How we designed our pattern library.

One of the biggest problems with AI-generated content is that everything looks the same. Same layouts, same colors, same templates. Two users posting on the same day produce identical output. That's not a content tool — that's a stamp.

We spent months studying what makes great LinkedIn visual content work. We looked at hundreds of high-performing posts and decomposed them into their atomic parts. What we found: great content is built from a small number of reusable composition patterns, combined in different ways.

We identified six pattern families: composition moves (how elements are arranged spatially), highlight styles (how emphasis is rendered), photo treatments (how images integrate with text), numeric treatments (how data is displayed), color schemes (palette and mood), and stacking rules (how families combine).

Each family has multiple variations. When the agent designs a post, it selects one variation from each family and stacks them. The number of possible combinations is enormous — which means no two posts look the same, even when two users write about the same topic on the same day.

The system also learns. Over time, it biases toward patterns that perform well for your specific audience. Your visual language evolves with your content practice.

Templates are dead. Patterns are alive.