Transcription
Have you ever wondered how someone with zero coding skills could build an app that makes $400,000 a month? Well, I just copied one of the app store’s most profitable apps using nothing but AI tools, and I got to say, it actually works. In this video, I’m going to show you exactly how I replicated a wildly successful rock identifier app that’s making over $400,000 monthly without writing a single line of code. You and I are going to go through the entire process step by step, from start to finish, from selecting the right AI tool to connecting payment processing so you can actually start monetizing your own creations as well.
But here’s what makes this video different from other “no-code” tutorials you’ve either seen or scrolled by: I didn’t just build a basic prototype or a simplified version. No, I created a fully functional clone with all the premium features of the original app, complete with Stripe integration for payments, and I did it just using one single AI tool that most people don’t even know about yet. Because most people think that building profitable apps requires either extensive coding skills or hiring expensive developers. No, cuz that’s what I thought too, until I discovered this approach.
Now, what I’m about to show you isn’t just theory; it’s a proven method that’s already working for others who’ve never written a line of code in their lives. I’ll break down the entire process into simple steps that anyone can follow. I’ll show you the exact single AI tool I used, which is currently the most powerful coding assistant available today, and I’ll also reveal how to set up the Stripe payment system that turns our app from just a simple project into a potential six-figure business. And I promise you, by the end of this video, you’ll have everything you need to build your own profitable app too, without any coding knowledge at all whatsoever.
This isn’t just about copying an app; no, it’s about understanding how the modern app economy works and how AI today is completely changing the game about who gets to participate in it. So if you’re ready to see how deep the rabbit hole goes, let’s go ahead and dive in.
The AI tool that will take care of everything for us today in this video is Lovable. With the special link in the description box below, you will get double the credits, so you’ll have enough credits to build your own app as well. Now we’re going to start with the core feature of this rock identifier app: instant identification through photo uploads. The idea is simple: you take a snap or upload a picture of a rock or a mineral, and then the app tells you exactly what it is. So here we’re going to go ahead and tell Lovable we will be building a rock identifier app, one that allows users to upload photos of rocks and minerals for instant identification. And just like that, it generates a clean interface where our users can upload images straight from their own device. There’s no clutter, just a smooth, functional user interface that gets us, like, a rock rolling.
Now, once a photo is uploaded, it’s sent to the Gemini API. The response: a primary identification with the rock’s common name, its scientific name, and a confidence score that tells users how accurate the result is. Now that alone is already powerful, but we’re not going to stop there. So we’ll follow up with: “Use Gemini API for the AI recognition system. It should be able to recognize the common and the scientific name of the rock or mineral (primary identification), have a confidence level, basic classification (whether it is igneous, sedimentary, or metamorphic for rocks, or mineral family), now this single prompt upgrades the app with geological classification.” Now it tells our users if a rock is igneous, sedimentary, or metamorphic, or the mineral family if it is a mineral. The confidence score here is now shown as a percentage, making results even clearer.
Now, in just a few steps, we’ve built a smart, responsive foundation for the rest of our app. And we’re not just identifying rocks either; we’re teaching our users what they are and how they fit into the story of the Earth. And this part, it’s only the beginning.
Now that we’ve got the basics down—uploading a rock, getting a name, and seeing its classification—it’s time to go ahead and level things up. We want to make this app more than just a surface-level identifier. So we’ll go ahead and expand the prompt: “It should also be able to identify physical properties like the following: hardness on the Mohs scale, luster (metallic, vitreous, earthy, etc.), typical color range, streak color, cleavage/fracture pattern, typical crystal structure (if applicable), as well as the rock’s formation process, common locations, collecting value, and some fun facts.” Now, after running that prompt, everything changes. The Gemini API now returns a deeper set of physical properties for each rock or mineral. So when a user uploads an image, they’re not just told what it is; they’re shown how it behaves in the real world. The app now identifies hardness using the Mohs scale, describes the luster type (whether it’s metallic, vitreous, or earthy), and also breaks down both its visible color range and its streak color. It even detects the cleavage and fracture patterns, and if the sample has a defined crystal structure, that shows up too. And the result? We now have an app that goes beyond just identification; it’s practically a pocket geologist. Whether someone is using it to study rocks for school, to explore the great outdoors, or just to satisfy their curiosity, they’re getting meaningful, educational, and even fun information with every scan on the app.
Here’s where things get smarter. Now, one of the biggest challenges in rock identification is that a single photo can often not tell enough of the full story. You might miss textures, patterns, or structural clues that are only visible from a different angle. Now, to solve that, we’ll add something quite powerful to the app: multiple-view analysis. So we will prompt Lovable with: “The application should have multiple-view analysis to allow users to photograph specimens from different angles for more accurate identification.” The app now allows users to upload several images of the same rock, capturing it from multiple perspectives. So instead of treating those images individually, the Gemini API analyzes all of them together, synthesizing the views into one result for improved accuracy. It’s a huge leap forward in identification power, especially for rocks with subtle features that don’t show up in just a single shot. And it doesn’t stop there either. Prompting also refined the interface to better support multi-image inputs, making the user experience cleaner and more intuitive. And with this upgrade, the app feels more like a real field tool—you know, one that sees rocks the way a geologist would, from every possible angle.
All right, so now that the app can identify rocks with impressive detail and accuracy, it’s time to give our users a way to organize and then preserve their discoveries too. So we’ll add a feature that turns quick scans into a lasting digital collection. The prompt we’ll use in Lovable is: “Add a collection management feature. Users should be able to save identified rocks to a personal collection with notes and location data.” And with that, the app now lets users save any identified specimen to their own personal library inside of our app. And every saved entry includes the full identification data from the Gemini API, timestamp, and an optional location tag that marks where the rock was found. That’s good to know, right? There’s even a notes field so users can jot down anything they want: field observations, reminders, or even just a story of how they came upon the rock, wherever they found the rock. Each collection is tied to the authenticated user account, so data stays organized and personal. And on the visual side, as you can see, saved rocks are displayed with thumbnail images and are clean, readable summaries of all the AI-generated details. This feature doesn’t just make the app more useful; no, it adds a lot more meaning too, especially for our users, because now they can build a searchable, sharable, and even sentimental record of their geological journey, one rock at a time.
