Use Cases
Social Networks
Open social graphs need a ranking and algorithm layer to power search, discovery and recommendations without relying on centralized moderation and censorship, and enable safe and personalized user experiences. Web3 social graph protocols natively create peer-to-peer data based on engagement actions (likes, follows, comments, recasts, mints, tips, etc.). This open data can be used to create different reputation graphs which can be computed using graph algorithms like EigenTrust. The compute results in a ranking or score for all users in the network. A developer can decide the heuristics for the reputation graph, and the algorithm parameters and weights, making it easily verifiable.
An Open Ranking layer for social networks:
Provide ranking and recommendation systems, enabling personalized and curated community experiences instead of a single global feed or ranking.
Enables clients and developers to choose their algorithms and give users freedom to explore content and feeds of their choice.
Helps detect and reduce spam and sybil clusters faster and cheaper through community curation of rankings.
Read More about our implementation with Lens and Farcaster in the integrations section
Marketplaces
Permissionless and open marketplaces require a verifiable reputation layer to aid users in making informed decisions before interacting with goods, software, applications, or sellers. Marketplaces experience huge pain points around fraud, scams and rug pulls. There is no easy and resilient way to attest or aggregate the reputation of a creator, NFT collection or in general a smart contract. For users, these marketplaces become the de-facto venues for search and discovery. The time and cost of finding useful and safe things on-chain is high and borne by the users.
A verifiable reputation system for marketplaces can help capture contextual peer-to-peer attestations or transactions to create a community sentiment or wisdom that powers reputation of creators/sellers/developers and make it easy and safe for users to transact on the marketplace.
An Open Ranking layer for marketplaces :
Enables transparency in how ratings, rankings, and recommendations are done on a marketplace and bring personalized search and discovery for users.
Helps bring down the cost and efficiency of trust and safety through community wisdom versus centralized teams that end up costing significantly higher.
Opens up the innovation surface for ranking systems for trust and fraud heuristics to the community instead of relying on a centralized authority's opinion.
Consumer and Developer marketplaces such as NFT platforms, App Ranking portals, Gitcoin, Uniswap Hooks, Metamask Snaps, Farcaster Frames, Lens Open Actions can utilize rankings and ratings for their specific contexts.
Read More about our implementation with MetaMask Snaps Permissionless Distribution Experiment in the integrations section
Consumer Apps and Wallets
The rapid growth of on-chain users, transactions, bots and agents has highlighted the need for ranking and reputation systems for consumer apps. Wallets, block explorers, onchain communities can power better user experiences for search and discovery.
An Open Ranking layer for apps:
Enables personalized and contextually relevant feeds or recommendations of most popular apps/tokens/NFTs in a users own onchain graph.
Drives composability of reputation graphs across different applications.
Brings social discovery as the key construct for users instead of a general or popular thing to do onchain.
Read More about our implementation of onchain feed prototype in the integration section
Governance and Public Goods Funding
Reputation aims to solve several key questions in governance: who to trust with what decisions, who gets how many votes on what decisions, and who gets how much resource allocation based on contribution.
An Open Ranking layer for communities:
Enables collectives to leverage reputation to better drive governance and funding decisions, capturing peer-to-peer trust signals
Makes possible the discovery of project impact based on onchain engagement data that is anchored by trusted members of the community
Facilitates building various reputation graphs that signal domain expertise of community members in various contexts, resulting in reputation-based vote weighting
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