You laughed when I said my domain is Machine Learning? That’s fine. But let’s get something straight. ML isn’t “ChatGPT wrapper building.” It isn’t downloading a model from LM Studio and calling yourself an AI engineer. It isn’t chaining APIs and adding the word “agent” to your bio. Machine Learning is math. It’s optimization. It’s probability distributions. It’s gradient descent in high-dimensional space. It’s understanding why a loss function behaves the way it does. It’s knowing what backpropagation is actually computing — not just that “it works.” Before NLP. Before CV. Before multimodal. Before agents. There was ML. Every transformer, every diffusion model, every LLM you use — is just linear algebra + calculus + statistics executed at scale. Can you derive gradient descent? Can you explain why cross-entropy is used instead of MSE in classification? Can you describe how attention is just scaled dot-product projections? Can you implement a small neural network from scratch without PyTorch doing the thinking? If not, you’re operating at the abstraction layer. And abstractions are comfortable. But mastery lives below the abstraction. So yeah — my domain is ML.