Kaustubh Sule

“In an age of speed, I began to think, nothing could be more invigorating than going slow. In an age of distraction, nothing can feel more luxurious than paying attention. And in an age of constant movement, nothing is more urgent than sitting still.”

Tuning in to weird signals

In the world of trend research, we are traditionally taught to be on the lookout for ‘weak signals’ - small, low-frequency indicators that hint at a potential shift in culture. And while scanning for these weak signals is a fundamental part of the process, it’s easy to get stuck in a pattern of only noticing evidence

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

Feature engineering is the act of extracting features from raw data and transforming them into formats that are suitable for the machine learning model.

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.