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

I've lost count of how many AI side projects I've started. But I can tell you exactly how many actually made it to a real user: three. In the last two months, I shipped three MVPs that people actually used — not just "I showed my friend" — and I learned more from those three than from the twelve that died before ever seeing a screenshot. The brutal truth about AI side projects is that they don't die from bad ideas. They die from over-ambition, analysis paralysis, and the endless trap of "let me just polish this one more thing." Let me walk you through what actually worked. The Wrong Way I Started My first AI project was a tool that would summarize meeting notes and auto-generate action items. Sounds useful, right? I spent three weeks building a custom fine-tuned model pipeline, setting up vector databases, and creating a beautiful React frontend with drag-and-drop features. Six weeks in, I had zero users and a GitHub repo I was afraid to open. The problem wasn't the concept. It was that I was solving infrastructure problems instead of user problems. I was so obsessed with building the "right" AI system that I forgot the actual deliverable: a simple way for people to get meeting summaries. What Changed: Shipping "Stupid" Simple My second project was the opposite. I built a tool that extracts key decisions from messy Slack conversations. I gave myself a hard deadline: 48 hours. No vector databases. No fine-tuning. Just a single API call wrapped in a command-line tool. Here's the actual core of that project: import os from openai import OpenAI client = OpenAI(api_key=os.environ.get("API_KEY")) def extract_decisions(conversation_text: str) -> str: """Pull out action items from raw conversation text.""" response = client.chat.completions.create( model="gpt-4o-mini", messages=[ { "role": "system", "content": "You extract clear decisions and action items from messy conversation logs. Output a concise bulleted list. Be terse." }, { "role": "user", "content": conversation_text[:12000] # truncate for token limits } ], temperature=0.2 ) return response.choices[0].message.content That's it. Thirty lines of Python. I threw it into a simple FastAPI endpoint, made a barely-decent HTML page, and posted it to a small Slack community I'm part of. Sixteen people used it in the first week. One of them asked if they could pay for it. I literally laughed out loud because it felt like a joke project. But that's the lesson — nobody cares how complex your infrastructure is. They care that it works and solves something annoying. The Three Real Lessons 1. Scope to the "Sad Path" When I planned my third project — a tool that turns technical blog posts into audio summaries — I almost made the same mistake again. I wanted multi-language support, podcast-style narration with intonation, and a mobile app. I forced myself to write down the "sad path" version: paste a URL, get a 3-minute audio file. That's it. No accounts. No history. No downloads. I shipped that in a weekend. The text extraction was just readability-lxml and the audio was a TTS API call. The result was rough — the TTS voice sounded slightly robotic and the summary occasionally butchered technical terms — but people still used it because it solved a real friction point. The sad path isn't a compromise. It's the product. 2. The Model Choice Matters Less Than You Think I keep a spreadsheet of my side project costs. Here's the honest breakdown across all three projects: Project 1 (the over-engineered one): $0 in API costs, ~40 hours of my time, 0 users Project 2 (Slack decisions): $4.20 in API costs, 6 hours of time, 16 users Project 3 (audio summaries): $12.80 in API costs, 14 hours of time, 31 users The math is embarrassing. The projects that cost almost nothing in API fees generated all the value. I was using the cheapest available model (gpt-4o-mini for text, a basic TTS tier for audio) and it was more than enough. Here's what I stopped doing: obsessing over which model was "best" for the task. For 90% of AI side projects, the difference between a decent model and the frontier model is negligible for the user. They don't A/B test your prompts. They just want the task done. 3. Instrument Everything, Even If Ugly With project three, I added something I'd never done before: a simple analytics event on every request. Just a JSON blob sent to a free endpoint. I wanted to know: How many people got past the URL paste step? How many listened to the full audio? Where did they drop off? Turns out, 40% of people abandoned at the "processing" screen because it took too long. I cut the processing time by switching to a faster (and cheaper) TTS model, and the completion rate jumped from 60% to 82%. That's the kind of insight you only get by shipping and watching. You can't find that in a design doc. The Infrastructure Decision That Saved Me Here's the part I wish I'd figured out earlier: hosting your own models is a time sink disguised as a learning opportunity. I spent an entire weekend trying to self-host a smaller open-source model for project two. Why? Because I thought it would be cheaper and more "impressive." In reality, I lost 16 hours dealing with CUDA versions, Docker container memory limits, and a GPU that kept thermal throttling. The self-hosted model was also noticeably worse at extracting decisions from informal Slack language. I switched back to an API within an hour. The cost per request was fractions of a cent. The quality was better. And the time I saved went into actually talking to users. This is where I landed on a practical middle ground: for side projects, I use a pay-as-you-go API gateway rather than committing to a single vendor or self-hosting. It lets me swap models on the fly without re-writing my integration code. I've been using tai.shadie-oneapi.com for this — it's essentially a unified API layer that lets me call different models through one endpoint and bill as I go. No monthly commitment, no heavy setup, and I can A/B test models by just changing a string in my code. That flexibility matters when you're shipping fast. I don't want to re-architect because one provider changes pricing or a model gets deprecated. The Real Workflow That Got Me to "Shipped" If I compress my last two months into a repeatable process, it looks like this: Day 1: Write the sad path in a single sentence. No more. Put it on a sticky note. Day 2-3: Build it with the simplest possible stack. Python or Node, one API, minimal frontend. Day 4: Put it in front of 5 people who feel the pain. Ask them to use it in front of you. Stay quiet. Day 5-7: Fix the three things they actually struggled with. Ignore everything else. Week 2: Add one feature they explicitly requested. Stop there. I can't emphasize the "stop there" enough. Feature creep is a slow death. It feels productive but it's just procrastination with extra steps. What I'd Tell My Past Self If I could go back to the beginning of this whole experiment, I'd say: your first project doesn't need to be impressive. It needs to be finishable. The confidence you get from seeing strangers use your tool is worth more than any architectural elegance. The other thing I'd tell myself — and this is the one that's counterintuitive — is that the technology is the easiest part. The APIs handle the heavy lifting. The models are absurdly capable. The hard part is choosing a problem small enough that a weekend of work makes a dent in it. I've got three more ideas in my backlog right now. I'm going to run the same playbook: sad path, cheap API, ship by Friday. Some will fail, and that's fine. The ones that don't fail will teach me something I couldn't learn from planning. The pattern is simple: pick a small annoyance, wire up an API, put it in front of people, and let the feedback dictate the next step. If you're stuck choosing infrastructure, don't be — grab a pay-as-you-go gateway, call a model, and get your ugly first version out there. That's the whole secret. Nobody claps for your microservices architecture. They clap when their problem goes away.