You did it! Everyone loves your AI-powered Streamlit app that automagically finds free parking spaces in your neighborhood.

The best thing: You just built this prototype with two friends, some pizzas, and free AWS credits over the weekend.

You feel the urge to make it BIGGER! Let's grow!

Before you know it, you'll be trapped in prototyping hell. You'll know when:

• You start integrating custom JS and user management into Streamlit

• You're trying to make the UI "look nicer" for a Jupyter notebook

• You're looking for Docker hacks to fix your flawed architecture

Scaling a prototype doesn't work because you chose the wrong tool for the wrong task

When you built your prototype, you did what product people call "discovery".

Discovery is all about validating value, usability, feasibility, or viability.

Once your prototype did its job - validation - let it go and move on to the next phase: Delivery

At delivery, you know that your original assumptions have been validated. Now you prioritize the following things:

Scalability, Reliability, Performance, and Maintainability.

99% of the time, your prototyping stack doesn't meet these goals because you sacrificed them for speed and developer convenience.

Don't try to scale a prototype, but rebuild it properly step by step

I'm not advocating a "big bang". But if your product survives validation, you should have enough confidence to invest in a (small) dev team and build your actual features step by step.

In the AI example above, you could rebuild your model with modular code, clear docs, and an operational framework that enables maintenance.

There are many ways you could approach this.

When you finally abandon your prototype, you can focus on the next phase - or hire people to do it while you discover the next "big" thing.