Why AI Couldn't Build My Simple App - Lessons from a Frontend Dev

My App Idea

Every developer has that one idea, right? Something simple, something useful, a scratch-your-own-itch project. Mine was pretty basic on the surface. I wanted an app that could do X and Y. No complex features, just a straightforward tool to solve a specific problem I had.

The goal was simple: build a functional app quickly. I figured with all the buzz around AI and no-code tools, this would be a breeze. I imagined describing what I wanted, pressing a button, and having a working prototype almost instantly.

I thought, "How hard could it be for AI to handle this?"

Trying AI First

Like many others, I was curious about the buzz around AI and its capabilities, especially in development. Could it really handle the grunt work? With the idea for my simple app taking shape, I thought this was the perfect opportunity to put AI to the test. Why not start by seeing what these powerful tools could generate?

The promise was tempting: faster development, less boilerplate code, maybe even handling complex parts with ease. I envisioned a smooth process where AI would be my co-pilot, quickly generating the foundation or even significant chunks of the app. It felt like exploring a new, efficient path to bring my idea to life.

So, I decided to dive in headfirst, relying on AI as my primary tool for the initial build phase. This was my starting point, eager to see if the hype lived up to reality for a practical project like mine.

The AI Promise

In the world of tech, buzzwords come and go. Lately, AI's ability to build things has been a big one. There's a lot of talk about how AI can speed up development, maybe even build entire applications with just a few prompts. It sounds almost magical, promising to lower the bar for entry and empower anyone with an idea to create software.

This promise felt compelling. The idea of simply telling an AI what you want and having it instantly generate working code is incredibly appealing, especially for someone looking to bring a simple app idea to life without spending months learning complex frameworks or languages. It felt like the future was here, offering a shortcut straight to a finished product.

Starting the Build

With the idea solidified and the decision made to build it myself, the next step was to actually start coding. This felt familiar and comforting after the detour with AI. I decided to begin with the core functionality, focusing on getting the basic structure in place.

Setting up the development environment was straightforward. I chose the tools and libraries I was most comfortable with, ensuring a smooth workflow. Unlike the black box experience with AI, I had full control over the project setup and dependencies.

The initial coding phase involved building the foundational components. This included setting up the basic user interface, handling user input, and defining the core logic of the application. Each line of code felt like a small step forward, and I could see the app taking shape exactly as I envisioned it.

This hands-on approach allowed for immediate iteration and testing. I could quickly identify and fix issues, and the development process felt much more dynamic and responsive compared to generating code with AI. The ability to directly manipulate the code and understand its inner workings was a significant advantage.

Hitting a Wall

Building my simple app started smoothly, guided by the AI. It felt like the promises I'd heard were true – rapid development, minimal coding, just describe what I needed and watch it appear.

The initial setup was straightforward. I could layout basic components and define simple interactions. But as the app began to take shape and I moved beyond the most generic features, things started to get complicated.

I wanted a specific kind of user input form, something that didn't quite fit the standard templates the AI offered. I tried explaining it differently, tweaking the prompts, but the results were consistently close, yet not exactly right. It felt like trying to have a nuanced conversation with a machine that only understood basic phrases.

Then came the real challenge: integrating a third-party service for a core function of the app. This wasn't a simple copy-paste; it required specific API calls, handling authentication flows, and processing responses in a particular way. I asked the AI for help.

The AI provided code snippets, but they were often:

• Generic: Not tailored to the specific library or framework I was using.
• Incomplete: Missing crucial setup or error handling.
• Incorrect: Sometimes the logic was just plain wrong for the task.

Debugging these AI-generated snippets was often harder than writing the code myself from scratch. I was spending more time fixing and understanding why the AI code didn't work than I would have spent writing the correct code initially.

This is where I truly hit a wall. The AI was fantastic for the boilerplate, the simple stuff, but when it came to custom requirements and integrating external services, its limitations became starkly apparent. It couldn't reason about the specific context of my project or understand the subtle nuances required for a functional, integrated app.

Custom Needs Arise

As I got further into building my simple app, the initial excitement of using AI started to fade. The basic structure and common features were straightforward enough for the AI to handle. But then came the point where my idea had specific requirements.

