Building an AI Side Project That Actually Ships — Lessons from Shipping 3 MVPs

Description and use

The author demonstrates how to ship three MVPs quickly by using minimal API wrappers, treating infrastructure as a utility, and focusing on real user problems.

Problem

Developers often build AI side projects that become demos rather than usable products, leading to low adoption.

Outcome

By focusing on simple, user-centric features and treating infrastructure as a utility, the author shipped three MVPs that were adopted by real users, keeping costs low and avoiding over-engineering.

Workflow

• Identify a specific user pain point
• Build a minimal wrapper around an existing API
• Ship a simple, functional MVP quickly
• Iterate based on real usage
• Scale infrastructure only when needed

Evidence

I built a small endpoint that takes a URL and returns a structured summary. The entire AI part looks like this: ...

Source

My first shipped MVP in this new batch was painfully simple: a browser extension that rewrites selected text to be more concise. The entire "AI" component is a single API call wrapped in a background script.

Source

For all three MVPs, I went with pay-as-you-go API services. I don't have to think about capacity planning. When a social post goes viral and my bot gets a surge of requests, the API provider absorbs the load, and I just pay for what I use.

Source