With Electric Capital’s recent Developer Report and Repository made publically available, there are endless opportunities to analyze protocol development through Electric’s repository. The protocol I decided to focus on was Osmosis. Osmosis is the biggest decentralized exchange on Cosmos that allows users to trade via AMM model. I created a python ETL script and Google Dashboard, which is the main focus on my analysis, I dug into the development activity for the protocol. Dashboard link can be founder here:

Optimism is an L2 Protocol for Ethereum that has been doing quite a lot of exciting experiments to guide the future of its protocol. One of the first pieces was the announcement of the airdrop and who would qualify for it. While noble in intent, some have claimed that the airdrop didn’t go as well as intended. As of 10/13, 88% of airdrop wallets decreased their $OP position by 99%. To stay optimistic about Optimism is that there is a second airdrop happening and their ecosystem is thriving. What if there was a way to learn from past on-chain data, to predict future trends in the airdrop with some on-chain sleuthing and data science? In this analysis, we look at what went well, what went wrong, and how users interacted with $OP Optimism before and after the token's launch.

The current airdrop system is broken. Sybils try to create multiple addresses try to obtain as many claims to the airdrop as they can. Farmers will game the system to get as much of the airdrop as possible before dumping it all away. For Hop, 16% of those that received the airdrop left it within 30 days of receiving their airdrop. 25% of airdroppers currently do not have the token in their wallet. What if there was a way to detect addresses that may dump a token before it happens through on-chain data? In this experiment, a supervised machine learning model tries to predict if a user will dump a token based on their previous on-chain address history

While crypto and web3 are aiming to build a better society and the internet, there are a lot of bad actors taking advantage of the current system. Sybil attackers and airdrop farmers are just. These are users who create multiple accounts or just do the bare minimum to collect free money. Once they receive their rewards, they will sell. To help defend against this behavior, Gnosis Safe has tasked the community to help identify potential airdrop farmers. Gnosis Safe is a multi-signature smart contract wallet that allows users to define a list of owner/signer accounts and a threshold number of signers required to confirm a transaction. Once the threshold of owner accounts have confirmed a transaction, the Safe transaction can be executed. I first noticed the issue of airdrop farmers after an analysis I did with Uniswap where most airdrop receivers dumped their tokens and did not participate in governance as intended.

If you are interested in the data check out the dashboard here (this is the cool stuff) →