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

I'm going to be honest with you: the graveyard of AI side projects is vast and well-populated. It's filled with half-finished chatbots, abandoned RAG pipelines, and "revolutionary" apps that never made it past a localhost URL. I know because I've contributed several tombstones myself.

But then something clicked. In the last two months, I shipped three AI side projects — not just prototypes, but actual deployed apps with real users. Not thousands, mind you. But real. Strangers were typing into my apps. That's the line between a toy and a product.

Here's how I did it, what I learned the hard way, and why I'm probably never going to hand-roll a model deployment again.

The Trap I Kept Falling Into

Every AI side project I started before this period followed the same death spiral:

1. Get a brilliant idea at 11 PM
2. Spend 3 days researching model architectures
3. Spend 2 weeks building a custom fine-tuning pipeline
4. Realize I haven't written any actual product code
5. Get bored, abandon project, repeat

The problem was never the idea. It was that I was treating the AI infrastructure as the project, instead of what the AI does for users.

My breakthrough came when I forced myself to adopt a brutal constraint: the model is a commodity, not a differentiator.

Project One: The Image Captioning Bot That Actually Got Used

My first successful MVP started with a really annoying problem. My wife and I have thousands of unsorted photos across Google Drive, and we needed to find specific ones. You know, "that picture from the beach trip in 2019" or "the one where the dog is wearing a hat."

Building a custom CLIP-based search system sounded fun. I spent a weekend sketching out vector embeddings, thinking about FAISS indexes, and planning a fine-tuning strategy.

Then I stopped myself. I realized I could build something good enough with three existing tools:

1. A vision-language model for generating captions
2. A basic text search
3. A folder structure that's already there

Here's how the core function looks in Python:

from transformers import pipeline
from pathlib import Path
import json

caption_pipeline = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")

def caption_and_save(image_path, output_file):
    result = caption_pipeline(image_path)
    caption = result[0]["generated_text"]

    with open(output_file, "a") as f:
        f.write(json.dumps({
            "image": str(image_path),
            "caption": caption,
            "folder": str(image_path.parent)
        }) + "\n")
    return caption

That's it. That's the core. I ran this over my photo library overnight (took about 4 hours for 2,000 photos on a single GPU — not terrible), and then used a simple grep-style search for captions.

My wife actually uses this. She types "dog hat beach" into a simple web UI, and gets results in under a second. It's not fancy. But it works.

Lesson one: Ship the boring version first. The version that uses an existing model and does one thing well. You can always add vector search later, but you can't add users later if you never launch.

Project Two: The Email Summarizer Nobody Asked For, But Everyone Uses

With the first project shipping, I got greedy. I had this new idea during a particularly bad week of email overload. I was a dev who got 80+ emails a day — newsletters I never subscribed to, automated GitHub notifications, LinkedIn "connection request accepted" spam.

The obvious MVP: an email digest that uses AI to summarize the day's inbox into a morning briefing.

My first instinct was to build a full email client integration. OAuth flows, IMAP connections, subscription management. I spent two evenings on that hot mess before I killed it.

Lesson two: The second version — the one that actually shipped — was a simple "forward your emails here" system. I set up a special email address that users forward important emails to. A tiny Python script polls it, strips the HTML, and feeds the text to an LLM for summarization.

// The simplest "email summarizer" MVP possible
const mailParser = require('mailparser');
const axios = require('axios');

async function handleForwardedEmail(rawEmail) {
  const parsed = await mailParser.simpleParser(rawEmail);

  const summary = await axios.post(process.env.LLM_API_ENDPOINT, {
    messages: [
      { 
        role: "user", 
        content: `Summarize this email in 50 words: ${parsed.text?.slice(0, 5000)}` 
      }
    ]
  });

  // Send summary back to the user
  await sendSummaryBack(parsed.from.value[0].address, summary.data.reply);
}

// Then: poll inbox every 5 minutes, feed emails to handleForwardedEmail()

The beauty of this approach? I didn't need to handle OAuth for Gmail. I didn't need a database of users. Users simply create a forward rule. It took me 30 minutes to wire up.

