I Built a SaaS in a Weekend with AI: Lessons Learned and What I'd Do Differently

The Challenge

Last month, I challenged myself: could I build and launch a functional SaaS product in a single weekend using only AI-assisted development?

Not a toy project. Not a demo. A real product that could serve real users.

Here's what happened.

Friday Night: The Idea

I'd been thinking about a problem I kept running into: managing the various descriptions I need for directory submissions, social profiles, and press kits.

Every directory wants a different format:

• 50-word short description
• 150-word medium description
• 500-word long description
• Bullet points for key features
• One-liner taglines

I'd been manually adapting these for every submission. What if there was a tool that stored your core product info and generated appropriate versions for different contexts?

The idea: Product Description Manager

A simple SaaS where you input your product details once, and it generates descriptions in various formats for different use cases.

Friday night, 9 PM: I fired up Cursor and got to work.

Friday Night (9 PM - 1 AM): Setup & Core Functionality

Tech Stack Decision

I went with what I know:

• Next.js 14 (App Router)
• Supabase (Database + Auth)
• Tailwind CSS (Styling)
• Vercel (Deployment)
• Claude + Cursor (AI assistance)

The Initial Prompt

My first prompt to Cursor:

I'm building a SaaS called "DescribeIt" - a tool for managing product descriptions.

Tech stack: Next.js 14 (app router), Supabase for auth and database, Tailwind for styling.

Features needed:
1. User authentication (Supabase auth)
2. Dashboard where users can create "products"
3. For each product, they input:
   - Product name
   - Core value proposition
   - Key features (list)
   - Target audience
   - Website URL
4. The app generates descriptions in different formats:
   - Tweet (280 char)
   - Short (50 words)
   - Medium (150 words)
   - Long (500 words)
   - Product Hunt tagline
5. Users can copy any description to clipboard

Start with the project structure and core data models.

What Worked

Within an hour, I had:

• Project scaffolding complete
• Supabase tables created
• Basic authentication flow working
• A functional (ugly) form for product input

The AI handled all the boilerplate brilliantly. Database schemas, auth setup, API routes – all the stuff that usually takes hours was done in minutes.

First Major Snag

The AI-generated Supabase configuration had hardcoded API keys. Bad practice, potential security issue.

Lesson 1: Always review generated code for security issues. AI doesn't naturally follow security best practices.

Fixed it by asking: "Update the Supabase configuration to use environment variables properly."

End of Friday Night

By 1 AM, I had:

• ✅ Working auth (sign up, sign in, sign out)
• ✅ Database structure
• ✅ Basic product CRUD operations
• ❌ No description generation yet
• ❌ Ugly UI

I could create products and save them. Halfway there.

Saturday Morning (9 AM - 1 PM): The AI Description Engine

The core feature was description generation. I needed to call an LLM to generate descriptions based on product info.

The Architecture

User Product Info → API Route → Claude API → Formatted Descriptions

Simple enough. I prompted Cursor:

Add a feature to generate descriptions using the Claude API.

When a user clicks "Generate Descriptions" on a product:
1. Collect the product info (name, value prop, features, audience)
2. Call Claude with a prompt to generate all description formats
3. Display the results
4. Allow copying to clipboard

Use streaming for better UX.

Second Major Snag

The generated code used an old version of the Anthropic SDK. Classic AI problem – training data cutoff.

Lesson 2: AI knowledge has cutoffs. Always verify library versions and check documentation for breaking changes.

I fixed it by pasting the current SDK documentation into the chat and asking it to update the code.

The Magic Moment

By noon, I had working description generation. Type in your product info, click a button, get five different descriptions.

I remember the feeling of clicking "Generate" and watching formatted descriptions stream in. This tool I'd wanted for months was actually working.

Prompt Engineering the Descriptions

The descriptions weren't great at first. Too generic, too boring.

I spent an hour iterating on the system prompt:

You are a world-class copywriter specializing in SaaS products. You write descriptions that are:
- Specific, not generic
- Benefit-focused, not feature-focused
- Conversational but professional
- Optimized for the specific platform (Twitter, Product Hunt, directories)

Avoid:
- Buzzwords like "revolutionary," "game-changing"
- Starting every sentence with "Our product"
- Generic claims without specifics

Lesson 3: The quality of AI output depends heavily on prompt quality. Invest time in your prompts.

Saturday Afternoon (2 PM - 7 PM): UI/UX Polish

The functionality worked. Now it looked terrible.

The v0 Detour

I'd heard about v0 by Vercel for UI generation. I tried it:

Create a dashboard layout for a SaaS product.
- Sidebar navigation (Dashboard, Products, Settings)
- Main area with a grid of product cards
- Each card shows product name, tagline, last edited date
- Add New Product button
- Clean, modern design with shadows and rounded corners

V0 generated beautiful components. But integrating them into my existing codebase was more work than I expected.

Lesson 4: AI-generated UI components don't always integrate cleanly. Sometimes it's faster to describe UI changes to Cursor directly.

