I Cloned a $30M App Twice With ChatGPT Work & Claude Cowork (One Prompt)
ChatGPT Work vs Claude Cowork: I cloned Cal AI, a $30M calorie tracker app, with one prompt in both. Build times, usage limits, and the skill that wrote it.
OpenAI released ChatGPT Work.
And it is very similar to Claude Cowork.
So, I compared Claude Cowork and ChatGPT Work by building Cal AI, a $30M app used by millions of people to track their daily calorie intake.
Why build instead of clicking around?
Feature tours without doing anything do not help you. A real build does.
Look at this guy I found on Starter Story.
He copied two apps and made $30M combined.
The market is full of successful copies.
Pick a product you care about, then build a better version.
This sounds controversial, but it works.
To do that, we’ll use ChatGPT Work and Claude Cowork because;
ChatGPT Work has “sites”.
Claude Cowork has “artifacts”.
That’s why this will be a fair comparison.
So, I’ll use a reverse-engineering pipeline that I built earlier.
Let me explain how it works first.
What Is the Reverse Engineering Pipeline?
In one sentence: it researches the product you give it, trains a NotebookLM, gathers additional information, and turns everything into a single comprehensive prompt.
And this one big fat prompt will let you build anything you want.
Like I always do, I wrapped this up into a Claude Skill, so you can use it, using one single prompt.
Use the reverse-engineering pipeline on [any product] and write one big, detailed prompt for me.
So we’ll decode this $30M App, Cal AI, using the skill.
In the meantime, you’ll see how Claude Work and Claude Cowork create different apps from the same files, and we’ll compare those two together, but first, let me show you the results as usual.
What ChatGPT Work vs Claude Cowork Built From One Prompt
We built the same calorie tracker twice.
ChatGPT Work: Building Alma in 74 Minutes
First, here is the one ChatGPT Work created. It named the app Alma.
Alma is a desktop style web app with a 4 step onboarding.
You need to fill your age, height weight, target pick and your activity level.
Next, it calculates your calorie intake using the Mifflin-St Jeor Formula.
Then it drops you into a daily dashboard.
Claude Cowork: Building MacroLens in 52 Minutes
Next, we built the same app with Claude Cowork.
It named this one MacroLens.
MacroLens went mobile first.
The whole app lives in a phone shaped frame.
The onboarding runs 7 steps and asks a question ChatGPT skipped, how fast you want to lose weight, from a steady 0.25 kg per week to an aggressive 0.75.
The meal scan is the strong part.
I uploaded a photo of Turkish döner and it split the plate into ingredients, döner meat, pide, pepper, each with a gram slider and macros pulled from a USDA table.
It caps free users at 3 scans a day.
I created both apps using one prompt.
The Reverse Engineering Pipeline wrote this prompt for me by collecting information about Cal.AI and then created a notebooklm and assigned an agent to ask questions about how to build.
By the way, both apps came from one prompt.
The reverse engineering pipeline skill wrote that prompt for me in 5 minutes.
I’ll give you the prompt and the skill below. We’ll dig into ChatGPT Work and Claude Cowork as deep as we can on the way.
Let’s start. Here is the skill and the prompt.
