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.content.strip()



if __name__ == "__main__":

    diff = get_diff()

    if diff:

        print(generate_commit_message(diff))

    else:

        print("No staged changes.")

That's it. No LangChain, no ChromaDB, no streaming. It worked. I shared it on a forum, and within a week, 47 people had forked it. The version they use now has caching and a CLI—but the MVP was a single Python file.

Lesson 1: The first version should embarrass you. If you're proud of it, you probably spent too long.

Pick One User, One Workflow

My third project was supposed to be a "universal AI assistant for project management." I had grand plans: it would read Jira tickets, Slack messages, and GitHub issues, then summarize your day. I built the Slack integration first. Then the Jira one. Then I realized I'd never actually used it myself—because I didn't use Jira.

I pivoted hard. I asked myself: What's the one thing I personally do every day that AI could speed up? The answer was my daily standup notes. Every morning I wrote three bullet points in Notion. So I built a script that read my git activity from yesterday and generated standup notes.

// standup.js - requires GitHub token in env

const { execSync } = require('child_process');



const repos = ['my-project', 'another-tool'];

const since = new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString();



const commits = repos.map(repo => {

  const log = execSync(`git log --since="${since}" --oneline`, { cwd: `../${repo}` }).toString();

  return `# ${repo}\n${log}`;

}).join('\n\n');



const prompt = `Summarize these commits into three bullet points for a standup:\n${commits}`;

// Call OpenAI API and print result

I ran it manually for a week. Then I added a cron job. Then I realized my coworker wanted the same thing, so I turned it into a Slack bot. That bot now has 23 active users. It was never "universal"—it was just for me, and accidentally for others.

Lesson 2: If you don't use your own project daily, neither will anyone else. Build for your own pain first.

Don't Host Models. Please.

The biggest time sink in AI side projects is trying to run models yourself. I spent a weekend trying to get Llama 3.1 running on a $5 DigitalOcean droplet. It crashed. I tried a $20 one. It gave me a token every 30 seconds. I tried GGUF quantization, llama.cpp, Ollama—all of it. By Sunday night, I had a working local endpoint that was slower than a dial-up modem.

Then I looked at my API bills. For the commit message tool, I was paying about $0.03 per day. For the standup bot, maybe $0.10. That's less than a coffee per week. And I didn't have to worry about GPUs, Docker, or uptime.

Now, for all my projects, I use a pay-as-you-go API aggregator. I route requests through a single endpoint, and I only pay for what I actually use. That's the key to shipping: remove all infrastructure friction. If setting up a model host takes more than 10 minutes, you're going to lose momentum.

I personally use tai.shadie-oneapi.com because it gives me access to multiple models (GPT-4, Claude, Gemini) with a single API key and transparent pricing. No upfront commitment. I can deploy an MVP and if it gets zero users, I've lost maybe $2. If it takes off, I scale the API plan. Either way, I'm not worrying about GPU rental or model weights.

Lesson 3: Treat AI infrastructure like a utility, not a project. The best model is the one you can call in one line of code.

The Real Skill Is Shipping

After three MVPs, I've realized that the hardest part of AI side projects isn't the AI—it's the "side project" part. It's the discipline to stop adding features, to ignore the latest tool, and to put something in front of a real person.

Here's my current checklist before I start any new idea:

• Can I build a working prototype in one evening?


• Does it solve a problem I actually have today?


• Can I run it with a single API call and no custom infrastructure?


• Will I be able to show it to someone by tomorrow?

If the answer to any of these is "no," I either simplify the idea or throw it away.

The three projects I shipped? One is a CLI tool for summarizing video transcripts (using Whisper API + GPT). One is a simple web app that reformats messy JSON into clean markdown tables. And one is the standup bot. None of them are revolutionary. But they all have users, because they all shipped.

So if you're sitting on an idea for an AI app right now, here's my challenge: Spend the next 60 minutes building the dumbest version you can. Use an API. No vector database. No custom fine-tuning. No Docker. Just a script that works. Then share it with one person.

You'll learn more from that hour than from a month of planning the perfect architecture.

And when you need a model to back it, check out something like tai.shadie-oneapi.com—it's the kind of pay-as-you-go setup that lets you focus on shipping instead of server costs.

Now go ship something. Your future users are waiting.