How to Build an MVP with AI Tools, and Where to Stop
The part of an MVP that AI app builders get right is rarely the part that breaks. I went through 752 public reports of apps built with Lovable, Base44 and Replit going wrong, and sorted each one by its cause. Most of them weren't about the screens. They broke when the app went live, at logins and stored data, over money, and when the platform itself went down. That matters if you're deciding how…
When building a Minimum Viable Product (MVP) with AI tools, it's essential to understand that the part that often breaks is not the screens or user interface. According to 752 public reports of apps built with Lovable, Base44, and Replit, issues typically arise when the app goes live, particularly at login processes and data storage, monetary problems, and when the platform itself experiences downtime.
This insight is crucial when deciding how to build an MVP today, as AI builders can quickly create a functional product within days, but the danger lies in assuming that the visible part is the complete product.
The author, with experience in building SaaS apps using Next.js, Supabase, and Stripe subscriptions, emphasizes that the key lies in knowing when to stop and involve a professional engineer. They outline a specific order for building an MVP: identifying what AI tasks to delegate and which points to involve a human engineer. The concept of an MVP, as defined by Eric Ries, is not about creating a minimal product but rather a version that provides the most valuable customer learning for the least effort.
Moreover, Michael Seibel from Y Combinator advises building a "ridiculously simple" product for early users, while Reid Hoffman adds that launching quickly doesn't mean cutting corners. If users are alienated, the launch was too soon. The primary mistake made with AI-built MVPs is assuming the visible part equals the finished product.
The author provides a step-by-step guide on how to proceed, starting from identifying the product's core job and target users, then building screens using AI, and finally connecting it to a GitHub account for personal ownership.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
