Why I Wanted to Use AI

My Original Expectations

Before we formally start the series, I want to let you, the reader, know what my original expectation of using AI was. This transparency is important because it sets the foundation for everything that follows in this series.

The Time Problem

Having built several websites and apps before, I knew how much time even a "simple" project could take. For something the size of MiniBreaks, doing everything from scratch—while juggling a full-time job, would easily eat up a year or more. The endless cycle of:

• Setting up project structure and tooling
• Writing boilerplate code for authentication, databases, and APIs
• Designing and iterating on UI components
• Writing marketing copy and content
• Making countless small design decisions

I'd been through this before. I knew that by month 6, motivation would wane. By month 9, I'd be questioning whether the project was worth continuing. And by month 12, there was a good chance it would join the graveyard of unfinished side projects.

What I Wanted AI to Do

I wanted AI to accelerate the boring parts—the foundation work that every project needs but that doesn't feel particularly creative or engaging:

• Help me bootstrap a well-organized codebase with proper folder structure, configuration files, and initial setup
• Offer quick ideas for UI/UX when I was stuck staring at a blank screen
• Speed up writing copy and generating content for landing pages, feature descriptions, and marketing materials
• Act like a second brain during design decisions, someone to bounce ideas off when I wasn't sure which approach to take

Essentially, I wanted AI to be my development accelerator, not my replacement. I still wanted to make the key decisions, write the critical logic, and shape the product vision. But I hoped AI could eliminate the friction that often kills side projects before they gain momentum.

More importantly, I needed AI to help me finish my project before it becomes another my abandoned project! 🤦‍♂️

What AI Actually Delivered

AI did all of those things I expected—and occasionally, it did more. Much more.

Beyond My Expectations

For example, AI not only refactored my code using solid architecture principles, it sometimes suggested layouts or visuals better than what I had in mind. It would reuse styles from my existing design system and make smart decisions about what elements to highlight on a new page.

There were moments when I'd describe a feature I wanted, and AI would come back with an implementation that was not just functional, but elegant. It would suggest patterns I hadn't considered, point out potential edge cases, and even recommend user experience improvements.

Along the way, I had a few "aha" moments—like realizing I could ask AI not just to generate code, but to help structure it using best practices. Suddenly, my project folders were clean, organized, and extensible. My components followed consistent patterns. My API endpoints had proper error handling and validation.

The Partnership Shift

The real breakthrough came when I stopped thinking of AI as a tool and started treating it as a development partner. Instead of asking "Can you write a function of doing X?" I began asking something like "Before we go further, I want us to consider our current architecture so it will be more maintainable in the long term?" or "For this feature, but how can we sustain user engagement?"

Over time, I began giving more instructions to AI instead of writing the changes manually. This wasn't because I became lazy; it was because I discovered that articulating my requirements clearly to AI forced me to think more systematically about what I was building.

And that shift, treating AI as a partner rather than a tool, was what really unlocked its value.

The Reality Check

But let's be honest: it wasn't hands-off magic. AI required careful direction, clear communication, and constant quality control. There were moments of frustration when AI would misunderstand requirements or generate code that looked right but had subtle bugs.

I learned that AI amplifies both good and bad practices. If I was unclear in my instructions, I got unclear results. If I didn't understand the problem space well enough, AI couldn't compensate for that gap.

The most successful outcomes happened when I combined my domain knowledge, strategic thinking, and product vision with AI's ability to rapidly generate, iterate, and implement solutions.

Setting Your Expectations

Therefore, I want you to ask yourself a few questions before we continue with the series:

Questions for You

Briefly write down your answers. I'd encourage you to come back to these notes halfway through the series and see if everything is meeting your expectations. If not, that's valuable feedback: both for adjusting your approach and for understanding where AI's current capabilities align with your needs.

The Key Mindset

The most important mindset shift I made was this: AI is not about replacing your thinking, it's about amplifying it.

The developers who struggle with AI are often those who expect it to read their minds or solve problems they haven't fully understood themselves. The developers who thrive are those who use AI to execute their well-thought-out plans more efficiently.

In the next part, we'll dive into the specific tools and setup that made this collaboration possible. But first, take a moment to think about your own expectations and motivations. They'll shape everything that follows.