Pairwise

Optimism badge holders needed to evaluate hundreds of projects and decide how public-goods funding should be distributed between them.

The concept behind Pairwise began as a whitepaper. My role was to translate that model into a product people could understand, trust, and use to make real funding decisions.

Asking someone to score every project would make an already difficult decision even more overwhelming.

Pairwise broke the task into a series of smaller choices. Badge holders saw two projects at a time and selected which one they believed should receive more funding. The system combined those comparisons into an overall allocation.

Before voting, badge holders identified their areas of expertise and which categories they considered strategically important. This helped focus the experience on projects they were better positioned to evaluate.

Instead of feeling like a long budgeting exercise, the interaction became a sequence of clear, repeatable decisions.

Pairwise was handling real funding, so simplicity couldn’t come at the cost of confidence.

I worked closely with the team to make the comparison logic understandable, reduce unnecessary information, and guide badge holders through the process without influencing their choices.

I tested wireframes and interactive prototypes with badge holders, then iterated on the flows and interface with PMs and developers as we moved toward the MVP.

The result was a working product used by Optimism badge holders in a real allocation round supporting the distribution of $25M in OP tokens.

The MVP helped the team validate pairwise comparison as a practical way to make large-scale funding decisions and secure continued funding for the product.

Pairwise began as a complex allocation model. My work was turning it into something people could actually use, one decision at a time.