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

I still remember staring at my terminal for three hours, wrestling with a Docker Compose file just to get a small language model running locally. It was 11 PM on a Tuesday, and I had spent the entire evening trying to “do AI properly” — self-hosting everything, worrying about latency, and obsessing over model size. The side project I was building? It hadn’t seen a single user yet. In fact, it never would. That was my fourth failed AI side project in six months.

Then I flipped the script. Over the next two months, I shipped three different AI-powered MVPs — each one used by real people, each one built in under two weeks. The difference wasn’t that I suddenly got smarter or found a secret framework. It was that I stopped treating AI like a science experiment and started treating it like a feature.

Here’s what I learned along the way.

Start with the dumbest possible integration

My first shipped MVP was a tiny tool that summarized long Slack threads for remote teams. The idea was simple: paste a conversation, get a bullet-point summary. The old me would have tried to fine-tune a model on chat data, set up a queue system, and maybe even build a custom UI. Instead, I wrote a Python script that made a single API call.

import openai
import os

openai.api_key = os.environ["OPENAI_API_KEY"]

def summarize(text):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "Summarize this conversation in 3-5 bullet points."},
            {"role": "user", "content": text}
        ],
        max_tokens=200,
        temperature=0.3
    )
    return response.choices[0].message.content

That was it. No vector databases, no fine-tuning, no inference server. The entire backend was a Flask route that called that function. I deployed it on a $5 VPS and put a simple HTML page in front of it. Within a week, 50 people from a few online communities had tried it. Some used it daily.

The lesson hit me hard: shipping is the only thing that matters. That ugly, one-function MVP taught me more about user behavior than any architectural diagram ever could. I learned that people didn’t care about the model — they cared about the summary being fast and accurate enough.

Kill your darlings (especially the AI ones)

The second project was a small chatbot that helped indie hackers draft landing page copy. I had grand plans: multi-turn conversations, personality injection, A/B testing of responses. But after watching the first project’s feedback, I realized something — users almost never used advanced features on day one. They wanted one thing done well.

So I stripped the bot down to three actions:

• Generate a headline
• Write a subheading
• Suggest a call-to-action

That was it. No chit-chat, no memory, no persona. The code was laughably short — a few conditional prompts wrapped in a Streamlit UI. I shipped it in three days. Two weeks later, it had 200 users and a handful of unsolicited feature requests (which I promptly ignored for another month).

This was hard for me because I love tinkering with AI. But I realized that the perfect is the enemy of the shipped. Every extra feature I added before launch was a delay in learning whether the core idea even resonated. The AI didn’t need to be clever; it needed to be useful.

Infrastructure will eat your soul

By the third project, I had a rhythm: pick a narrow problem, wire up an API call, throw up a basic UI, and push it live. But this time I tried to cut costs by self-hosting a smaller model. I spun up a GPU instance, loaded Llama 2, and spent two days optimizing the prompt format.

The result? A 3-second response time (vs. 300ms from the API), a $60 GPU bill for the weekend, and zero users because the thing kept crashing under load. I had fallen back into the trap of optimizing infrastructure before proving demand.

I switched back to a pay-as-you-go API and got the MVP out in a few hours. It wasn’t as cheap per request, but it was predictable — no surprise bills, no server maintenance, no Docker nightmares. The project got 150 users in its first month, and the API cost was $12. That’s less than a pizza delivery.

That experience permanently changed how I think about AI infrastructure. Unless you’re operating at massive scale, your time is more valuable than your compute. Self-hosting models is a distraction when you’re still searching for product-market fit. The only thing that matters is getting the prototype in front of users as fast as possible.

The one infrastructure trick that stuck

After those three projects, I settled on a workflow that I’ve used ever since. I pick a hosted API that supports multiple models, so I can swap between GPT-4 for quality and GPT-3.5 or Claude for speed/cost tradeoffs without rewriting code. I want a single endpoint, a simple key, and no surprise bills.

That’s why I ended up using tai.shadie-oneapi.com for most of my recent experiments. It’s a unified API that gives me access to a bunch of models (including open-source ones) with a consistent interface and pay-as-you-go pricing. No account minimums, no complicated tiers — just a token bucket that I can top up when needed. It’s not glamorous, but it lets me focus on building instead of wrestling with infrastructure.

If you’re starting an AI side project today, I’d recommend finding a similar setup. Don’t host your own model unless you have a specific reason. Don’t over-engineer the pipeline. Ship a single API call, watch how people use it, and iterate from there.

Final thoughts

Looking back, those three MVPs taught me more than any AI course or tutorial ever did. The first one showed me that users don’t care about the model — they care about the result. The second taught me that less features mean faster learning. The third hammered home that infrastructure should be invisible, not a project in itself.

If you’re stuck on an AI side project right now, ask yourself: what’s the absolute simplest version of this that I can put in front of someone today? No Docker, no vector store, no fine-tuning. Just an API call and a basic interface. Ship that. Then decide what to do next.

The gap between “I’m building something” and “people are using something” is narrower than you think. You just have to stop optimizing and start shipping.