Problem: There are no tools to monitor pending transactions, compare node behaviour, or spot network anomalies.
Built for: infra teams, Ethereum core devs, chain devs, anyone working with memepools. But also, traders, researchers, etc.
Importance: analyzes how the network is working, and its attacks if/when they happen, memepool transaction analysis, etc.
Solution: Open-source public good visualization tool, real-time feed of pending txs from multiple nodes, easy to integrate, features like search, filter, and explore tx by type.
Protocols distribute capital towards ecosystem growth
Very few protocols have a group dedicated to public goods
Gitcoin grants program has struggled to raise outside funding for public goods
Misalignment
Butter identified that ecosystem misalignment is a root issue and aims to solve it through conditional funding markets that track outcomes tied to shared objectives.
Instead of framing these as public goods, Butter suggests calling them ecosystem goods, benefiting Ethereum participants specifically.
There’s a funding gap between EF (which supports infrastructure) and VCs (who expect returns), leaving many projects unfunded.
The EF’s neutrality protects its legitimacy but prevents it from supporting all ecosystem needs.
This can leave Ethereum vulnerable when essential services lack funding.
The EF avoids funding projects with tokens or financial products and focuses on developer growth, research, and infrastructure.
Their commitment to neutrality limits their scope, creating blind spots in the ecosystem.
Conditional Funding (and Decision) Markets
Butter is building markets that use prediction mechanisms to guide funding decisions.
Inspired by futarchy, these markets forecast outcomes of funding different projects, enabling data-driven capital allocation.
Decision markets don’t work exactly like prediction markets. They operate as a set of prediction markets working in tandem.
Butter’s Approach
Butter applies decision markets to funding: metrics (like TVL) are tracked, and markets simulate the impact of grants to competing protocols. The most promising outcomes drive actual funding.
How it works (OP example):
A market was run for Optimism, where participants estimated each project’s Superchain TVL increase after three months if given 100,000 OP.
The experiment involved giving everyone “OP Play,” a fake token instead of real money.
This led to interesting dynamics, including many bots.
22 projects participated, including Rocket Pool, Superform, Balancer, and others.
Participants predicted the TVL increase after three months, given that each project received 100,000 OP.
Incentives
The goal is for the accuracy of prediction markets to translate to real markets.
Incentives are aligned because traders aim to maximize their returns
Prediction markets are expensive to game with real money
Ask for Public Goods WG
The ask involves seeking support, both financial and advocacy, for CFMs within the ecosystem.
Funding support would increase the rewards pool for conditional funding markets.
