Problem

Today's blockchain explorers serve technical info: transactions in the latest block, internal transactions, and a hundred pages of ERC20 transactions in a SQL entry format.

Investors, Web3 operators, researchers, and the rest of us are manually translating this data into what we care about: the economic activity and profile of the address.

Today, after clicking through endless 42-digit Ethereum addresses, we hope a relevant wallet or transaction pops up.

This exploration is unintuitive and indirect.

Solution

The Map puts all addresses on a flat plane.

This way, address discovery becomes scalable and visually hinted. Similar behaving addresses are close together. Cluster structures indicate communities that own and trade together. The Map's search engine suggests addresses with high behavior similarity to the query.

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Machine Learning

Recent advances in AI uncovers insights from on-chain activity.

After aggregation of token-level data, address transaction histories, plus other Web3 data, A neural network is trained to integrate these sources into an unified view.

This is the start of Web3 native AI.

Wallet Labeling

In Web3, top performing wallets gave you the edge. The Map is the search engine for top performing wallets.

Verify new labels after comparing similarity to current labels.

Generate new labels by considering like candidates.

3AC Case Study

In this wallet labeling exercise, we expand the set of known 3AC wallets using The Map's search function.

Wallet labeling is the process of identifying notable wallets to monitor. Knowing the important wallets likely belonging to top investors and traders, one can set alerts for when assets are being moved, and follow these trades. Have your own wallets? For a demo using your own queries, send an email to lihan.the.map@gmail.com with the subject title “demo” and up to 5 addresses in the email body. Do not write anything else in the message.