Inspiration
About the Project 🌱 Inspiration
The idea for this project came from a simple observation: Amala joints are everywhere in Lagos, yet discovery, reviews, and digital ordering are still fragmented. Unlike big food delivery apps, there isn’t a dedicated platform for connecting Amala vendors and consumers directly.
We wanted to build an ecosystem where:
Vendors can register their spots, showcase menus, and manage delivery.
Consumers can find the nearest Amala spots, filter by ratings, delivery, and trending status.
The community can leave reviews and comments, creating a living, breathing map of the Amala experience.
In short, this app is Google Maps meets Jumia Food — but for Amala lovers. 🍲
🛠️ How We Built It
We built the project using Next.js (App Router) for the frontend and server-side rendering, ensuring performance and scalability. The backend logic is powered by Node.js/Express APIs, handling:
Vendor registration and login
User authentication (JWT-based)
Reviews and comments system
Trending algorithm (based on clicks, visits, and social mentions)
For the map integration, we used Google Maps API and layered it with filters (delivery, ranking, trending, etc.).
We also designed a role-based system:
Vendors can register their spot, add menus, and connect WhatsApp for direct communication.
Consumers can explore, filter, and engage with vendors.
Hybrid users (vendor + consumer) can do both.
📚 What We Learned
The importance of data consistency between SSR and CSR to avoid hydration errors in Next.js.
Building filter systems that handle multiple inputs (stars, reviews, delivery) efficiently.
How to integrate real-time data (visits/clicks) into a trending algorithm.
User-centered design: how to make the registration process simple but scalable.
⚡ Challenges We Faced
Hydration mismatches in Next.js (due to dynamic values like Date.now() and inconsistent SSR rendering).
Designing the database schema to handle both vendor and consumer roles flexibly.
Integrating WhatsApp API in a way that feels natural and easy for both users and vendors.
Ensuring the trending algorithm is fair and not biased by bots or spam.
Time management — balancing between frontend polish and backend robustness.
💡 The Vision
This is just Phase One. Moving forward, we plan to add:
AI-powered reviews analysis (grouping what people say online into “Best Soup,” “Great Delivery,” etc.).
Gamification (badges for most visited Amala spots, top reviewers).
Social integration (users can share their Amala journeys on Instagram/Twitter).
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