Two years. One problem. Zero broken imports.

The Origin

In 2023, I started building AI agents full-time. It was a nightmare.

Every time I asked ChatGPT to scaffold a RAG pipeline or a LangChain agent, it gave me code that looked perfect but failed to compile. The imports were wrong. The methods were deprecated. The libraries had moved from v0.1 to v0.2, but the model was frozen in time.

I spent 90% of my weekends debugging setup code instead of building product logic.

The Realization

I realized that general LLMs are historians. They know what the code used to look like.

But in AI engineering, history is useless. When the Vercel AI SDK pushes a breaking change on Tuesday, a model trained three months ago becomes a liability.

The Solution: A "Living" CLI

I spent the last two years building an infrastructure to solve this freshness problem.

SnapApp isn't just a prompt wrapper. It is backed by a specialized model training pipeline that:

1. Curates a "Clean Set" of working, compiling repositories (filtering out broken code).
2. Retrains Weekly using lightweight adapters on the latest SDK documentation.
3. Specializes exclusively in AI scaffolding (we don't try to write poetry).

Our Goal

We built SnapApp to delete the "Setup Tax."

We believe you should be able to have an idea on Saturday morning and have a compiling, production-ready AI architecture by Saturday lunch.

Stop scaffolding. Start shipping.