How AI App Building Transformed My Day into a $100k/mo Idea

Crafting an app that could potentially earn $100,000 per month felt like chasing a distant star, but watching someone harness AI app building to create a productivity tool in just one day shifted my perspective entirely. The Forest app, known for its impressive revenue, inspired this endeavor—a science-based focus tool designed to help users tap into mental states like creativity or deep concentration through binaural frequencies. As an observer, I followed this process closely, absorbing every step to understand how artificial intelligence can revolutionize app development. The individual spearheading this project had years of experience designing for top Australian companies, which lent credibility to the experiment. Their goal was ambitious: build the app without writing a single line of code, relying entirely on AI tools to plan, design, and execute. By the end, I realized this wasn’t just about speed—it was about precision, creativity, and leveraging technology to simplify complex tasks. The journey unfolded in clear stages, each one a lesson in AI app building that I’ll break down for you here. Let’s dive into the first step: planning the app’s features with AI.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.

Planning Features with AI: A Strategic Foundation

The process began with a structured approach to planning, a critical phase in AI app building that ensures clarity before diving into creation. The individual turned to an AI tool called DeepSeek to brainstorm features, demonstrating how even ideation can be enhanced through technology. They crafted a prompt with deliberate context: “Imagine you’re a product owner who is an expert in building productivity apps.” This set the stage for the AI to think strategically, aligning its suggestions with industry standards. They described the app as a science-informed tool that uses binaural frequencies—overlapping audio tones that influence brainwaves—to help users achieve states like focus or relaxation. Adding a “why” gave the AI more depth to work with: people are increasingly drawn to science-backed methods to boost productivity. The prompt ended with a request for feature ideas and a nudge to “think step by step” for optimal results. Watching DeepSeek respond was fascinating—it outlined user personas, considered core functionalities, and delivered actionable ideas like a frequency library, timed focus sessions, and a stats dashboard. This methodical planning showed me how AI app building can distill complex ideas into practical features.

Choosing Core Features: Building the App’s Backbone

Once DeepSeek provided a list of potential features, the next step in AI app building was selecting the ones that would form the app’s foundation. The individual sifted through the suggestions with a discerning eye, prioritizing elements that would deliver value to users. They settled on three key features: a binaural frequency library for easy access to different mental state-inducing tones, timed focus periods (like 25 or 60 minutes) for structured productivity, and a stats area to track user engagement with frequencies over time. This selection process wasn’t random—it was guided by an understanding of what makes productivity apps compelling. The library would cater to variety, the timed sessions to discipline, and the stats to motivation through progress tracking. Observing this taught me the importance of balancing user needs with technical feasibility in AI app building. With features chosen, the groundwork was laid, and the next phase—creating a wireframe—beckoned as a bridge between concept and creation.

Crafting a Wireframe: Visualizing the App’s Structure

Moving into the wireframe stage of AI app building, the individual emphasized its role as a blueprint for the app’s layout, even if it didn’t need to be overly detailed since AI would fill in gaps later. They used Figma, a design tool, though they noted simpler options like Google Slides could work too—just something to draw boxes and text. Starting with a left-hand menu, a common app design choice, they sketched out sections like a library for frequencies and categories for mental states such as creativity, focus, or calmness. Each menu item was meant to highlight benefits, showing how the app’s architecture could revolve around user outcomes. They worked iteratively, mentioning that collaborating with DeepSeek could refine this further if needed. The wireframe wasn’t about perfection but about providing a visual guide for the AI to interpret. Watching this process unfold, I saw how AI app building thrives on collaboration between human intuition and machine precision, setting the stage for a more detailed prompt creation.

Enhancing the Wireframe: Preparing for AI Interpretation

With the initial wireframe sketched, the individual refined it to make it AI-friendly, a nuanced step in AI app building that ensures the technology can interpret and expand on the design. They added placeholders in curly brackets—like {benefit category}, {subcategory}, {frequency}, and {description of use case}—instead of filling in every detail themselves. This was a clever move, as it allowed the AI to populate these fields with relevant data, such as specific frequencies or category descriptions, based on its knowledge. The wireframe now looked like a mix of structure and prompts, ready to be fed into DeepSeek for further elaboration. They explained that this approach saves time while leveraging the AI’s ability to handle specifics like binaural frequency science. Observing this, I learned how AI app building can offload detailed research to machines, letting creators focus on the bigger picture. The wireframe was now a scaffold, ready to support the next critical step: crafting a detailed prompt.

