Vibe coding deployment checklist before you share an AI-generated app

Before a preview link leaves your machine, open the generated page locally and look for the obvious failures: missing assets, broken relative paths, empty states, hidden overflow, mobile layout issues, and interactions that depend on a dev server. These problems are easier to catch before the link reaches a client or product reviewer.

For ZIP projects, confirm that the entry file is obvious. Most static previews expect an index.html file at the root or a clear static folder. If the AI tool created nested exports, rename or reorganize the output before publishing.

A preview link should match the state of the work. For first-round feedback, use a noindex link that reviewers can open without accounts. For production launch, move the project to a hosting platform with ownership, monitoring, and a deployment history.

This is where many AI-generated projects get tangled. Teams jump straight from generation to deployment, then spend time managing domains and repo setup for a prototype that may be rewritten tomorrow. A preview-first workflow keeps the experiment cheap.

A link alone often produces vague feedback. Send one sentence that explains what the reviewer should inspect: visual direction, copy clarity, mobile behavior, feature logic, or sign-off readiness. The more specific the review request, the faster the next AI iteration becomes.

For stakeholder review, include the source of truth as well. If the prototype came from a PRD, product spec, or design prompt, link that context next to the preview. Reviewers can then compare intent against output instead of reacting only to the surface.

The cleanest AI workflow has stages. Generate the prototype, publish a preview link, collect feedback, revise, then promote the approved version into a production platform. That gives every page a lightweight path to review without pretending every draft is a product launch.

HTMLShare is useful because it gives vibe-coded work a repeatable review checkpoint. You can publish the current static output quickly, share it with people who do not use your AI tool, and keep the production stack reserved for work that has passed review.