Nft - Fair Trade

PROBLEM Royalties on secondary sales are a new way that creators can benefit from their work. Often the value of their work accrues over time but, in the past, the creator has got little benefit. This benefit only went to intermediators, something that the blockchain tries to eliminate. The blockchain allows this to be encoded in a smart contract. This became a common feature of NFTs however, it cannot be enforced in smart contracts and is up to the market. Many markets enforce, but plenty do not. Over-the-counter trades, also prove a problem. Many members of the crypto community also argue that enforcement is also against the ethos of the movement. Our position is that there can be a choice, just as a creator can chose not to ask for a royalty, however a choice to ignore the wishes of a creator should come with other cost.

PROPOSED SOLUTION NFTs that specify royalties will expose to buyers whether previous royalties have been paid. This will create a bifurcation in the market between those that care about cost more than the creation itself. Some creations will naturally fall into the 'flipper' category and traded for profit. Others will appeal to holders who keep them more for their artistic merit or collectability. It is expected that this will improve the desirability of those with a clean royalty record. This will likely flow through to price, counterbalancing the royalty in some way. The solution will therefore incentivise payment of royalties by: • Creating a sigma around unpaid royalties and a social norm that they should be paid, so that there is a value differential in price that counterbalances the royalty cost. • Exposing the royalty payment status at point of sale • Highlighting cases where the royalty has not been paid • Exposing suspiciously low prices (over the counter side-payments) • Exposing attempts to avoid payment using various techniques such as wrapping • Allowing users to transfer between wallets without being market as a

To create a Classifier, the project needed to create a new subGraph to access the required data from the chain. And populated a Postgress data base. This process can take considerable time for highly trades collection so we have limited it to a single collection HashMasks (which unfortunately does not return metadata on the APIs we tried, so we have simulated the image in the demo.)

The data is then analysed for marketplace transactions and NFTs scored accordingly.

The back-end uses: thegraph (subgraph), nodejs, typescript, tsed (backend framework), postgresql, docker.

The UI uses an API to fetch the scores from the Classifier as a list of transactions and displays them. It uses the Zora API to fetch the metada for the NFT (although we could not find on in time that work for HashMasks).

Work on allowing the user to attestations to the state of an NFT using an EAS schema and onchain attestation was incomplete at the time of this submission.

The Front-end tech uses: scaffold.eth, hardhat, ether.js, next.js, viem