After 10+ Years of Coding, I Rediscovered the Magic

I started coding in 2010 during high school. That first moment of making a computer print “Hello World” felt magical. I was making a machine bend to my will and I was hooked. But as any developer knows, professional software engineering is light years away from that experience.

For 7 years as a professional developer, the daily reality was less magical: countless hours reading documentation, debugging code, wrestling with cloud deployments, and yes, struggling with CSS. There’s always that rewarding moment when you finally make the machine bend to your will, and that part is addictive even through the struggles. A true love-hate relationship.

That was my reality, until AI changed everything.

The first wave: 10x

I began integrating Claude from Anthropic and ChatGPT from OpenAI into my development workflow. The productivity boost was immediate, but I quickly hit limitations, especially around context-sharing. So I built custom scripts to feed proper context to these AI tools, and my productivity skyrocketed.

I was already operating at 10x my normal speed, particularly with frontend work. This newfound efficiency led me to challenge conventional wisdom: instead of starting with Figma prototypes for customer feedback, I built five fully functional prototypes in just a couple of months.

The second wave: 100x

Then it got even better. After diving deep into the AI tools landscape, I discovered Cursor and Lovable, and suddenly I was operating at what felt like 100x capacity.

Every developer knows that emotional cycle: start, struggle, solve, celebrate, repeat. Usually, I’d hit this loop once or twice a day. Now it’s happening every 20 minutes. The magic of coding is back, only now I’m shipping production-ready applications, not Hello Worlds.

I felt that same rush I felt at 17 writing my first lines of code, except now I’m building complex, production-ready systems.

More than productivity

This is about rediscovering the joy of creation and the thrill of building without barriers. The struggle-to-reward ratio has fundamentally shifted. You still need deep engineering knowledge, perhaps more than ever, to guide these tools effectively and architect systems properly. But the tedious parts (the boilerplate, the CSS battles, the deployment scripts) those barriers are dissolving.

What this means for builders

With LYRASENSE, we’re bringing this same transformative experience to data scientists working with satellite data. We’re making them feel superhuman and putting joy back in building.

AI has already changed how we build software. The only question left is whether you lean into it or watch from the sidelines.