VibeEvent — Minimal Prototype

A tiny full-stack demo that:

• /api/events: pulls Luma events (if LUMA_API_KEY present) and merges with mock Partiful JSON, normalizes fields, and returns distance from user (Haversine).
• /api/distance: returns distance_km and walking_minutes between two coordinates.
• Frontend: lists events with status badges, date, category, location, and an “Open in Maps” link. Requests browser geolocation.

JSON shape

Each event returned by /api/events follows:

{
  "name": "Google AI Mixer",
  "start": "2025-10-07T19:00:00-07:00",
  "category": "AI / fintech",
  "location": "SoMa, SF",
  "status": "accepted",
  "lat": 37.776,
  "lng": -122.398,
  "distance_km": 2.4
}

Setup

1. Install dependencies

npm install

2. Configure env (optional, for live Luma data)

Create .env (see .env.example) and set LUMA_API_KEY.

3. Run

npm run dev
# then open http://localhost:3000

Notes

• Luma API: https://public-api.luma.com — this demo calls /api/v1/events with Authorization: Bearer <key>.
• Partiful: no public API. We use data/partiful.mock.json to simulate a few events.
• Geocoding uses Nominatim (OpenStreetMap) best-effort for addresses missing lat/lng.
• Distance uses Haversine; walking time ≈ 5 km/h.

VibeEvent-

A dynamic web scraping project to collect event data from Luma using Playwright.

Features

• Scrapes food & drink events from Luma
• Uses Playwright for dynamic content loading
• Extracts event details: title, host, date, location, and URL
• Generates CSV output with random acceptance status
• Handles infinite scroll and lazy-loaded content

Setup

1. Create and activate virtual environment:

python3 -m venv venv
source venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. Install Playwright browsers:

playwright install chromium

Usage

Run the scraper:

python luma_scrapper.py

This will:

• Navigate to https://lu.ma/food
• Scroll to load more events
• Extract event data
• Generate luma_events_with_acceptance.csv with the scraped data
• Save a debug screenshot (debug_screenshot.png)

Output

The CSV file contains the following columns:

• Description: Event title
• Hosted by: Event organizer(s)
• Date: Event date and time
• Location: Event location
• Acceptance: Random status (Approved/Not Approved/Pending)
• URL: Direct link to the event page