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

I remember the exact moment my first AI side project died. It was 3 AM, I had just spent two full weeks building an elaborate RAG pipeline with vector databases, custom embeddings, and a fine-tuned model—all for a tool that would "revolutionize how developers read documentation." I hadn't written a single line of user-facing code. I hadn't even validated if anyone wanted it. And when I finally deployed it to a hobby server, the cost of hosting the model alone was $200/month. I killed the project before anyone ever visited the URL. That was three months ago. Since then, I've shipped three AI side projects that actually have users. Not millions—but real people who use them daily. Two of them even cover their own hosting costs now. The difference? I stopped trying to build the perfect AI infrastructure and started shipping the stupidest thing that could work. Here's what I learned from those three MVPs, and how you can break out of the "AI side project graveyard" too. The Trap: Thinking You Need to Build Everything The biggest lie in the AI side project space is that you need to own the stack. Every tutorial screams "self-host Llama 3," "set up your own vector database," "build a custom agent framework." That's great for learning, but it's death for shipping. For my second project—a tool that automatically generates commit messages from diffs—I spent exactly one evening. I used the OpenAI API directly, with no caching, no streaming, no error handling. Here's the core of it: import openai import subprocess def get_diff (): result = subprocess . run ([ " git " , " diff " , " --cached " ], capture_output = True , text = True ) return result . stdout def generate_commit_message ( diff ): response = openai . chat . completions . create ( model = " gpt-3.5-turbo " , messages = [ { " role " : " system " , " content " : " Write a concise git commit message summarizing the changes. " }, { " role " : " user " , " content " : diff } ] ) return response . choices [ 0 ]. message .