Octant is an important funding partner for OSO and dozens of other open source projects in the Ethereum ecosystem. Since 2023, Octant has contributed approximately $5 million (in ETH) to over 60 projects.

Octant has a novel way of funding open source. Users of the Octant platform lock GLM tokens into a staking contract. In exchange, they gain voting rights and earn ETH rewards. Every quarter, Octant runs an "epoch" where users get to allocate a portion of their staking rewards to projects they care about. This allocation determines how a larger staking pool from the Golem Foundation is allocated across a set of projects.

In 2024, Octant supported 47 open source software teams across four epochs. The funded projects have been diverse in scope, ranging from privacy-focused tools like Tor to ecosystem analytics platforms like L2Beat to up-and-coming organizations like the GreenPill Developer Guild.

This post explores some high-level trends in developer activity over the past year. As these projects have different missions and objectives (and not all are open source software projects), one should not attempt to directly compare their impact or productivity. Nonetheless, we hope these insights can highlight ongoing contributions, identify growth areas, and provide additional context on what each project has been up to.

OSO is partnering with Gitcoin to enhance data infrastructure and analytics capabilities on top of Grants Stack.

The goal is to make it easy for developers, researchers, and community members to connect Gitcoin Grants' data with any of OSO's public datasets. In doing so, we hope to streamline data engineering, improve donor transparency, and enable more data-driven decision-making for the Gitcoin ecosystem.

This post shows how you can use OSO to:

• Find and explore Gitcoin grantee information in OSO's repository
• Evaluate coding activity, contributions, and productivity to assess project momentum and engagement
• Track funding patterns across rounds, review top-funded projects, and correlate funding with developer activity
• Identify similar grantees with comparable development profiles and discover additional funders supporting Gitcoin grantees

This is the final post in our Opening up the Ballot Box series for 2024. Changes planned for Retro Funding 2025 will likely reshape how we analyze voting behavior.

In RF6, our results (as an organization) reflected a critical issue with Retro Funding in its current form: subjective visibility often outweighs measurable, long-term impact.

We had two project submissions:

1. Insights & Data Science (work like this series of posts): awarded 88K OP, the highest of any submission in the round.
2. Onchain Impact Metrics Infra (open data pipelines for the Superchain): awarded 36K OP, despite being a much larger technical and community effort.

We are humbled by the support for our Insights & Data Science work. Retro Funding has made our work at OSO possible, and we are deeply grateful for this affirmation. But we can’t ignore the underlying signal: the work that is most visible-—like reports, frontends, and ad hoc analysis—-tends to receive higher funding than work that delivers deeper, longer-term impact.

Open-source projects power innovation across industries, yet they often face a significant challenge: securing sustainable funding. Retroactive funding offers a promising solution by rewarding impactful contributions based on past results, but today’s retro funding rounds are complex, time-consuming, and infrequent, making them unreliable sources of support for public goods.

This inspired us to build AutoRF during ETHGlobal San Francisco 2024. AutoRF makes retroactive funding continuous, simple, and scalable by removing the barriers that hold current models back.

We’ve been thinking a lot about advanced metrics lately. We want to get better at measuring how specific types of interventions impact the public goods ecosystem.

For example, we frequently seek to compare the performance of projects or users who received token incentives against those who did not.

However, unlike controlled A/B testing, we’re analyzing a real-world economy. It's impossible to randomize treatment and control groups in a real-world economy.

Instead, we can use advanced statistical methods to estimate the causal effect of treatments on target cohorts while controling for other factors like market conditions, competing incentives, and geopolitical events.

This post explores our early experiments with synthetic controls and causal inference in the context of crypto network economies.

Optimism’s Retro Funding Round 5 (RF5) just wrapped up, with 79 projects (out of ~130 applicants) awarded a total of 8M OP for their contributions to Ethereum and the OP Stack. You can find all the official details about the round here.

