Objects Over Opinions: How One Dev Solved the Trend Definition Problem

Building a Trend Detector: When One Developer’s Brainstorm Becomes an Architecture Problem

Gleb faced a familiar pain point: his users—businesses dealing with shrinking revenue—needed to understand what’s really trending versus what’s just noise. The problem wasn’t finding trends. It was defining what a trend actually is.

Most people think a trend is just “something becoming popular.” But that’s dangerously vague. Is it about React 19’s new features trending? Good luck—in six months, React 20 arrives and your analysis becomes obsolete. Gleb realized the fundamental issue: you can’t track what you can’t define.

So he started from scratch, working backward from the chaos. The breakthrough came around 10:35 AM: trends aren’t the base unit. Objects are.

His logic was elegant: take any object—material or immaterial. A fork. React.js. A viral tweet. Each exists in some quantity. When that quantity shifts dramatically in a short time, that’s when you have something worth measuring. The rate of change becomes your signal. Objects belong to categories (aluminum forks → utensils → kitchenware; React.js → JS frameworks → frontend tools), creating a taxonomy that survives version changes and technological shifts.

But here’s where it got interesting. Gleb added a property most trend-tracking systems ignore: emotional intensity. Around every object, there’s a mathematical measure of how much people are talking about it. You can quantify discussion volume, sentiment shifts, and urgency—all as numerical properties attached to the object itself.

The architecture became clear: build a base of objects, not trends. Attach properties to each: instance count, consumption rate (measured in “person-days”), speed of change, emotional intensity. The trend isn’t separate—it emerges from these properties. When you see the rate of change accelerating, you’ve spotted a trend. When emotional intensity spikes while consumption stays flat, you’ve found hype that won’t stick.

One insight proved crucial: individual objects can drag entire categories upward or down. A single viral fork design might spike aluminum utensil demand broadly. But forks and spoons would be variants within a single object definition, not separate entities. This prevented the system from fragmenting into useless micro-categories.

By 11:20 AM, Gleb had moved from “what is a trend?” to “here’s a system that finds them.” Not a database schema yet. Not a prototype. But something testable: a conceptual model that could survive contact with reality.

Why this matters: Most trend-detection systems fail because they chase moving targets (version numbers, platform changes). By anchoring everything to objects and their measurable properties, Gleb built something that could stay relevant for years, not months.

The next phase? Building the actual system. Probably starting with a lightweight database, a properties schema, and a velocity calculator. But the hard part—the thinking—was done.

😄 How can you tell an extroverted programmer? He looks at YOUR shoes when he’s talking to you.