we ran into latency issues as the use of API keys from coupon websites and API keys had large amounts of data to pull from.

Accomplishments that we're proud of

We're proud to have learned how to better use learning models and AI in code, as it simplifies many aspects of coding. In addition, we learned to project plan and work efficiently.

What we learned

What's next for honeybear

As a project with real-world impact, honeybear has scalable qualities and can be developed for more positive impact on many communities who face food scarcity.

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