In some ways, this round felt like a return to earlier Retro Funding days. There were fewer projects than Rounds 3 and 4. Venerable teams like Protocol Guild, go-ethereum, and Solidity were back in the mix. Voters voted on projects instead of metrics.

However, RF5 also introduced several major twists: voting within categories, guest voters, and an expertise dimension. We’ll explain all these things in a minute.

Like our other posts in the “opening up the ballot box” canon, this post will analyze the shape of the rewards distribution curve and the preferences of individual voters using anonymized data. We’ll also deep dive into the results by category and compare expert vs non-expert voting patterns.

Finally, we'll tackle the key question this round sought to answer: do experts vote differently than non-experts? In our view, the answer is yes. We have a lot of data on this topic, so you're welcome to draw your own conclusions.

Open Collective is a platform that enables groups to collect and disburse funds transparently. It is used by many open-source projects, communities, and other groups to fund their activities. Notable projects include Open Web Docs (maintainers of MDN Web Docs), Babel, and Webpack.

At Open Source Observer, we have been working on collecting and processing Open Collective data to make it available for analysis. This includes all transactions made on the platform, such as donations, expenses, and transfers. Datasets are updated weekly.

One of our primary goals at Kariba (the team behind Open Source Observer) is to build a network of Impact Data Scientists. However, “Impact Data Scientist” isn’t a career path that currently exists. It’s not even a job description that currently exists.

This post is a first step in changing that. In it, we discuss:

1. Why we think the Impact Data Scientist is an important job of the future
2. The characteristics and job spec of an Impact Data Scientist
3. Ways to get involved if you are an aspiring Impact Data Scientist

One important caveat. This post is focused on building a network of Impact Data Scientists that serve crypto open source software ecosystems. In the long run, we hope to see Impact Data Scientists work in all sorts of domains. We are starting in crypto because there is already a strong culture around supporting open source software and decentralizing grantmaking decisions. We hope this culture of building in public and experimenting crosses over to non-crypto grantmaking ecosystems. When it does, we’d love to help build a network of Impact Data Scientists in those places too!

In baseball, there’s an advanced statistic called WAR, short for Wins Above Replacement. It measures a player’s overall contribution to their team by comparing them to a “replacement-level” player—a hypothetical average player who could easily be brought in from the bench or minor leagues. The higher a player’s WAR, the more valuable they are to their team.

Now, let’s apply this concept to decentralized networks like Ethereum or its Layer 2s, which steward ecosystems of public goods including infrastructure, libraries, and permissionless protocols.

Just as baseball teams aim to build the best roster, ecosystem funds and crypto foundations strive to create the strongest community of developers and users within their networks. They attract these participants through incentive programs like grants and airdrops.

But how can the success of these initiatives be effectively measured? One approach is to evaluate how well these programs retain community members and generate compounding network effects compared to the average across the broader crypto landscape. The best networks are the ones that achieve the highest WAR outright or per unit of capital allocated.

This post explores how an empirically-derived metric similar to WAR might be applied to ecosystem grants programs as a way of measuring ROI. It includes some use case ideas (like a WAR oracle) and strawman WAR formulas for protocols and open source software (OSS) projects. It concludes with some ideas for getting started.

While this is currently a thought experiment, it’s something we at OSO are seriously considering as we develop more advanced metrics for measuring impact.

The voting results for Optimism's Retro Funding Round 4 (RF4) were tallied last week and shared with the community.

This is the last in a series of posts on RF4, analyzing the ballot data from different angles. First, we cover high-level trends among voters. Then, we compare voters’ expressed preferences (from a pre-round survey) against their revealed preferences (from the voting data). Finally, we perform some clustering analysis on the votes and identify three distinct “blocs” of voters.

Retro Funding aims for iteration and improvement. We hope these insights can inform both the evolution of impact metrics and governance discussions around impact, badgeholder composition, and round design.

You can find links to our work here.