What to eat today?

Tired of deciding what to eat? ‘What to eat today?’ blends your mood, weather, and cravings to serve perfect meal ideas—with adorable pixel characters delivering your picks!

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  1. Inspiration

This project started with a simple, everyday question: “What should I eat today?”

With so many choices—and factors like weather, mood, diet, and who you’re dining with people often experience decision fatigue even over small daily choices.

Especially when eating alone, with friends, or deciding whether to dine out or stay home, the options and context can completely change the decision.

“What to eat today?”*was created to ease this burden by making smart recommendations tailored to each user’s situation, while also adding a touch of fun with cute pixel characters.

  • Factors considered:
    • Weather
    • Type of food (rice, noodles, soup/stew, rice cakes, dessert, meat, vegetables, seafood)
    • Hot or cold dish
    • Spicy or not spicy
    • Dieting or not
    • Who you’re dining with (alone, family, friends, significant other, someone you’re dating)
  1. What it does

What to eat today? is an app that recommends what to eat today based on factors such as weather, mood, diet, dining companion, and location.

Users can get recommendations in two ways:

  1. Tap the random button*to receive a completely random food suggestion.
  2. Enter personal information to receive tailored recommendations matching their situation.

On the recommendation result page:

  • The food name and a brief food description are displayed.
  • A cute pixel character appears to make the user experience more fun and engaging.

If the user doesn’t like the first suggestion:

  • They can tap the “Do you like this?” button to confirm their choice.
  • Or tap “Try Another” to get up to Top 3 alternative suggestions.

If the user still doesn’t like any of the three suggestions, a special button appears to randomly pick any food for them. The food chosen randomly at that point becomes the user’s final recommendation.

  1. How we built it

    1. Design Inspiration**: We took visual and UX inspiration from platforms like Pinterest, Mobbin, and Brutalistwebsites.
    2. Prototyping: We created the app prototype using Adobe XD.
    3. Development Platform*: We built the main logic and UI using **Bolt.new*, which enabled us to quickly transform our ideas into functional screens and workflows.
    4. Database: We used Supabase to store and manage food data and to handle real-time queries for personalized recommendations.
    5. Challenges we ran into
    6. Balancing simplicity vs. flexibility — Users want fast answers, but also personalization tailored to their unique situations.
    7. Creating a unified UI — Designing a clean UI with multiple decision paths (random choice, personalized input, Top 3 suggestions) while keeping the experience consistent and intuitive.
    8. Designing micro-interactions — Making animations and interactions feel smooth and natural across both mobile and desktop devices.
    9. Building recommendation logic — Implementing smart filtering and ranking logic without overwhelming the user with too many input fields.
    10. Keeping users engaged — We worked hard to make sure the app was not only functional but also fun and engaging, using cute pixel characters and playful UI to capture user interest and reduce decision fatigue.
    11. Accomplishments that we're proud of
  • We’re proud that we could transform our ideas into a real, working product through countless brainstorming sessions, design experiments, and discussions about different directions for the user experience.
  • We created not just an app, but a fun and engaging tool that makes daily decisions easier and more enjoyable.
  1. What we learned
  • We learned a lot about how to design an app that’s not only functional but also fun and easy to use.
  • Users appreciate speed and clarity over endless customization, so we focused on keeping interactions simple and delightful.

  1. What's next for What to eat today

  2. Implement real-time recommendation logic using APIs and user history to make suggestions even more personalized.

  3. Explore smart integrations like calendar sync, wearable input, or location triggers to improve context-based recommendations.

  4. Expand beyond food recommendations to help users decide on other everyday choices like:

    • What to wear today?
    • What to do today?
    • Where to go on the weekend?

  5. Add a login system so users can track their personal recommendation history and revisit previous suggestions anytime.

Built With

  • bolt.new
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