My app wasn't just another standard template. It needed particular ways of handling user input and displaying information that weren't typical. For example, I needed a custom layout for showing certain data points and a unique method for processing user interactions based on specific conditions.

This is where the limitations started to show. The AI was great at generating common patterns and readily available components. However, when I tried to describe these more unique, custom requirements, the AI struggled to understand and produce the correct code. It often defaulted back to standard solutions or generated code that was close but required significant manual modification.

It felt like the AI had a library of pre-built blocks, but I needed a block that didn't exist in its collection. Trying to force the AI to create something truly custom was challenging and time-consuming.

AI Limitations Show

Up to a point, AI seemed promising. It could generate basic structures, suggest simple code snippets, and handle repetitive tasks. It felt like having an assistant that could quickly lay the groundwork. However, as the project evolved and specific, non-standard requirements emerged, the cracks in the AI-first approach started to show.

The initial scaffolding generated by AI was functional, but rigid. Customizing anything beyond the most generic patterns became a significant challenge. I needed specific user interactions, unique data handling, and integrations that weren't part of the standard AI toolkit.

It felt similar to trying to modify a pre-fabricated house compared to building one from scratch. Making small changes was possible, but anything significant required dismantling and rebuilding parts in a way that the AI wasn't designed for. The "drag and drop" or "generate and tweak" promised by some AI tools fell short when faced with the messy reality of a custom application.

Instead of saving time, I found myself spending hours trying to force the AI-generated structure to do something it wasn't intended to do. Debugging issues within the AI's output was often opaque; it was hard to understand *why* something wasn't working or how to fix it properly without diving into the underlying code, which defeated the purpose of relying on AI.

This experience highlighted that while AI can be a powerful tool for specific tasks or generating boilerplate, it struggles with the nuances, creativity, and problem-solving required for building a truly custom application that meets unique user needs. The ability to write and understand code became essential once again to overcome these limitations.

Why Code Matters

While AI and no-code tools offer compelling promises of speed and ease, my experience building a simple app revealed a crucial truth: understanding and writing code remains essential.

AI can generate boilerplate and handle standard tasks efficiently. However, when faced with unique requirements or the need for intricate customization, the limitations of AI assistants quickly become apparent. They rely on patterns from existing data. If your idea steps outside the most common scenarios, or if you need to integrate specific, non-standard functionalities, AI alone will hit a wall.

This is where traditional coding skills shine. Writing code allows for precise control over every aspect of your application. You can implement complex logic, optimize performance for specific use cases, and build truly bespoke features that distinguish your app.

Relying solely on AI or no-code platforms can lead to being stuck blocked when the platform's capabilities end or when you need to debug an issue the AI can't diagnose. Knowing how to code provides the flexibility and power to overcome these hurdles and bring your exact vision to life. It's the difference between assembling something from a kit and being able to build anything you can imagine from raw materials.

Lessons From AI

Trying to build my app with AI taught me a few things. While AI can help with many tasks, it hit limits fast when it came to specific requirements.

One key lesson is that AI excels at general tasks and boilerplate code, but struggles with custom logic and complex integrations that need a deep understanding of the project's unique needs.

It highlighted the value of fundamental frontend development skills. Knowing how to structure code, debug issues, and implement precise designs became essential when AI couldn't provide the exact solution.

Flexibility and problem-solving ability remain core to development. AI is a tool, but it doesn't replace the developer's role in understanding the full picture and writing tailored code.

Dev Skills Are Key

While AI tools can be helpful, they aren't a magic bullet. When building even a simple app, I quickly learned that relying solely on AI wasn't enough. There comes a point where custom needs arise, and that's where fundamental development skills become critical.

Understanding how code works, the structure of your project, and being able to debug problems are essential. AI can provide suggestions or even generate snippets, but you need the knowledge to integrate them, modify them, and build features that aren't standard or easily templated.

Hitting a roadblock with an AI-generated approach highlighted the need for that core technical foundation. It's the ability to understand the underlying logic and implement specific requirements that truly allows you to bring a unique idea to life. AI is a powerful tool, but it complements, rather than replaces, skilled development.