Can I vibe code Tracify?

Build an open-source cookieless ecommerce analytics and attribution web application using Next.js, Node.js, and PostgreSQL.

1. DATA MODEL:
- Create tables for: 'ad_accounts' (platform, credentials), 'campaigns' (external_id, name, platform, spend_daily), 'click_events' (id, anon_fingerprint, utm_source, utm_medium, utm_campaign, utm_content, timestamp, landing_page), 'orders' (order_id, gross_value, currency, timestamp, matched_click_id), and 'attribution_results' (order_id, campaign_id, weight, model_type).

2. SERVER-SIDE EVENT COLLECTOR:
- Implement a lightweight tracking endpoint (/api/v1/track) that collects click data without persistent cookies.
- Compute a privacy-compliant anonymized session fingerprint using a daily rotating salt combined with anonymized IP address (truncate last octet) and User-Agent header. Never persist raw IPs or unhashed user identifiers.
- Capture standard UTM parameters and store incoming clicks.

3. ECOMMERCE INTEGRATION:
- Build a Webhook ingestion endpoint for Shopify order creation events ('orders/create').
- Extract order ID, total value, currency, and line items.
- Extract any passed client attribution tokens from cart attributes, or attempt probabilistic matching against click events within a 7-day lookback window based on anonymized fingerprint and timestamp.

4. ATTRIBUTION LOGIC:
- Implement three attribution models via background job: First-Touch, Last-Touch, and Linear.
- Calculate ROAS and CPA by joining aggregated daily ad spend fetched via platform APIs with total attributed revenue per campaign.

5. DASHBOARD UI:
- Build a dashboard showing total revenue, spent budget, overall ROAS, and a campaign breakdown table filtered by date range and attribution model.

6. OUT OF SCOPE:
- Do not build complex ML cross-device graphs. Focus exclusively on deterministic UTM matching and 24-hour probabilistic server-side fingerprinting.

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