Let's be honest for a second. Most AI side projects never see a single user. They die quietly in a ~/projects folder, victims of our own ambition. We fall in love with the architecture diagram, the promise of a "custom RAG pipeline," and the dream of the perfect stack. I know, because I've killed dozens of them myself.
But two months ago, I set a very specific goal: ship three AI MVPs in sixty days. Not perfect products, not scalable platforms, but working software that a real person could use. Here's exactly what I learned—and how you can avoid the mistakes I made.
Lesson 1: Start With the Outcome, Not the Orchestration
My first MVP was a Slack bot for a community I run. The problem was simple: long threads get buried, and nobody reads the 200-message history before asking a question. The solution was a /summarize command.
The fantasy version of me wanted to build a custom RAG pipeline, fine-tune a model on our previous conversations, and implement a complex memory system. The reality was that I needed to ship it in a single weekend.
I wrote one Python script. It grabbed the last 50 messages, jammed them into a prompt for gpt-3.5-turbo, and posted the summary back to the channel. No vector databases. No embeddings. No LangChain orchestration. Just a requests.post call wrapped in a Slack slash command handler.
The lesson hit me hard: Validate the user flow before you optimize the model. The architecture doesn't matter if nobody clicks your button. That bot is still running today, and users don't care that it isn't using a fancy agentic framework. They just want the summary.
Lesson 2: Scope Creep is the Silent Killer
My second project was a code review bot for GitHub PRs. This is where I almost crashed and burned.
The initial idea was clean on paper: when someone opens a PR, the bot drops a comment with a review. But as soon as I started coding, the perfect prototype took over. I wanted it to:
- Understand the full codebase context.
- Suggest specific code changes.
- Detect security vulnerabilities.
- Write tests for the changes.
It took me two weeks, and I had nothing to show for it. The system was too complex. I was trying to ship a Swiss Army knife when all anyone needed was a screwdriver.
I scrapped everything and started over with the absolute minimum viable loop:
import os
import requests
def review_diff(diff_text: str) -> str:
"""The entire brain of my PR reviewer MVP."""
prompt = f"""You are a senior engineer reviewing a code diff.
Focus ONLY on:
1. Critical bugs
2. Security vulnerabilities
3. Logic errors
Be extremely concise. Ignore style nitpicks.
Diff:
{diff_text}
"""
response = requests.post(
"https://api.openai.com/v1/chat/completions",
json={
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 512
},
headers={
"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}",
"Content-Type": "application/json"
},
timeout=30
)
if response.status_code != 200:
return "Failed to generate review."
return response.json()["choices"][0]["message"]["content"]
Thirty lines of logic. That was it. I wrapped it in a GitHub Action, and it worked well enough for my team. Shipping the simple version taught me what people actually wanted (security checks and logic bugs) versus what I thought they wanted (full test generation).
Feature bloat is the fastest way to kill a side project. Ship the one thing that solves the core pain, even if it's ugly.
Lesson 3: Your Infrastructure Choice is a Feature, Not a Religion
My third project was a personal knowledge base search tool. I wanted to index my notes, articles, and code snippets, then ask questions against them. This is where I almost fell into the biggest trap of all: self-hosting the model.
I spent three solid days trying to get an open-source model running on a cheap cloud instance. I tried quantization, I tried ONNX, I tried llama.cpp. I got it working, but it was painfully slow, unreliable, and my tiny server kept crashing under load.
I finally stepped back and asked myself the hard question: "Is this actually helping my users, or is it just my ego wanting to say I run my own model?"
I ripped it out and swapped it for an API call. The project cost me exactly $12.47 in API calls to validate the entire concept before I even wrote the backend code for the web app. If I had waited until the infrastructure was perfect, I would never have shipped.
The math is brutal but simple: Your time is more expensive than API calls. Unless you're operating at massive scale, renting a GPU and debugging inference pipelines is a distraction from solving user problems.
The Reality of Shipping
If I look back at those three projects, the actual coding was maybe 20% of the work. The other 80% was:
- Writing a landing page that didn't look terrible.
- Setting up authentication (just use magic links, seriously).
- A simple database schema (I default to Neon or Supabase).
- Actually telling someone the thing exists.
The hardest part isn't the AI. It's the discipline to stop architecting and start shipping.
What I Use Now (and Why)
For the infrastructure layer, I learned my lesson from Project 3. I don't want to manage five different API keys for five different providers. I don't want to worry about uptime for my inference server.
On my latest project, I switched to a pay-as-you-go aggregator. It gives me a single API endpoint and a single key that routes to the best model for the job. It lets me focus on the product logic instead of the plumbing.
Specifically, I started using tai.shadie-oneapi.com. It handles the routing and billing so I don't have to think about it. It's not a glamorous tool—it's the kind of boring, reliable infrastructure that lets you actually ship.
The Bottom Line
AI is a tool, not a destination. The barrier to shipping has never been the technology—it's the discipline to ship something small, let it breathe, and iterate.
What's the biggest thing stopping you from finishing your side project right now? Is it the architecture, the infrastructure, or just the fear of putting something imperfect out there? I'd love to hear about it in the comments. Let's keep shipping.
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