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

Most of my side projects died quietly. Not with a bang, not with a dramatic shutdown post on Twitter, but with a soft whimper — a forgotten folder in ~/projects, an undepoloyed GitHub repo, and a lingering sense of "I'll get back to it someday."

Someday never came.

That was before I started treating AI side projects differently. Over the past two months, I shipped three MVPs to actual users. Not thousands of users, mind you — but real, breathing humans who weren't me or my mom. In this post, I want to walk through what actually worked, what spiraled into chaos, and the honest lessons I'd tell my past self if I could.

The Hype Disconnect

Here's the thing about the AI side project discourse right now: everyone talks about the end result — "I built X in 24 hours and got 10k users!" — but nobody talks about the messy middle. The part where your LLM returns garbage, your API costs are silently eating your lunch, and you're debugging a prompt at 2 AM instead of sleeping.

I fell into that trap with my first project. I thought building an AI wrapper would be trivial. Just wire up some API calls, throw a nice UI on top, and boom — product. I was wrong.

The reality? The model is maybe 20% of the work. The other 80% is everything else: error handling, rate limiting, prompt injection prevention, cost management, UI state for streaming responses, and making the whole thing not feel like a janky prototype.

Lesson 1: Start With a Pain You Actually Have

Project #1 was a meeting notes summarizer. Sounds generic, right? But here's the twist — I built it because I was drowning in meetings. I was averaging 22 meetings per week (yes, I counted), and every single one generated noise that I couldn't parse later.

The MVP was brutally simple: paste in a raw transcript, get back a structured summary with action items, decisions, and open questions.

import openai

def summarize_transcript(transcript: str) -> dict:
    system_prompt = """
    You are a meeting summarizer. Extract:
    1. Key decisions made
    2. Action items (with owner if mentioned)
    3. Open questions or blockers
    4. Overall summary (max 100 words)

    Format as JSON.
    """

    response = openai.chat.completions.create(
        model="gpt-4o-mini",
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": transcript}
        ],
        temperature=0.3
    )

    return json.loads(response.choices[0].message.content)

That's it. A single function. No fancy architecture, no vector database, no RAG pipeline. Just a well-crafted prompt and a JSON response format.

The key insight here: I was solving a problem I had today, not a problem I theoretically might have someday. This keeps you honest. You can't fool yourself into thinking the product is useful when you're the primary user and you know it isn't.

Time to ship: 3 days. Users after 2 weeks: 12 (all from a Discord server I was active in).

Lesson 2: Scope Is a Survival Strategy

My second project was where I almost failed. I wanted to build an AI-powered code review tool. The vision was ambitious: analyze commits, understand context, flag bugs, suggest refactors, integrate with CI/CD pipelines.

I spent an entire week building the "real" version. Architecture diagrams. A plugin system. Multi-repo support. Then I showed it to two friends who actually do code reviews professionally.

Both said the same thing: "This is too complicated. I just want to paste a diff and get feedback."

So I killed the fancy version. I kept the core — a simple API endpoint that took a diff and returned line-by-line feedback — and shipped a web UI in a day.

@app.post("/review")
async def review_diff(request: Request):
    data = await request.json()
    diff = data.get("diff", "")

    review_prompt = f"""
    Review this code diff for:
    - Bugs or logic errors
    - Security concerns
    - Performance issues
    - Style violations (only if serious)

    For each finding, give: severity (high/medium/low), 
    line number, explanation, and suggested fix.
    Format as a numbered list.

    DIFF:
    {diff}
    """

    result = await call_llm(review_prompt)
    return {"review": result}

The response? A single HTML page with a textarea, a button, and a results section. No auth. No database. No account system. Just a tool that worked.

Lesson: Every feature you don't build is a bug you don't maintain. The 80/20 rule doesn't just apply to features — it applies to your mental bandwidth and your ability to ship.

Time to ship: 8 days (including the wasted week I had to abandon).

