production-harden

To transform an MVP into production-grade code, systematically audit and fix common pitfalls like data model drift, happy path delusion, and type safety gaps. Apply production-hardening practices to ensure robustness and maintainability.

Common code quality issues when moving to production include Data Model Drift, Happy Path Delusion, Observability Void, Environment Confusion, and Type Safety Gaps. These fragilities make functional prototypes unsuitable for deployment.

You can add Zod validation and error handling to a prototype by applying concrete code transformation patterns. This includes implementing data validation schemas, error boundaries, and type-safe components for production readiness.

No, production hardening does not require external dependencies. It provides code examples and patterns directly to systematically audit your project's error handling, validation, and type safety without needing additional libraries.

Your functional prototype fails in production due to environment confusion and observability voids. Fragile MVPs often lack proper error boundaries, image fallbacks, and type safety, causing failures under real-world conditions.

The best way to audit code for production readiness is to systematically check for the Five Mortal Sins of production code. This involves reviewing data models, error boundaries, observability, environment variables, and type safety.