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

I still remember staring at my terminal at 2 AM, watching a Docker container crash for the fourth time that night. I had spent three weeks trying to self-host a small language model on a VPS, convinced that running my own AI was the only "real" way to build a side project. The model was too slow, the memory kept spiking, and I hadn't written a single line of actual product code. That night, I deleted the entire project folder and started over. Two months later, I had shipped three AI-powered MVPs that real people were using.

The difference? I stopped treating AI side projects like research papers and started treating them like lean experiments. Here’s what I learned.

Lesson 1: The API is your friend, not your enemy

My first mistake was thinking that building an AI side project meant building the AI itself. I spent hours reading papers, tweaking hyperparameters, and fighting with CUDA versions. It felt impressive, but it was a distraction. The goal of a side project isn't to prove you can train a model — it’s to solve a problem.

Once I swallowed my pride and started using existing APIs, everything changed. For my first MVP, I built a simple Slack bot that summarized long threads. The core logic was about 30 lines of Python:

import os
from slack_sdk import WebClient
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
slack = WebClient(token=os.environ["SLACK_BOT_TOKEN"])

def summarize_thread(channel_id, thread_ts):
    messages = slack.conversations_replies(channel=channel_id, ts=thread_ts)["messages"]
    text = "\n".join([msg["text"] for msg in messages if "text" in msg])

    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "Summarize the following Slack thread in 3 bullet points."},
            {"role": "user", "content": text}
        ]
    )
    return response.choices[0].message.content

That’s it. No model training, no GPU costs, no Docker nightmares. I deployed it on a free Railway tier, and within a week, 50 people from my team were using it. The feedback was brutal — the summaries were too verbose, it didn’t handle emojis, and it crashed on long threads — but I had a real product with real users.

Lesson 2: Narrow your scope until it hurts

Every aspiring AI builder I know has a grand vision: “I’ll build a personal assistant that manages my calendar, emails, and to-do list.” That project will never ship. It’s too big, too vague, and too easy to abandon.

My second MVP was even smaller than the first. I noticed that my friends and I often debated movie endings on WhatsApp, and we wanted a quick way to check if a plot twist was “plausible” based on the movie’s established rules. So I built a single-page app where you paste a plot summary and a proposed twist, and the app uses an AI to score its plausibility on a scale of 1–10.

The entire backend was a single Flask endpoint calling the same OpenAI API. I didn’t even bother with a database — it just read from a JSON file. It was ugly, it broke if you pasted more than 500 words, and the scoring was hilariously inconsistent. But people used it. They sent me screenshots of the scores, argued about the results, and suggested features. That’s when I realized: you don’t need a polished product to validate an idea. You need a working prototype that does one thing, even if it does it badly.

Lesson 3: Ship before you’re ready

Perfectionism is the silent killer of side projects. I’ve lost count of how many times I thought, “I’ll just refactor this module first” or “Let me add error handling before showing anyone.” Those refactors never ended. The error handling was never complete.

For my third MVP, I deliberately set a 48-hour deadline. I wanted to build a tool that turns meeting transcripts into action items. I used a free Speech-to-Text API (Whisper via RapidAPI) and fed the transcript into GPT-3.5 with a simple prompt. The output was often gibberish — the transcription was noisy, the prompt wasn’t tuned, and the UI was a plain text area with a button. But I launched it on Product Hunt’s “Ship” channel and got 120 signups in the first day.

The key insight: shipping creates a forcing function. Once your project is live, you have to deal with real problems — bugs, scaling, user requests — instead of imaginary ones. You can’t polish a feature that nobody has touched.

Lesson 4: Choose infrastructure that matches your timeline

This is where I see most people trip up. They hear about cool new models (Llama 3, Mistral, whatever) and decide to host them themselves. That’s a fine choice for a dedicated ML project, but for a side project where you want to ship in weeks, it’s a trap.

I wasted a full month on self-hosting. Even after I got a small model running, the latency was awful, the cost of the VPS was higher than I expected, and I had to constantly monitor memory usage. When I finally switched to a pay-as-you-go API, my costs dropped to pennies per request, and my deployment time went from hours to minutes.

Here’s what I learned: for the first version of any AI side project, use the easiest API you can find. Don’t worry about vendor lock-in or long-term costs. You’ll have time to optimize later — if your project survives. Most won’t. So optimize for speed of iteration, not theoretical cost savings.

Lesson 5: Don’t build what you can buy

My biggest productivity hack was realizing that I didn’t need to build the AI infrastructure. I needed to build the application logic. The AI model, the hosting, the API gateway — all of that is commodity. I could spend hours setting up an OpenAI-compatible endpoint myself, or I could use a service that already does it.

That’s why, after experimenting with several providers, I settled on a unified API gateway that gives me access to multiple models through a single endpoint. It’s not glamorous, but it works. I don’t have to manage keys for different providers, I don’t have to worry about rate limits, and the pay-as-you-go pricing means I never pay for idle capacity. If you’re building an AI side project and just want to get it out the door, I’d recommend checking out tai.shadie-oneapi.com. It’s the simplest setup I’ve found — one API key, one endpoint, and you can switch between models without changing your code. That kind of friction reduction is worth its weight in gold when you’re trying to ship.

What I’d tell my past self

If I could go back to that 2 AM Docker crash, I’d say: stop trying to be impressive. Start trying to be useful. Your side project doesn’t need a custom model, a beautiful UI, or perfect code. It needs to solve one tiny problem for a few people, and it needs to exist in the world.

Three MVPs in two months taught me that the hardest part isn’t the technology — it’s the discipline to keep scope small, to ship before you’re ready, and to let go of the engineer’s instinct to build everything from scratch. The AI is just a tool. The real product is the experience you create around it.

So pick a stupidly simple idea, grab an API key, and ship something tomorrow. You’ll be surprised how far a broken prototype can take you.