Description DAO Drops is a public goods funding experiment that leverages on-chain data to empower Ethereum ecosystem participants to make fund allocation decisions. People who have participated in activities that indicate they would be an informed decision maker, are given fund allocation power. For Drop 1, the allocators are comprised of these three sets of addresses, and as we do iterative rounds we will refine the allocation scoring algorithm: Galaxy.eco’s Shadowy Super Coder list. 110,294 Ethereum addresses who have deployed at least 1 contract on Ethereum mainnet before August 1st, 2021, and deployed contracts that had at least 2 different addresses interacted. POAPs for all past DevCon’s, ETHCC, ETH Paris, and ETH Denver. DeepDAO’s data set of participants in DAOs. These allocators vote on projects from a public nomination process which will include outreach to local Ethereum meetup organizers for geographic inclusion. A curation process will reduce the number of projects to review for nomination, to mitigate popularity contest. Each project will receive funds in proportion to the points they were given by allocators.

Project Description The EF has the funds but lacks the information needed to effectively allocate resources beyond primary tracks like ETH2 R&D. It is simply too difficult for any single organization to focus on all of the needs of such a large and diverse ecosystem. The individuals and communities that know what's needed are highly distributed. DAOs could leverage the domain knowledge of these actors to serve as more effective and transparent allocators over time. We propose a program to score addresses based on past on-chain activity (dapp usage, development, event attendendance) that gives them influence over fund allocation decisions. The program could be seeded with funds and the minimal marketing and UI necessary to start allocating resources as they see fit. This project encompasses the R&D phase needed to get the program to launch-readiness. Month 1: Research Investigate current resource allocation practices across the ecosystem to identify gaps and potential areas for improvement Evaluate different governance frameworks Generate list of possible cohorts