An AI agent can now build a complete website in one conversation. A landing page, a dashboard, a small game, a gallery. The code is real and it works.
Then the conversation ends, and the website is trapped. It exists as a code block in a chat window. You can copy it, screenshot it, or lose it. The build cost collapsed. The shipping cost did not.
This article describes five practical patterns for the last mile: getting AI-built front-ends onto real URLs, with the least friction for each situation.
Disclosure: I run Nippy, the static host I use in the examples. The patterns work with any host that supports the same operations; the point is the pattern, not the vendor.
Pattern 1: the agent ships it
The cleanest path: the AI that built the artifact also publishes it.
This works through MCP (Model Context Protocol). You connect a hosting tool to your AI client once. After that, "publish this" is part of the conversation. The agent writes the files, calls the tool, and returns a live URL.
I wrote a full walkthrough with a live demo if you want the step-by-step version.
Use this pattern when the artifact is born in a chat and the chat is where you work.
Pattern 2: you ship the folder
Sometimes you have the files locally — an export from an AI tool, a folder from a code session. The old-fashioned path is still the fastest: drop the folder on nippy.host, pick an address, and the site is live. No account setup ritual; the first publish confirms your email.
Use this pattern for one-off artifacts and for people on your team who do not touch a terminal.
Pattern 3: iterate on a stable URL
AI-assisted building is iterative. Version 2 arrives ten minutes after version 1. If each version gets a new URL, the person reviewing it is always holding a dead link.
The pattern: publish once, then update in place. The URL never changes; the content does. Your client refreshes the same link and sees the newest version. On Nippy this is one command:
npx @nippyhost/cli deploy
Use this pattern for client review loops and for anything you share in a group chat.
Pattern 4: CI ships what the repo holds
When the AI-built project graduates into a git repository, stop deploying by hand. Put a deploy token in a repository secret and add one step to your workflow:
NIPPY_TOKEN=$SECRET npx @nippyhost/cli deploy
Every merge ships. This is the boring, correct pattern for anything with a lifespan longer than a week.
Pattern 5: protect the prototype
Not every AI-built artifact is for the public. A pitch mock-up with a client's name on it should not be indexable, and sometimes should not be open at all.
Two small tools cover most cases. A password on the site keeps drafts private until they are ready. A QR code for the URL puts the prototype on a phone in one scan, which is the fastest way to test a mobile layout with a real device.
Use this pattern before you send anything with a client's brand on it to a public URL.
One warning: read what the AI wrote
Before you ship an AI-built page, search the files for secrets. Agents sometimes embed API keys, private endpoints or test data directly in the HTML, because that is the shortest path to "it works". A static site is public by definition. What is in the file is on the internet.
Thirty seconds with a search for key, token and secret is enough. Make it a habit.
The point
The interesting change is not that AI writes code. It is that the artifact loop — idea, build, share, feedback — now fits inside an hour. The build step got fast. The patterns above make the share step match it.
If you want to try the full loop, Nippy covers all five patterns, and the help center documents each one. Total setup time is roughly the length of this article.
What is the best thing an AI has built for you that never made it out of the chat window? Tell me in the comments — and then ship it.
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