When you’re digging into what users actually need, it’s rarely something concrete and explicit. There’s the old Ford line — “if I’d asked people what they wanted, they’d have said a faster horse” — but once you get to actually building the product, you still have to land on something concrete: specific features that satisfy the underlying need. So what I mean by “requirement analysis and management” here is the whole chain: start from a surface-level ask, dig into the real need underneath it, evaluate it, land on a specific solution, and hand that off to engineers and designers to turn into an actual product. B2B and B2C products differ quite a bit in how this plays out. ...

1 Why Bother with Competitive Analysis 1. Better decisions. Understanding competitors’ products helps you position your own — and steer clear of the strongest players so you don’t wander into a red ocean. It lowers the risk of getting your bets wrong. 2. Learning from others. Studying what makes a competitor’s product strong lets you build more deliberately: take what works, absorb their strengths, and use it to sharpen your own edge. ...

Tensors Tensor = container Tensors are the foundation everything in modern machine learning sits on. At its core a tensor is just a container — usually of numbers, occasionally of strings, though that’s rarer. Think of it as a bucket of numbers. A 0-D tensor is a scalar; a 1-D tensor is a vector; a 2-D tensor is a matrix; a 3-D tensor (and up) is what you use for shared data storage — things like color images. ...