How AI Reginited My Passion For Software Development

Every developer goes through phases of burnout – it's just part of the journey. I was fortunate to have years of uninterrupted passion for software development from 2015 to 2020. During this period, I was freelancing, growing my YouTube channel, and my productivity was through the roof. But in 2020, I hit a wall. A massive burnout phase that left me struggling with inconsistent motivation ever since.

That is, until I discovered hardcore AI development tools like Cursor, Windsurf, and Claude Code.

Beyond ChatGPT and Copilot

I'll admit, I was late to the party. While I'd been using ChatGPT and GitHub Copilot since their release, I was skeptical about the more advanced AI coding tools. These newer tools aren't just about autocomplete or copying code snippets – they're literally AI agents that write entire applications while you simply dictate what you want to build.

I know this is controversial. Some developers absolutely hate this approach, and I understand why. I was skeptical too, and honestly, I still am in many ways. There are definitely situations, people, and projects where heavy AI assistance is a terrible idea.

But for others? It's like having superpowers.

Why My Passion Returned

The truth is, I've always loved building things. That feeling of creating something useful – whether for others or just yourself – is what got me into development in the first place. What these AI tools have done is remove the friction between having an idea and seeing it come to life.

I can now go from concept to deployed MVP in a couple of hours, depending on the project size. The speed is incredible, but it's more than that. I no longer have to spend time on tedious tasks like:

• Setting up configurations
• Writing boilerplate code
• Debugging environment issues
• Writing repetitive CSS
• And countless other time-sinks

Instead, I can build and manage at a much higher level, focusing on the creative and strategic aspects of development.

This shift has been so motivating that I find myself getting up earlier just to work on projects. I have some larger projects in the works but here are some small tools that I came up with in the past 1-2 weeks:

• https://apimocker.com - Fake REST API similar to JSONPlaceholder
• https://apiprobe.dev - HTTP Request & API tool
• https://bundleview.dev - View and optimize your JS bundles
• https://faviconforge.io - Create favicons from image files
• https://paletteforge.io - Create color palettes

Real-World Impact

Since then, I've been tackling projects I've wanted to build for years but never had the bandwidth for. One example is close to my heart: I suffer from migraines, vertigo, tinnitus, and other ailments that doctors have struggled to diagnose. I'm now building an AI-powered symptom tracking app that could help people find patterns and potential causes for their health issues.

The inspiration came from a personal experience where ChatGPT helped me identify that blocked ears I'd dealt with for 5 years were caused by dry skin in my ear canal. A simple ear oil from Amazon fixed what multiple doctors couldn't. I want to create a dedicated platform for this kind of problem-solving.

This is something that would have taken me months to build from scratch – if I ever found the time. Now, it's achievable in a few weeks, working just an hour or two a few days per week.

I'm also finally able to tackle rebuilding the custom video platform for traversymedia.com, something I've wanted to do for years but never had the resources to dedicate to it.

The Elephant in the Room: Concerns for Beginners

However, we need to address some serious concerns, especially for new developers.

If you're just starting out, relying too heavily on AI tools can actually hurt your long-term growth. When I learned to code, I had to understand every line, every function, every concept because I was writing it all myself. That struggle – as painful as it was – built deep foundational knowledge.

If beginners just tell AI agents to build entire apps without understanding the code, they're not really learning to code. They're learning to be project managers for AI. When things break (and they will), they won't have the knowledge to debug or understand why.

There are also industry-wide concerns:

• What happens to entry-level positions if AI dramatically increases productivity?
• Will companies expect senior-level output from junior developers?
• Will fewer developers be hired overall?

Additionally, these tools aren't perfect. They can generate code that looks correct but contains:

• Subtle bugs
• Security vulnerabilities
• Performance issues
• Code that works in development but fails in production

My Rule of Thumb

I recently tweeted something that summarizes my philosophy perfectly:

If you're an experienced developer, use AI to build. If you're a beginner or new to a technology, use AI to LEARN.

There's a spectrum of AI usage, from having it write entire projects to using it for suggestions and explanations while you write the code yourself. Beginners should lean heavily toward the learning side – AI is incredible for understanding concepts, debugging, and getting explanations of how things work.

The Future is Here

The way we code is changing, and there's no fighting it. I believe the only outcome of being hesitant about these tools is falling behind. I already feel behind because I wasn't using them when they first emerged – a mistake I won't repeat.

These tools have reignited my passion for development in a way I didn't expect. They've removed the barriers between imagination and implementation, allowing me to focus on solving problems rather than wrestling with implementation details.

But like any powerful tool, they need to be used thoughtfully and appropriately for your situation and experience level.

You can find the video version of this post - here