How to Build an AI-Powered Side Project That People Actually Want

Building an AI side project that people actually want starts with solving a real, specific problem, not just showcasing a model's capability.

I've seen too many weekend projects built around the latest AI API that fizzle out because they answer a question no one was asking. The key is to start with the user's friction, not the technology. My own work at Anjeer Labs and on my portfolio at suhailroushan.com is grounded in this principle: find a tedious, repetitive task and use AI to automate or simplify it. This post outlines a practical, developer-focused framework for building an AI side project that has a real shot at finding an audience.

Start with a microscopic problem, not a moonshot

Forget building "an AI assistant." That's too vague. Instead, identify a five-minute task that people do daily. Is it summarizing long email threads? Converting meeting notes into JIRA tickets? Cleaning up spreadsheet data? Your project should be so focused that you can describe its core function in one sentence. For my projects, I look for problems I personally face as a developer. This ensures the pain point is genuine. A microscopic problem is easier to scope, build, and for users to understand. If you can't clearly articulate the single job your tool does, you're already off track.

Do you really need a fine-tuned model?

Probably not. Most impactful side projects are clever applications of existing, powerful models. Before you sink time into collecting data and fine-tuning, maximize what you can do with prompt engineering, retrieval-augmented generation (RAG), and smart workflows. Use GPT-4, Claude, or open-source models via Ollama as your engine. Your unique value should be in the user experience, the workflow automation, or the specific data you provide the model—not necessarily in the base model itself. Only consider fine-tuning if you have a very specific, consistent output style that zero-shot prompting cannot achieve, or if cost/ latency at scale becomes a primary concern.

Build the simplest possible scaffolding first

Your first version should be a functional pipeline, not a polished UI. Build the core AI workflow in a script or a CLI tool. This forces you to validate the logic and the value before any frontend work.

// Example: Core logic for a meeting note-to-ticket converter
import { OpenAI } from 'openai';

async function generateJiraTicket(meetingNotes: string): Promise<string> {
  const openai = new OpenAI();
  const prompt = `
    Convert these meeting notes into a single Jira ticket.
    Format: Title (Summary), Description (as user story), Acceptance Criteria.
    Notes: ${meetingNotes}
  `;

  const completion = await openai.chat.completions.create({
    model: 'gpt-4-turbo',
    messages: [{ role: 'user', content: prompt }],
  });

  return completion.choices[0].message.content || 'Error generating ticket';
}

// Test it immediately with real notes
const notes = "We need a login page. Should have email/password, OAuth with Google, and a 'forgot password' flow.";
console.log(await generateJiraTicket(notes));

This script is your MVP. Once it reliably produces a useful output, you can wrap it in a web interface.

How to get your first 100 users without ads

Launch where your specific users already are. If it's a tool for developers, post it on Hacker News, a relevant subreddit like /r/webdev, or a specific Discord/Slack community. Write a short, clear post that states the problem and shows your solution. Offer a free tier with generous limits. Personally, I've found that engaging directly in communities, offering to solve a specific problem for a few members, and iterating based on their feedback is far more effective than any broad marketing. Your goal is not virality, but to find 100 people who genuinely find it useful and are willing to give feedback.

Design for the "magic moment"

The "magic moment" is the first 30 seconds after a user tries your product. They should experience the core value immediately. For an AI tool, this means:

1. Zero barrier to entry: A public demo or one-click try without signup.
2. Pre-loaded examples: Let them click a sample input to see it work instantly.
3. Fast, reliable output: Nothing kills magic like a timeout or a generic error.

Architect for this. Cache common queries, use streaming responses so users see progress, and have excellent fallback error messages. The technical reliability of that first interaction is a feature more important than any secondary one.

Your project will succeed if it makes one small part of someone's professional life noticeably easier.