Lesson 3: Users Tell You What to Build Next (If You Listen)

Project #3 was the most unexpected one. I built a tool that converts meeting notes into project management tasks — Jira, Linear, Asana. I honestly built it as a quick utility for myself.

But then I put it on Product Hunt (not to promote it, just to get feedback). To my shock, it got 50+ upvotes and three paying customers in the first week.

Here's what those users taught me:

• They didn't care about the AI model I was using (they asked, but I could have used anything)
• They did care about the templating system — being able to define "when a decision is mentioned, create a task with these labels"
• They wanted multi-language support for the meeting notes (French and Spanish specifically, from two users)

That last one was interesting. I'd never considered it. But two people asked, so I added it. It took one afternoon — just adding language detection and adjusting the prompt.

from langdetect import detect

def detect_language(text):
    try:
        return detect(text)
    except:
        return "en"

# In the prompt...
language_instruction = f"""
The meeting notes are in {lang}. 
Preserve the original language when extracting:
- Decisions
- Action items  
- Questions

Do NOT translate to English unless explicitly asked.
"""

That single change got me two more paying customers. Both from France.

Lesson: Ship something useful, then let your users tell you what missing 20% of features will make it 100% more valuable to them.

Lesson 4: Infrastructure Is a Trap (Or an Opportunity)

The biggest decision that saved me was choosing not to host my own models. In the beginning, I tried — I spent a weekend setting up llama.cpp on an old GPU server just to "save money." The complexity killed me. The actual cost, once I factored in my time, was astronomical.

I switched to API-based hosting. Specifically, I use tai.shadie-oneapi.com for most of my heavy lifting. It's a pay-as-you-go API that sits in front of multiple LLM providers, so I can swap models (GPT-4o, Claude, Llama, whatever) without rewriting my code. I can also set spending caps per project, which is something I desperately needed after a $37 surprise bill in week two.

The economics are simple for a side project:

• Self-hosted: $0/month in GPU costs, but ~$80/month in my time patching things
• API-based: ~$5-15/month in actual usage, zero maintenance, instant model upgrades

That's not a debate. API wins for anything under hobby-scale traffic.

What I'd Do Differently

If I was starting today, knowing what I know now:

1. Spend 1 hour max on the idea. Most side project success comes from execution, not ideation.
2. Write the "trailer" first. A one-paragraph description of what the product does. If you can't write it, you don't understand the product.
3. Set a 2-week deadline. Not a month. Not "when it's ready." Two weeks. You'll cut scope ruthlessly.
4. Deploy on day 2, not day 20. Even if it's a blank page with a text input. Staging doesn't count. Public URL does.
5. Tell exactly 3 people. Not your friendly dev friends. Non-technical people. They'll ask questions you wouldn't think of.

The Real Metric That Matters

People talk about users, revenue, and retention. For a side project, those are vanity metrics. The only number that matters is: did I learn something I didn't know last week?

Project #1 taught me prompt engineering under real pressure.
Project #2 taught me that scope creep kills momentum.
Project #3 taught me that users will surprise you if you ship something real.

That's more valuable than any MRR number.

Wrapping Up

Look, building AI side projects in 2024 is the new "learn to code." Everyone's doing it, but very few ship. The gap isn't technical skill — it's decision-making. What to build, what not to build, when to stop polishing and just publish.

Three MVPs in two months taught me:

• The idea is 10% of the work, and it's the easiest part.
• The infrastructure is a solved problem if you use existing tools rather than reinventing the wheel.
• Real users are the worst critics and the best teachers.

And one more thing — the tooling matters more than you'd think. I spent too long rolling my own infrastructure because I was too stubborn to just use an API. Now I lean on tai.shadie-oneapi.com for quick prototyping because it lets me test different models without committing to any single vendor. It's one less thing to think about, which means more time actually solving user problems.

If you've got a side project festering in a GitHub repo somewhere, go deploy it today. Even if it's ugly. Even if it only works 80% of the time. The magic happens when real people start poking at your creation.

Trust me. I've shipped three this month, and I'm already annoyed about the fourth.