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

Short summary

A developer shares hard-won lessons from shipping three AI MVPs in two months after years of abandoned projects. The core insight: over-engineering kills AI side projects—start with the thinnest possible LLM wrapper, use AI only where it adds clear value, and prototype with brute force before optimizing. The post includes concrete code examples, cost breakdowns (~$80 in API credits), and a recommendation for an API aggregator to simplify model switching.

• •Start with the thinnest possible wrapper around an LLM—skip databases, auth, and vector stores until users validate the idea
• •Use AI only where it creates clear value; use the dumbest possible solution for everything else
• •Prototype with brute force (e.g., TF-IDF instead of vector DBs) and optimize only when performance evidence demands it

Generated with AI, which can make mistakes.