implementation

On that template, AI looks like it has already finished the first act. The outer ring has cracked: neoclouds, “AI infra” smaller caps, and nuclear or power-adjacent trades are down 50% or more from their highs. That’s your paper-railway phase. The core of the story, Nvidia and a handful of megacaps, is still treated as the “safe” way to own AI, but

But from the perspective of Alibaba and JD, the motivation is broader. Instant delivery is increasingly becoming the way consumers interact with local commerce. If you control the app people open whenever they need something quickly, you control an important part of daily life. That position brings data, merchant relationships, and habit formation

China’s experience also looks very different from India’s because the underlying models are different. In India, quick commerce has largely been built around dark stores — dedicated warehouses holding inventory that can be delivered quickly. The platform controls stock, pricing, and fulfilment, but it also takes on inventory risk and high fixed

A feature is a numeric representation of raw data. There are many ways to turn raw data into numeric measurements, which is why features can end up looking like a lot of things. Naturally,

Feature engineering is the process of formulating the most appropriate features given the data, the model, and the task.

Practitioners agree that the vast majority of time in building a machine learning pipeline is spent on feature engineering and data cleaning. Yet, despite its importance, the topic is rarely discussed on its own.

Mastery is about knowing precisely how something is done, having an intuition for the underlying principles, and integrating it into one’s existing web of knowledge. One does not become a master of something by simply reading a book, though a good book can open new doors.

Next, consider the scale of the features. What are the largest and the smallest values? Do they span several orders of magnitude? Models that are smooth functions of input features are sensitive to the scale of the input.

Each piece of data provides a small window into a limited aspect of reality. The collection of all of these observations gives us a picture of the whole. But the picture is messy because it is composed of a thousand little pieces, and there’s always measurement noise and missing pieces.