Awesome Backyard

Built on PostgreSQL with pgvector extension for semantic search capabilities.

-- Example: Products table with vector embeddings
CREATE TABLE products (
  id UUID PRIMARY KEY,
  name TEXT,
  category TEXT,
  location GEOGRAPHY(POINT),
  embedding VECTOR(1536)
);

-- Enable pgvector for similarity search
CREATE INDEX ON products
USING ivfflat (embedding vector_cosine_ops);

Leverages Server-Side Rendering (SSR) for optimal SEO and Static Site Generation (SSG) for performance.

• Dynamic route generation for 1000+ equipment pages
• Automatic metadata optimization per location
• Image optimization with Next.js Image component
• Incremental Static Regeneration (ISR) for real-time updates

The system uses the Haversine formula in PostgreSQL to calculate geographic proximity and automatically generate contextual internal links.

-- Haversine formula in PostgreSQL
SELECT
  city_name,
  ( 6371 * acos(
      cos(radians($1)) *
      cos(radians(latitude)) *
      cos(radians(longitude) - radians($2)) +
      sin(radians($1)) *
      sin(radians(latitude))
    )
  ) AS distance_km
FROM cities
ORDER BY distance_km
LIMIT 5;