As users build out their collections and dive deeper into rock identification, navigating all that data becomes essential. So the next upgrade is all about searchability. So let’s go ahead and prompt: “The next feature is for search filters. Allow users to find rocks by characteristics like color, texture, luster, etc.” And with that, a new search interface is added to the app. It’s clean, it’s fast, and it’s built for precision, because now our users can filter through their saved rocks or explore the broader database using specific attributes. So whether they’re looking for a metallic luster or a specific glassy texture or a certain rock type, the filters narrow things down instantly. You can search by color, luster, texture, rock type, and even hardness, making it easier to find exactly what you’re looking for without endless scrolling. And if the search performance isn’t quite where it needs to be, that’s not a problem at all. All we have to do is just some subtle additional prompting. This feature just doesn’t improve the user experience; it turns the entire app into a powerful reference tool. So whether someone’s managing a growing collection of rocks or using the app to study geology, they now have a smarter way to access the data that matters most.
Identification is powerful, but also understanding is what keeps users coming back to the app. And to transform the app into a true learning tool, we’ll add a new feature focused entirely on education. So let’s go ahead and use this prompt: “The next feature is educational content. Add information about geology, rock formation processes, and mineral properties.” Now this part creates a brand new section within the app, and it’s packed with curated educational content. Inside, our users find well-organized articles and visuals and infographics that break down key geological topics in a way that’s easy for our users to understand. So from the full rock cycle to the basics of crystal systems, even the science behind famous geological sites, it’s all right there. And whether someone’s brand new to geology, like myself, or just brushing up on their knowledge, this section gives us a place to go deeper. We’ve intentionally made the content beginner-friendly but rich enough to also be genuinely useful. It’s not just about knowing what kind of rock you found in your hand; it’s more about understanding how it formed, why this rock matters, and what makes it unique in the overall bigger story of Earth’s history.
Now this next feature brings real-world context into the experience. Instead of just identifying rocks after we’ve found them, the app can now help us know exactly what to look for based on where they are. And to make that happen, we’re going to go ahead and add this new prompt: “The next feature is location-based suggestions. Show likely rock types based on users’ current geological region.” Once our user gives permission, the app will grab their current location and will tap into geological data tied to that specific region. And from there, it generates a list of rock types most commonly found nearby, giving users location-specific suggestions before they even start searching. It’s really like having a local geologist in your pocket, pointing out what’s most likely under your foot. And behind the scenes, we use prompting to refine how regions are mapped to rock types, making the suggestions more accurate and relevant. The app now delivers smarter, more personalized results based on real-world geology. And this is actually a game-changer for outdoor discovery.
All right, at this stage, the app is packed with powerful features, but now let’s go ahead and make it more personal, and that starts with user authentication. We add user authentication simply by prompting: “Add authentication. Users should be able to register, log in, and log out.” Lovable handles the entire workflow, allowing our users to securely register, log in, and log out. Authenticated users can now save their rock collections, they can track their identification history, and access, of course, premium features tied to a Stripe subscription. Every user’s data is stored privately, with the database structured to isolate and then manage individual accounts.
Now next, we’ll shift focus to first impressions. To showcase what the app can do right from the start, we’ll go ahead and type: “Add a proper landing page to provide an overview of the best features we have.” And here Lovable generates a polished, professional landing page, and it’s complete with a hero banner and a call to action. There are animated icons and smooth transitions between each section. Each core feature that we’ve had so far—like rock identification, AI analysis, and educational content—it’s all highlighted in a clean and scroll-friendly layout. The landing page itself does more than just look good; it also helps to guide our users through everything the app offers before they even log in.
With authentication and a strong front door in place to our app, the app now feels complete, secure, and ready for real users. To turn this into a sustainable product that we can keep going, we’ll need a way to generate revenue. So we’ll add a premium subscription system, and we’ll do that with this prompt: “Now add a premium subscription using Stripe.” And here Lovable integrates full payment functionality using Stripe’s test mode. The $9.99 a month plan unlocks premium features like unlimited identifications, multi-angle uploads, collection cloud backups, and location-based suggestions—all gated and ready to scale. There were a few hiccups during setup, things like webhook issues and syncing problems between user plans, but that’s nothing a few follow-up prompts can’t fix. And all we have to do is type in: “Use a different Stripe method and fix webhook configuration.” And with those two prompts, just like that, everything will click into place. Subscribe users now have seamless access to premium features, while non-subscribers can see upgrade prompts built right into the UI.
Remember, as with any no-code project, not everything will work perfectly the first time. We ran into issues like blank results, non-selectable fields, and infinite loading screens. But with simple prompts like “fix this feature” or “try a different implementation,” Lovable will do it all by itself and will handle those problems with adequate solutions. Of course, with some trial and error, the app will become fully functional, visually clean, and yeah, monetized.
All right, we made it—from start to finish, from a few prompts to one complete product. We built a fully functional app using nothing but AI—no code, no shortcuts, just some smart thinking, smart tools, and solid execution. And if this doesn’t get your gears turning about what you and your imagination can do with AI and no-code, I don’t know what will. But I do know that we have a ton of other examples you can look to for inspiration, so do go ahead and watch our other videos. I’ll get in touch with you in the comments section below, and I’ll catch you at the next one. Thanks for watching.