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meetly_metoring_app

HarperCodeLabs

A working prototype of a mentor booking platform built with Next.js 16 and React 19, featuring a mentor directory, filtering, simulated payments, and an in-memory data store that requires zero setup.

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Last updated

2026-07-13

AI repository intelligence
FR-AI / ANALYSIS

Why it is worth attention

Demonstrates a complete end-to-end mentor booking flow (browse, filter, book, pay, accept, meeting link) using modern Next.js App Router, with a focus on minimal setup and realistic payment simulation.

Who it is for

  • Developers evaluating Next.js 16 for prototyping marketplace apps
  • Startups building MVP for mentor/coaching platforms
  • Students learning full-stack React with state management
  • Tech leads assessing feature scope for a booking system

Use cases

  • Prototyping a mentor match platform with booking and payments
  • Testing end-to-end user flows for mentee and mentor roles
  • Demonstrating simulated payment authorization and capture logic
  • Building a reference implementation for a filterable directory with availability management

Strengths

  • Zero database dependency – in-memory store with seed data allows instant run with npm install && npm run dev
  • Covers full booking lifecycle: browse, filter, book, pay authorization, mentor accept, meeting link sharing
  • Simulated payment flow with authorization at booking and capture on acceptance avoids refund complexity
  • Well-structured codebase with clear separation of concerns (API routes, components, lib) and documented mapping to requirements

Considerations

  • Data resets on server restart – not suitable for production or persistent usage
  • No authentication or login – mentor selection is manual via a dropdown in the console
  • Payments are simulated and out-of-scope items like ratings, chat, recurring bookings are missing

README quick start

MentorMatch — Mentor Platform (test build)

A working prototype of the mentor booking platform described in the requirements: mentees browse mentors, filter by tags, book an open slot, pay, and get a meeting link once the mentor accepts.

Built with Next.js 16 (App Router) + React 19. No database is required — data lives in an in-memory store (lib/db.js) seeded from lib/seed.js, so the app runs with zero setup. Payments and payouts are simulated.

Run it

npm install
npm run dev      # http://localhost:3000

npm run build && npm start for a production build.

The in-memory store resets every time the server restarts (fresh seed data).

Where to click

PageRouteWhat it does
Directory/Grid of mentor cards + all filters (main screen)
Mentor profile/mentors/[id]Full details, bio, availability, booking panel
My bookings/bookingsMentee's bookings, status, meeting link
Mentor console/mentorSet availability, accept/decline requests (pick which mentor you act as — no login in this test build)

How the build maps to the requirements

  1. User roles — Mentee flow (/, /mentors/[id], /bookings) and Mentor flow (/mentor). No login in the test build; the console has a "acting as" mentor picker.
  2. Mentor directory — Card grid with name, photo (initials avatar), industry, working status, availability ("next slot" + open-this-week count), meeting mode, years of experience, university, company, last title, and support-area tags.
  3. Filtering — Any combination of industry, working status, meeting mode, experience range (min/max), university, and support areas (multi-select). "Both" mentors match Online/In-person requests.
  4. Mentor profile page — All tags in detail, bio, open-slots-only availability, per-slot "Book".
  5. Booking flow — Select slot → simulated payment (authorize) → booking is pending → mentor accepts/declines. On accept, a meeting link is shared to both parties; on decline, the mentee is refunded and the slot released.
  6. Mentor availability — Add/remove open time blocks (datetime-local). Booked slots disappear from mentee view and can't be removed until resolved.
  7. Payments — Authorized at booking, captured only after the mentor accepts (avoids refund complexity on decline). Payouts are manu

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