Insights

Will the Senate overturn Biden’s Veto?

N+1 Analytics

09 Jul 2024 • 3 min read

In this edition, we explore SAB 12, Chainlink, the PancakeSwap airdrop and more.

💥 Cutting Edge with Shoal Research

In collaboration with Shoal Research, this week’s special feature will dive into EtherFi.

Key Terms

Restaking - A process where staked assets are used to earn additional yields without unstaking the original assets.
eETH - Synthetic tokens representing staked ETH, allowing liquidity while the actual ETH is staked.
EigenLayer - A platform integrated with EtherFi to provide additional yields on restaked assets.
Liquid Restaking Token (LRT) - Tokens that represent staked assets and can be used in other DeFi applications while earning staking rewards.

What is it? EtherFi is a liquid restaking protocol designed to let users stake their Ethereum while maintaining liquidity through synthetic tokens (eETH). It integrates with the EigenLayer platform to offer additional yields on these restaked assets.

What does it do? EtherFi provides a suite of products that allow users to stake ETH, earn yields, and spend crypto seamlessly. The protocol issues synthetic eETH tokens to represent staked ETH, which can be used in other DeFi applications. EtherFi's credit card, ether.fi Cash, allows users to spend their crypto directly by converting eETH to USDC or borrowing against their holdings.

What does it mean? EtherFi aims to reduce the friction associated with using cryptocurrency for everyday transactions. By maintaining liquidity of staked ETH and providing easy spending options, EtherFi enhances the usability and accessibility of crypto assets.

Why is it interesting? EtherFi offers a comprehensive approach to DeFi, combining staking, liquidity, and spending. Its integration with EigenLayer for additional yields and the introduction of a crypto credit card are significant steps towards mainstreaming the use of crypto assets in everyday transactions.

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💡 Coin Spotlight: The N+1 HODL

In this segment, we dive into the N+1 Hodl basket and discuss our altcoins.

Coin: Chainlink ($LINK)

Sector: Oracle

Narrative: Chainlink is a leading decentralized oracle network. The narrative behind Chainlink is therefore rooted in connecting information between the real world and blockchains.

Fundamentals: Chainlink offers features for cross-chain communication, including forms of data and compute. There are a variety of use cases including asset tokenization, DeFi, gaming, collectables and more.

Why do we hold: Chainlink is a well known Oracle with a variety of possible functions.

Performance: LINK is up 8% YTD. (Source: Coinmaketcap)

⏰ Crypto News Recap

Mt. Gox Repayments

Mt. Gox has begun steps to pay back creditors through their program called the ‘Rehabilitation Plan’. Mt. Gox was a crypto exchange that collapsed in 2014. Last Thursday, Mt. Gox moved $2.7 billion worth of Bitcoin to a new wallet. The move raised concerns about Bitcoin prices falling if Mt. Gox liquidates the assets.

The Latest on SAB 121

In May 2024, Joe Biden vetoed a resolution to overturn the Staff Accounting Bulletin (SAB121). Under SAB121, banks must hold a customer’s digital assets on their balance sheet. The House and Senate will need a two-thirds majority vote to override the veto. This vote could happen as early as this week, with expectations for Tuesday or Wednesday.

PancakeSwap Airdrop

PancakeSwap intends to airdrop 2.4 million zkSync tokens to users. zkSync is a layer-2 protocol working on Ethereum scaling through zero-knowledge (ZK) proof-based technology. The initiative will run from July 5th to August 5th and is meant to increase user engagement with zkSync and reward active users. In the past year, PancakeSwap implemented various new developments on ZKsync including seamless Swaps, Yield Farming, Prediction Markets, Fiat On-Ramp and more.

📚 N+1 Recommendations

The N+1 reading and media recommendations are sourced from our team of experts and offer weekly suggestions for learning more about blockchain, crypto, finance and technology.

This weeks recommendation: Deep Learning with Python