In three weeks, 47 people were using it. Not viral, but solid. And the feedback taught me exactly what to improve — which brings me to my third lesson.

Lesson three: The product that's easy to abandon is the product users tell you about. When they don't have to install or configure anything, they're more willing to actually try it — and complain about it.

Project Three: The GitHub PR Reviewer That Doesn't Suck

This one was the most technically complex, but also the one that taught me the most about real-world AI constraints.

The idea: an automated PR reviewer that catches common issues without being annoying (looking at you, every bot that flags style violations as critical bugs).

The key constraint: it needed to work well enough that humans would actually read its comments. If every PR got 15 nitpick comments, nobody would engage. I aimed for one comment per PR at most.

I built this using a fairly straightforward chain:

• Extracted the diff
• Stuffed it into a prompt with rules (don't nitpick style, only flag logic errors or security issues)
• Made the LLM output structured JSON with severity levels

The hardest part wasn't the AI part. It was dealing with API rate limits. This was the project that taught me why infrastructure choices matter more than model choice.

The Infrastructure Wake-Up Call

Here's where I'll be straight with you. In the first week of the PR reviewer, I was burning through tokens like crazy. A single PR review could cost $0.50 in API calls. My API bill ballooned to $40 in one week for a side project.

The logical move seemed obvious: self-host a model. I spent two days setting up vLLM on a rented GPU box, wrestling with CUDA versions and quantization flags. It worked, but it was a time sink with zero user-facing benefit.

Then I found something better: pay-as-you-go API platforms that give you multiple model options without lock-in. You know, the kind where you just make an API call and get billed per request. No infrastructure to babysit.

The site I've been using for the past month is tai.shadie-oneapi.com. Look, I'm not here to give you a sales pitch. It's just a practical endpoint that does what I need: you hit it with whatever model you want — GPT-5, Claude, even open models — and it routes it. Pay per request, no monthly commitment, no GPU to babysit.

For the PR reviewer project specifically, this let me test different models without code changes. I could literally swap from one model to another by changing a string in my config. That flexibility ended up saving me both time and money.

My cost for that project dropped from $0.50 per review to about $0.08 after I switched models and tuned the prompt.

What I'd Tell My Past Self

If I could go back to March and give myself advice, it would be these five things:

• Build the vertical slice first. Pick the absolute core feature, build it with existing tools, and get a user staring at it within a week.
• Prompt engineering beats fine-tuning for 90% of side projects. You're not Google — you don't have 100k users.
• Infrastructure is a means, not a feature. Nobody cares how your model is deployed. They care if the response isn't garbage and doesn't take 10 seconds.
• Cost awareness matters from day one. Track your API costs from the very first call. It dictates your design.
• One user is a success. If you can make one person's life measurably easier, you've shipped something real. Everything else is scale.

The Real Measure of Shipping

Here's the thing nobody tells you about side projects: the hardest part isn't the technology. It's the sustained interest. You have to keep showing up for three weeks, not three days, and that's only possible if you're not drowning in the boring parts.

Every time I was tempted to spend a weekend building something clever and infrastructure-heavy, I asked myself one question: "Can this be done with an API call?"

If yes, that's the answer. Ship it.

The three projects I've launched in the past eight weeks have each taken between 30 hours and 60 hours of total work. That's the sweet spot for a side project. Any more, and you're building a company. Any less, and you're just playing.

And for the infrastructure — use what works, spend a minute choosing your API provider, and then never think about it again. That's what I did with tai.shadie-oneapi.com. One endpoint, multiple models, pay per request. I think about it for about 3 minutes per month. The other 99.9% of my time goes into the actual product.

That's the trade-off that allows me to ship. Not because I'm a genius, but because I stopped doing things that don't matter.