Writing the Prompt: The Heart of AI App Building

The prompt creation phase felt like the linchpin of AI app building, where clarity and specificity determine the quality of the final product. The individual aimed to generate a robust prompt for Lovable, an AI tool that builds fully functional apps. They started by asking DeepSeek for tips on writing effective prompts for Lovable, ensuring the AI could draw on prior knowledge for better results. Then they described the app again—a science-based productivity tool using binaural frequencies—with build notes outlining the left-hand menu, mental benefit categories, and features like a profile area, favorites, and stats. The wireframe images were exported as PNGs, labeled clearly, and uploaded to DeepSeek with a message: “Review these images and use them as a reference.” DeepSeek responded by filling in placeholders with specifics like “focus,” “relaxation,” and “creativity” for categories, making the prompt more detailed. Watching this, I saw how AI app building relies on iterative refinement—each layer of input sharpens the output, preparing it for the development phase.

Building the App with Lovable: From Prompt to Prototype

With the prompt polished, the individual moved into the development phase of AI app building, using Lovable to turn words into a functional app. They pasted the DeepSeek-generated prompt into Lovable’s interface and hit “generate.” The tool sprang to life, coding in real-time while displaying a preview on the right-hand side. Initially, it created a basic interface with sections for focus, relaxation, and stats, but it wasn’t perfect—errors popped up, as they sometimes do. Lovable’s “try to fix” feature impressed me; it self-corrected the mistakes, refining the code until a working prototype emerged. The app had a clean layout: a menu listing mental states, clickable categories, and a stats area. But it lacked key functionalities, like playing frequencies or favoriting options, which the individual noted as gaps to address. This phase showed me that AI app building isn’t flawless—it requires oversight and iteration, but the speed of getting a tangible product is unmatched.

Refining the App: Adding Missing Features

The app needed refinement, and the individual tackled this head-on, demonstrating the iterative nature of AI app building. They wrote additional prompts for Lovable, specifying features like playing binaural frequencies and adding a favoriting system tied to the profile area. For the frequency playback, they described a step-by-step process: users should click a category, select a time, and hear the tones through a synthesizer API that Lovable eventually integrated. The favoriting feature allowed users to save preferred frequencies, with those selections reflected in the stats dashboard. It took some back-and-forth—Lovable needed guidance to get the audio working—but the result was impressive: users could start a session, set a timer, and end it, with stats tracking their habits. Watching this refinement taught me that AI app building excels when paired with clear human direction, turning a rough prototype into a functional tool in mere hours.

Improving the User Experience: Polishing with Design

The final step in AI app building focused on enhancing the user experience (UX) and interface (UI), a phase where aesthetics and functionality converge. The app worked, but it felt basic, so the individual explored two paths: using Lovable to improve the design via prompts and manually optimizing it in Figma. They prompted Lovable to “improve the UX/UI design,” suggesting inspiration from apps like Headspace and requesting animations, micro-interactions, and smooth transitions. Lovable responded with softer colors, hover states, and subtle animations, but some changes—like low-contrast text—reduced accessibility. Dissatisfied, they turned to Figma, redesigning the interface with clearer typography, a more vibrant color scheme, and intuitive navigation. The final design was sleek and user-friendly, with a focus on accessibility and visual appeal. This dual approach showed me how AI app building can benefit from both automated and human-led design, ensuring the end product feels polished and professional.

Conclusion: The Power of AI App Building in Action

Reflecting on this journey, I’m struck by how AI app building empowers creators to turn ideas into reality with unprecedented speed and efficiency. In just one day, a fully functional productivity app emerged, complete with binaural frequencies, timed sessions, and user stats—all without writing a single line of code. The process wasn’t flawless; it required planning, iteration, and a keen eye for refinement, but the result was a testament to what’s possible when human creativity meets artificial intelligence. From brainstorming features with DeepSeek to building with Lovable and polishing in Figma, each step offered a lesson in leveraging technology effectively. For anyone looking to break into app development, this approach proves you don’t need years of coding experience—just a clear vision and the right tools. AI app building isn’t just a shortcut; it’s a new way to innovate, opening doors for creators to bring their ideas to life faster than ever before. If this inspires you, consider diving into these tools yourself and see what you can build in a day.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.