AI coding assistants like Lovable, Bolt, Cursor, and Replit can generate a functional application in a single afternoon. However, a common pattern emerges where these projects run perfectly on a developer's laptop but fail immediately upon deployment. The issue is rarely a bug in the generated logic; it is the fundamental gap between a local development environment and a containerized production server. A recent breakdown on DEV.to identifies six critical checks that catch most of these first-deploy failures, each taking less than a minute to verify.

Missing Start Commands and Port Bindings

The first two failures often stem from how the platform launches the application. Many AI-generated projects only include a 'dev' script in package.json, leaving hosting platforms without a clear 'start' command. For static sites generated by Vite or Create React App, developers must explicitly configure a serve command or deploy the built folder. Simultaneously, local apps often bind to localhost:3000, but containers require the app to listen on 0.0.0.0 to be reachable. The platform typically provides the port via an environment variable, so hard-coding ports or binding to 127.0.0.1 results in a healthy-looking log but an unreachable service.

Environment Variables and Dependency Drift

Secrets and dependencies represent the next major hurdle. Local .env files are not shipped with the project, meaning any variable the code reads must be explicitly defined in the deployment environment. A grep command can identify these requirements, but developers must be wary of platform-specific naming conventions. In Vite projects, for instance, only variables prefixed with VITE_ are exposed to the browser, and they are baked in at build time. If a value is missing during the build, the deployed site may display 'undefined' without throwing an error. Additionally, AI tools often install packages in the local session without updating requirements.txt or package.json. Testing in a clean virtual environment or running npm ci is essential to catch these missing dependencies before they crash the container.

Persistent Data and Hard-Coded Secrets

Container filesystems are ephemeral, which poses a significant risk for applications using SQLite or writing to relative paths. Data written to the code directory can vanish during a redeploy, potentially replacing the real database with an empty one. Developers should configure persistent storage paths, such as /data on platforms like Deployka, and ensure these paths are adjustable via environment variables. Furthermore, AI tools sometimes hard-code API keys to make demos work. A simple grep for patterns like 'sk-' or 'api_key' can reveal these leaks. If a key was ever committed, it must be rotated immediately, as deleting it from the latest commit does not remove it from the git history.

Key Takeaways

  • AI-generated code often lacks a production 'start' script, defaulting to 'dev' commands that hosting platforms cannot execute.
  • Apps must bind to 0.0.0.0 and use environment variables for ports, as containers do not support localhost bindings from outside.
  • Local .env files are not deployed, requiring explicit configuration of secrets and careful handling of Vite-specific variable prefixes.
  • Dependencies installed in local AI sessions often miss requirements.txt, causing crashes in clean container environments.

The Bottom Line

Stop treating AI-generated code as production-ready. The magic of instant generation hides the mundane but critical infrastructure work of defining start commands, binding ports correctly, and managing ephemeral storage. These six checks are non-negotiable for any serious deployment.