About.
From implementing backprop to architecting enterprise-grade AI systems.
The Early Days
My journey into artificial intelligence didn't start with ChatGPT. It began years ago, poring over deep learning concepts from 80s textbooks in what felt like an abandoned field. I remember the distinct mix of frustration and awe while implementing backpropagation in Java—long before PyTorch or TensorFlow existed—just to truly understand how the gradients flowed.
There was a purity to those early Restricted Boltzmann Machines (RBMs). They weren't just statistical tools; they felt like the first real steps toward modeling cognition.
The Transformer Revolution
As the field evolved, so did my focus. I watched the rise of Convolutional Neural Networks, but it was the emergence of the Transformer architecture that signaled a paradigm shift. The ability to model long-range dependencies and context at scale changed everything.
I spent years diving deep into attention mechanisms, fine-tuning large language models, and architecting systems that could leverage this newfound power for real-world applications, not just academic benchmarks.
Why NeuralNexus?
I founded NeuralNexus because I saw a gap. On one side, pure research labs pushing boundaries but disconnected from business reality. On the other, companies desperate to "do AI" but drowning in hype and slideware.
We exist to bridge that gap. We bring the engineering rigor of a decade in high-scale SaaS and Fintech to the chaotic world of modern AI. We don't just wrap APIs; we build neural architectures that solve actual business problems.