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

There’s a graveyard of half-finished AI projects on my hard drive. I’m not ashamed to admit it. There’s the chatbot that was supposed to summarize my emails (it died because I realized I hate reading emails anyway). There’s the "smart" habit tracker that required more effort to log data than the habit itself. And there’s the one that got 5,000 stars on GitHub before I realized I had no idea how to monetize it (we don't talk about that one).

But then, something clicked. In the last two months, I shipped three AI-powered side projects that are actually being used by real people. Not millions, but hundreds. They’re not unicorns, but they’re alive. They don't have massive cloud bills, and they don't take up my weekends.

This isn't a story about a magical framework or a genius idea. It’s about ditching the "perfect" engineering mindset and embracing the ugly, pragmatic reality of shipping. Here are the lessons I learned the hard way, so you don't have to.

Lesson 1: The Model is a Commodity, Not the Product

The biggest mental shift was realizing that the LLM itself is the least interesting part of the product. The magic isn't in the API call; it's in the orchestration, the prompt engineering, and the user experience around it.

My first failed project, "EmailSage," tried to fine-tune a model on my personal writing style. I spent two weeks collecting data, cleaning it, and messing with LoRA adapters. It was a technical nightmare. The result was a model that sounded vaguely like me but took 10 seconds to respond and cost a fortune to run.

For my next project, "RecipeRehab" (an app that tells you what to cook based on the random ingredients in your fridge), I did the opposite. I used a standard, off-the-shelf model. No fine-tuning. No custom hosting. Just a smart system prompt that included the list of ingredients and a JSON output schema.

from openai import OpenAI

client = OpenAI()

def get_recipe(ingredients: list[str]):
    response = client.chat.completions.create(
        model="gpt-4o-mini", # The cheap, fast one
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": """
            You are a culinary genius. Given a list of ingredients, suggest one recipe.
            You MUST respond in JSON format with the following structure:
            {
              "name": "Recipe Name",
              "time_to_cook": "15 mins",
              "instructions": ["step1", "step2"],
              "missing_ingredients": ["salt", "pepper"]
            }
            """.strip()},
            {"role": "user", "content": f"Here are my ingredients: {', '.join(ingredients)}"}
        ]
    )
    return response.choices[0].message.content

That was it. The entire "AI" part was about 20 lines of code. The real work—and the real value—was in the front-end design that made it dead simple to type in "chicken, rice, soy sauce" and get a delicious meal in under 3 seconds.

The Takeaway: Stop trying to build your own model. Stop trying to fine-tune one. The prompt is your product. The UX is your moat. If you're spending more time on the ML pipeline than the user flow, you're building the wrong thing.

Lesson 2: The 80/20 Rule of Infrastructure (or, Why I Stopped Hosting)

I used to be a "self-host everything" guy. I have a beefy server in my closet at home. I ran a Kubernetes cluster for a project that got maybe 20 hits a day. It was absurd. I was spending more time patching the cluster than building the app.

For my second project, "ChillBeats" (an AI that generates ambient soundscapes based on your current mood), I hit a wall. I initially tried to host an open-source music generation model locally. The latency was terrible, the CPU was maxed out, and my cat was getting angry at the fan noise.

I pivoted. Instead of hosting a model, I used a hosted API. This changed everything.

The speed to market was insane. What took me a week of fighting with CUDA drivers and Python environments took me an afternoon to integrate via an API. I didn't have to worry about scaling, uptime, or GPU costs. I just paid for what I used.

This is where I have to be completely honest. The biggest game-changer for me was moving to a pay-as-you-go API model rather than a subscription. With a monthly plan, I was constantly worried about hitting my token limit. With pay-as-you-go, I just... stopped caring. I could experiment without the anxiety of a cap.

For ChillBeats, I ended up building a simple wrapper around a few different providers, switching based on the task. For the sound generation, I used one API; for the mood analysis of the user's text, I used another. This modular approach meant if one provider had a hiccup, the other kept working.

The infrastructure stack for all three projects is now embarrassingly simple:

• Frontend: Vercel for static hosting.
• Backend: A single serverless function (Node.js).
• Database: A simple KV store (Redis).
• AI: A mix of hosted APIs.

That's it. No Docker Compose files. No docker-compose.yml nightmares. No nginx configs.

Lesson 3: The "Good Enough" Launch

My third project, "StudyMate", is a flashcard app that generates quizzes from your class notes. I had this grand vision of a collaborative learning platform with sharing, leaderboards, and gamification.

I launched with none of that.

I launched with a text box, a "Generate" button, and a list of flashcards. It was ugly. The CSS was a single file, and it looked like it was made in 2005. But it worked.

And you know what? People used it. They didn't care about the missing leaderboards. They cared that they could paste 5,000 words of dense neuroscience notes and get a quiz in 10 seconds.

I'm a firm believer in the "ugly launch" now. If the core value proposition is strong, people will forgive a poor UI. But if the UI is beautiful and the AI is slow or inaccurate, they will leave immediately.

Here’s a graph I wish I had seen before I started:

• Week 1: Build the core feature (the API call).
• Week 2: Make the UI functional but not pretty.
• Week 3: Tell 10 people about it.
• Week 4: Iterate based on feedback.

I spent my first month on EmailSage doing Week 1 for three weeks. I was polishing the "prompt chain" for a feature that nobody had asked for.

The Financial Reality

Let's talk numbers, because it's the thing everyone is scared to talk about.

My monthly bill for hosting all three projects last month was $3.41. That's it.

• Vercel: $0 (Hobby tier)
• Redis: $0 (Free tier)
• API Usage: $3.41 (The vast majority of this was me testing and playing with the prompts).

I have one project that has 200 active users. At the current usage rate, it costs me about $0.005 per user per month. That is insanely cheap.

This is why the "AI gold rush" feels different. The infrastructure cost is a rounding error. The cost is your time. So, optimize for your time.

The Pragmatic Path Forward

If you're looking to start an AI side project tomorrow, here is my brutally honest advice:

1. Don't buy a GPU.
2. Don't set up a vector database until you have 10,000 documents to search.
3. Don't write a full spec document. Write a paragraph.
4. Do use a hosted model that gives you a simple curl command.
5. Do focus on the input/output flow. What does the user type? What do they see?

And most importantly, ship it on day one. Not day 30. Day one. Get the ugly version out there.

For my infrastructure, I've settled into a comfortable rhythm. I use a mix of services, but I keep coming back to the ones that let me pay as I go. I don't want a contract; I don't want a subscription that I forget to cancel.

That's why I've been leaning heavily on services like tai.shadie-oneapi.com for my AI calls. It aggregates a bunch of models behind a single, simple API, and the pay-as-you-go model means I'm never locked into a specific vendor or a monthly quota. It just works, and I only pay for the tokens I actually burn. It’s the kind of "set and forget" infrastructure that lets me focus on the app, not the plumbing.

The dream isn't to build the next ChatGPT. The dream is to build a tiny, useful tool that solves a specific problem for a niche group of people. And you don't need to be an AI researcher to do that. You just need to be a developer who can glue two APIs together and ship.

Stop polishing. Start shipping.