Hey everyone, Carl from Open Source Observer here, sharing the evaluation algorithms for Retroactive Public Goods Funding (Retro Funding) Season 7: Onchain Builders. The Onchain Builders Mission seeks to reward protocols and dapps that demonstrate meaningful onchain usage, drive TVL growth, and support interop across the Superchain. See here for more on the mission’s objectives.
This season, Optimism’s Retro Funding is pioneering a "metrics-driven, humans-in-the-loop" approach. Instead of voting on projects, citizens vote on evaluation algorithms. Each algorithm represents a different strategy for rewarding projects. Algorithms will evolve in response to voting, community feedback, and new ideas over the course of the season.
Here’s a tl;dr of the three algorithms:
| Algorithm | Goal | Best For | Emphasis |
|---|---|---|---|
| Superscale | Reward clear-cut leaders | Large, established projects with high usage | Adoption (favors projects with high recent activity) |
| Acceleratooor | Prioritize fast-growing projects | New and emerging projects gaining traction | Growth (favors projects with the largest net increase in impact) |
| Goldilocks | Achieve a more balanced distribution | Consistently active projects with steady engagement | Retention (favors projects maintaining impact over time) |
Each project’s score is based on four key metrics:
For each core metric (i.e., transactions, gas fees, TVL, user counts), we present three variants for comparing the values for the current period vs. the previous period. Each algorithm we present has a different weighting of these variants.
We’ve developed an initial three candidate algorithms for evaluating impact. Each algorithm highlights different priorities: Superscale focuses on established, high-volume projects, Acceleratooor emphasizes rapid growth, and Goldilocks tries to balance adoption, growth, and retention.
We’ve included details on each algorithm below. We also have a Hex dashboard where you can see data on each algorithm.
Important: as projects are still signing up for the current measurement period, we simulated the results for each algorithm using past Retro Funding participants and historical data. The “top projects” below are based on applications from RF4 and data from Dec / Jan.
This algorithm aims to reward projects with significant current usage and established impact. It places heavier weights on the most recent values of TVL and transaction metrics, and less weight on user numbers. This strategy aims to embody the philosophy of "it's easier to agree on what was useful than what will be useful". You should vote for this algorithm if you want to keep things simple and give the projects that are having the most impact right now the recognition they deserve.
Weightings & Sample Results:
Top projects (using simulated data):
This algorithm seeks to reward projects experiencing rapid growth over the current measurement period. In particular, it emphasizes growth (i.e., net increases) in TVL and transaction volume. The goal is to spot breakout stars and accelerate them. This is a good algorithm to vote for if you want to create a strong signal for rising projects that the Superchain is the place to be.
Top projects (using simulated data):
This algorithm seeks to evenly balance various aspects of impact. It places a moderate weight on each metric and prioritizes retention over sheer growth. The goal is to support steady, sustained contributions across the board rather than “spiky” projects that only excel in one area. This is a good algorithm to vote for if you want to support a wide range of projects.
Top projects (using simulated data):
One of the proposed evaluation algorithm for the Onchain Builders mission will be chosen using approval voting. This means you can approve one or multiple options. For an algorithm to be selected, it must meet the following conditions:
The algorithm with the most approvals, provided it meets the thresholds, will be used for the remainder of Season 7.
To learn more about this mission, please see the Onchain Builders Mission Details. We also have more in-depth documentation on these algorithms in these algorithms in the Retro Funding repo.
If you have ideas on refining these models, let us know in the comments. And if you’d like to contribute to making these algorithms better, you can submit a PR or open an issue here.