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

I've lost count of how many AI side projects I started and abandoned. The pattern was always the same: a spark of excitement, two weeks of frantic coding, then the slow fade into yet another half-finished repo collecting dust on GitHub.

But something changed in the last two months. I shipped three AI-powered MVPs to real users. Not all of them made money, but every single one taught me something about what it actually takes to go from "cool idea" to "working product." Here's what I learned.

The brutal truth about AI side projects

When I started my first real AI project back in February, I had grand ambitions. I was going to build a content summarizer that would pull articles from any URL, analyze sentiment, and generate Twitter threads. I spent three weeks obsessing over the perfect prompt engineering, containerizing the whole stack with Docker, and setting up a complex pipeline using LangChain and Pinecone.

Then I showed it to a friend. "Can I just paste a link?" she asked. I had built an entire orchestration layer, but the input field was buried behind two authentication screens. The project died that weekend.

Here's the thing I keep rediscovering: AI side projects fail not because the technology doesn't work, but because we over-engineer before we have users.

The three MVPs that actually shipped

After that failure, I changed my approach. I decided to ship something—anything—every two weeks. No matter how ugly. No matter how incomplete. The goal was to have a URL someone could visit and use.

MVP #1: A dead-simple blog title generator

I built this in a single afternoon. The entire frontend was a text box and a button. Backend? A single Node.js endpoint that called OpenAI's API with a prompt like: "Generate 5 catchy blog titles about [topic]."

Here's the code that powered it (I've simplified it, but this is the gist):

import express from 'express';
import OpenAI from 'openai';

const app = express();
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

app.post('/generate', async (req, res) => {
  const { topic } = req.body;
  const completion = await openai.chat.completions.create({
    model: 'gpt-3.5-turbo',
    messages: [{
      role: 'user',
      content: `Generate 5 blog title ideas about ${topic}. Return them as a JSON array.`
    }]
  });

  const titles = JSON.parse(completion.choices[0].message.content);
  res.json(titles);
});

app.listen(3000);

That's it. No database. No authentication. No vector embeddings. I deployed it to Railway with a free tier, shared the link on Reddit, and got 47 unique visitors in the first day. The design was hideous—I used default Bootstrap with zero customization. But people used it.

Lesson: Start with the thinnest possible wrapper around an LLM. Everything else can wait.

MVP #2: An email digest that actually worked

My second project was more ambitious: an AI-powered newsletter that would summarize Hacker News stories every morning. I wanted it to be personalized based on the user's interests.

I made a critical mistake here. Instead of just sending raw summaries, I tried to build a recommendation engine with user profiles, a database of past interactions, and a feedback loop. Two weeks in, I had a beautiful database schema and zero emails sent.

I pivoted hard. I deleted the database entirely. Instead, I created a single Google Form where users could submit their email and three keywords. Every morning, a cron job on GitHub Actions would scrape the top 30 HN stories, filter by those keywords using simple string matching (not AI!), and send a plain-text email via SendGrid.

The AI part? Only the summary generation. I used GPT-4 to rewrite each matching story into a 2-sentence digest. By stripping away all the complexity, I shipped in 3 days.

Lesson: Use AI only where it creates clear value. For everything else, use the dumbest possible solution.

MVP #3: A chat interface for my personal notes

This one was the most technically interesting. I wanted to be able to ask questions about my own writing—blog posts, journal entries, meeting notes. The obvious approach was RAG (retrieval-augmented generation) with embeddings and a vector database.

But I didn't have the patience to set up Pinecone or Weaviate. Instead, I did something stupidly simple: I kept all my notes as plain text files in a folder. When a user asked a question, the app would read every file, split them into chunks, and use a simple TF-IDF similarity search (via the natural npm library) to find relevant chunks. Then it would feed those chunks into GPT as context.

Was it fast? No. For a 50-file corpus, each query took about 4 seconds. But it worked. I got a working chat interface for my notes in one evening. Later I added caching and moved to a proper vector search, but only after I had confirmed people actually wanted to use it.

Lesson: Prototype with brute force. Optimize only when you have evidence that performance matters.

The infrastructure reality check

By the time I shipped those three MVPs, I had burned through about $80 in API credits. OpenAI's API isn't cheap when you're experimenting, and I was paying per token for every failed prompt and every test.

I also realized I didn't want to manage my own models. Hosting a Llama 2 variant on a VPS? Too much friction. Running a local embedding model? Not worth the DevOps headache for a side project. What I needed was a pay-as-you-go solution that let me use any model without worrying about infrastructure.

That's when I discovered something that changed my workflow: an API aggregator that gives me access to dozens of models—GPT-4, Claude, Mistral, and a bunch of open-source ones—all through a single endpoint. No separate accounts. No managing GPU instances. Just one API key and a credit balance that I top up when needed.

I've been using tai.shadie-oneapi.com for my last two projects. It's not a sponsored plug—I literally found it while searching for "one API to rule them all" for AI models. The pricing is transparent (pay per token, no monthly commitments), and it saved me from having to make infrastructure decisions before I even knew if my idea would work.

For a side project, the ability to swap models without changing code is huge. I can prototype with GPT-3.5-turbo to keep costs low, then switch to GPT-4 or Claude 3.5 Sonnet for the final version with zero code changes. The API is OpenAI-compatible, so my existing code worked without modification.

What I'd do differently next time

After shipping three MVPs, here's my playbook for the next one:

1. Ship in one day. If I can't have a working prototype by bedtime, the idea is too complex. Scale it down.
2. No database until Day 3. Everything can live in memory or a JSON file for the first 48 hours.
3. One API call per user action. No chaining five LLM calls together. That's how you burn $50 before breakfast.
4. Manual processes are fine. If I don't have 100 users, I can manually curate, manually review, manually send emails. Automate when it hurts.
5. Use a model aggregator from day one. I wasted hours setting up separate accounts for OpenAI, Anthropic, and Replicate. One key is all you need.

The biggest shift in my mindset was realizing that an AI side project isn't about building the most technically impressive system. It's about finding a question that people have, and answering it with the least amount of code possible. The AI is just a tool—a really powerful one—but it's not the product.

My blog title generator had 47 users on day one. Three weeks later, it had 0. But that's fine. I learned more from that failure than from any of the projects I spent weeks architecting and never released.

Next time you have an idea, try this: before you install LangChain, before you set up a vector database, before you write a single test—build the absolute simplest version that could possibly work. Put it on the internet. See if anyone cares.

Then, and only then, start thinking about scale.