transparenza

Current review platforms have the following flaws:

• Reviews are set by employees and friends.
• False reviews.
• More than one review per person with different accounts.

We propose a decentralized permissionless review registry for ERC20, ERC721 and ERC1155 tokens. You can only review a token if you are a holder of the token and have a valid proof of Person Id from WorldCoin.

An example use case for NFTs would be a situation where a user wants to check the following before acquiring an NFT:

1. Reviews of token gated chats and vibe.
2. Reviews of utility, meetings, roadmap, etc.

All these reviews are provided by past holders with a unique comment.

This public good is gasless for reviewers and is going to benefit every user and bring transparency. It is also possible to add a layer which rewards reviewers based on their number of ratings, kind of “Local review” badges.

We are verifying directly onchain that our reviewers are a person thanks to Proof of Person Id from Worldcoin. Furthermore, we verify onchain if the reviewers are holders of the ERC20, ERC721 and ERC1155 tokens.

A review has to be decentralized but not at a cost. That’s why we built our Review contract based on ERC 2771 and GelatoRelay. Reviews should be free for users to make; token creators (companies, DAOs, NFTs) can sponsor their reviewers’ gas costs. This way we achieve a Web2 experience of leaving reviews, but with the benefits from blockchain.

In order to index the reviews, we built our own subgraph on the new subgraph studio. Moreover, even though it was a hackathon, we did tests in our subgraph.

Subgraph: https://api.studio.thegraph.com/query/46766/transparenza-mumbai/v1.0.0

Our smart contracts are tested with Foundry and are verified on mumbaiscan and polygonscan.

Mumbaiscan: https://mumbai.polygonscan.com/address/0x85e8daec6ac680965404bd947c7d2967887fdf23

Polygonscan: https://polygonscan.com/address/0xe528ea78684d61f164b7e9f42ed9b2628e2ba531

On the frontend: we store the review metadata of our reviews in Filecoin thanks to WebStorage. We query all tokens data with the AirStack API, even the Lens profiles from the reviews. We query from our subgraph the reviews.