I ended up asking Cursor to restyle the existing components:

Restyle this page to look more modern:
- Add card shadows
- Rounded corners (lg)
- More whitespace
- A sidebar layout
- Modern button styles
Keep the existing functionality, just make it look better.

This worked much better. Same functionality, much prettier.

Adding Polish

The last few hours of Saturday were spent on:

• Loading states (skeleton loaders)
• Error handling (toast notifications)
• Empty states ("No products yet, create your first one!")
• Responsive design (mobile-friendly)

I used the Sonner library for toasts, which Cursor knew about and integrated cleanly.

Saturday Night: The Launch Prep Scramble

By 8 PM Saturday, I had a functional product. Now I needed to actually launch it.

Quick Marketing Assets

I needed:

• Landing page
• Logo
• Screenshots
• Social media posts

Landing page: Asked Cursor to create a simple hero section with the value proposition.

Logo: Used an AI logo generator (took 5 minutes).

Screenshots: Actual product screenshots in a mockup tool.

Social posts: Asked Claude to write launch tweets.

Deployment

Vercel deployment was easy – connect repo, set environment variables, deploy. Took 10 minutes.

Buying the Domain

Grabbed a cheap .app domain. Set up DNS. Working site by 10 PM.

Sunday: Launch Day

I woke up Sunday determined to actually launch, not just have it sitting there.

Product Hunt Prep

I'd already submitted to Product Hunt earlier in the week (they have a scheduling feature). My launch went live Sunday morning.

Where I Shared

• Product Hunt (scheduled)
• Twitter/X (launch thread)
• Reddit (r/SideProject, r/SaaS)
• Indie Hackers (community post)
• HackerNews (Show HN)

Sunday Evening: Results

By Sunday night:

• Website visits: 340
• Sign ups: 47
• Product Hunt position: #14 for the day
• Reddit post: 45 upvotes

Not viral by any means, but real users using a real product I built in a weekend.

What I'd Do Differently

1. Spend More Time on the Idea

I jumped into building quickly. With more thought, I might have identified a better monetization angle or more compelling differentiator.

2. Test Earlier with Ugly Version

I spent hours on UI before validating that anyone wanted the core feature. Should have shipped ugly first, then polished.

3. Set Up Analytics from Day 1

I forgot analytics initially. Had to add PostHog after launch, missing early user behavior data.

4. Plan the Launch in Advance

I decided to do Product Hunt last minute. With more prep, I could have coordinated supporters and had a better launch day.

5. Build in Public

I didn't document the process until after. Building in public during the weekend would have created more content and engagement.

The AI Coding Reality

Here's my honest assessment of AI-assisted development:

What AI Does Well

✅ Boilerplate: Auth setup, CRUD operations, form handling ✅ Styling: CSS, Tailwind classes, responsive design ✅ Integration: Connecting APIs, setting up databases ✅ Refactoring: Cleaning up messy code, adding types ✅ Problem-solving: Debugging error messages

What AI Struggles With

❌ Novel architecture: Unusual requirements need human guidance ❌ Up-to-date knowledge: Library changes, new APIs ❌ Security: Doesn't default to secure patterns ❌ Performance optimization: Basic code, not optimal code ❌ Business logic nuance: Edge cases need human thinking

The Reality Check

AI didn't build this product. I built it with AI assistance.

I made hundreds of decisions:

• What features to include/exclude
• How the user flow should work
• What the UI should look like
• How to handle edge cases
• When to ship vs. keep polishing

AI was a 10x accelerator on execution. But the thinking was still mine.

Time Breakdown

Here's roughly how I spent the weekend:

Spread across Friday night to Sunday evening, with breaks.

Where Is It Now?

A month later:

• ~200 registered users
• ~30 weekly active users
• $0 revenue (free product for now)
• Considering adding premium features

Not a business yet, but a real product that people actually use. That's pretty cool for a weekend project.

Could You Do This?

Yes, with caveats.

You'll have an easier time if you:

• Have some technical background
• Know the tools you're using (or can learn quickly)
• Start with a simple, focused idea
• Accept "good enough" over perfect

You'll struggle if you:

• Try to build something too complex
• Spend too much time on features vs. launch
• Don't understand the code at all (debugging becomes impossible)
• Expect AI to make all decisions

The Weekend Project Checklist

If you want to try this yourself:

Before the Weekend

• Have a clear, simple idea
• Choose your tech stack
• Set up accounts (Vercel, Supabase, etc.)
• Have AI tools ready (Cursor, Claude)

Friday Night

• Project scaffolding
• Database setup
• Authentication working
• Basic data model

Saturday

• Core feature functional
• UI/UX acceptable
• Deployment working
• Basic landing page

Sunday

• Final bug fixes
• Launch on 2-3 platforms
• Share on social media
• Submit to directories

The Bigger Picture

Here's what excites me about all this:

A few years ago, this weekend project would have taken me months. Or I would have paid thousands to a developer.

Now, with AI tools and the right approach, a single person can build and ship meaningful products in days.

The barrier to creating software has never been lower.

The question isn't whether you can build things. It's what will you build?

Built something with AI? Submit it to our vibe-coded products directory and get discovered by our community of builders.