Most people think about AI as this distant, almost magical thing. But when you're actually building it, you realize it's just pattern recognition at scale. The magic isn't in the algorithms. It's in finding the right problems to solve.
I'm Tyler. I study CS at Oklahoma State. Since 2018, I've shipped 63 projects. Most failed, which is exactly what you'd expect. The ones that didn't fail taught me more than any class ever could.
Here's what I've learned: the best way to understand something is to build it. Not read about it, not watch videos about it. Build it. That's why most of my projects are on GitHub. Code doesn't lie.
The interesting thing about AI right now is that we're in this weird transition period. Everyone's talking about it, but most people are just using other people's models. The real opportunity is in understanding the fundamentals well enough to see what's actually possible, not just what's hyped.
I spend most of my time thinking about how to make machines understand the world better. Sometimes that means building neural networks, sometimes it means just writing really good software. The line between AI and good programming is blurrier than people think.
When I'm not coding, I'm usually reading (mostly technical stuff, some philosophy), working out (sitting at a desk 12 hours a day will kill you), or tinkering with hardware projects. Physical constraints teach you things that pure software never can.
The projects that matter aren't always the ones that get the most attention. I'm interested in problems that seem simple but turn out to be surprisingly deep. Usually these involve making computers do things that humans find trivial but machines find impossible.
If you're working on something interesting, especially if it involves AI, robotics, or just building things that matter: tylergibbs048@gmail.com
See what I'm reading if you want to know how I think.
Resume for the formal stuff.