Can I vibe code Crossing Minds?

1. DATA MODEL: Design a PostgreSQL schema with tables for `products` (id, title, category, tags, embedding vector(1536)), `sessions` (id, user_hash, created_at), `events` (id, session_id, product_id, event_type ['view', 'add_to_cart', 'purchase'], timestamp), and `co_purchases` (product_a, product_b, weight). 2. EMBEDDINGS & INGESTION: Create a background worker that generates product text embeddings using OpenAI text-embedding-3-small whenever catalog items are updated. Sync order histories nightly to update `co_purchases` weights based on items bought together. 3. RECOMMENDATION ENGINE: Build a REST API endpoint `/api/recommend` accepting a `session_id` or `product_id`. If `session_id` is provided, calculate the centroid vector of the last 5 viewed/carted products in that session, then query Pgvector using cosine similarity (`<=>`) filtered by inventory > 0. If no session history exists, fallback to `co_purchases` for the current product, or top-selling products in the same category. 4. COOKIELESS FRONTEND WIDGET: Write a lightweight JS widget (<10kb) that logs pageviews and cart actions to local SessionStorage and dispatches anonymized event payloads to the API. Render recommendation UI dynamically in shadow DOM without external tracking cookies. 5. FAILURE MODES & OUT OF SCOPE: Fall back gracefully to top category products if Pgvector query latency exceeds 100ms. Exclude real-time model retraining and complex GPU cluster management from scope.

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