@arbitrum 1/ On a high level, AnyTrust chains are a cheaper but less secure version of the #Arbitrum rollup. The idea is that not all the tx data will be stored on the $ETH L1 but rather off-chain by a data committee
@arbitrum 2/ By minimizing the amount of data submitted to the $ETH L1, #AnyTrust saves gas which results in lower tx fees. What is interesting about AnyTrust is the low trust assumptions
@arbitrum 3/ Assuming a data committee size of 20 nodes and a quorum of 19, an #AnyTrust L2 is safe as long as at least 2 out of 20 nodes are honest. These are much lower trust assumption than the ones behind conventional BFT chains which require more than 2/3 of nodes to be honest
@arbitrum 4/ This is true as there are at least two honest members and only one member can be outside of the quorum
@arbitrum 5/ Hence, if a quorum signs a promise to provide the data backing a batch of tx, itβs safe that that the tx data will be available and consequently itβs safe to only post the hash of the tx data on the L1, rather than the full data
@arbitrum 6/ But what happens if there are not enough nodes available to reach the necessary quorum? Well, if there no active quorum that is willing to sign statements, then the βfallback to rollupβ mechanis, kicks in
@arbitrum 7/ That means that tx data is posted on Ethereum (as normal #Arbitrum rollup data is), and new rollup states are confirmed after a challenge period
@arbitrum 8/ In conclusion, #AnyTrust achieves significant scaling advantages because thereβs no need to record L2 tx data on the L1 chain (similar to Metis)
@arbitrum 9/ #AnyTrust nodes rely on the committee to provide tx data if needed. So instead of having all the tx data stored on L1, only the hash of a batch of tx is recorded on the L1, saving a lot of gas and mitigating the main cost factor for running a rollup system
@arbitrum 10/ An additional benefit is immediate withdrawals to L1, as soon as the committee vouches for the validity of the state
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I spent months diving into the DePIN and AI narrative and have identified a few select winners.
One early-stage gem that will be a true game-changer is @vistaralabs.
A Hardware Availability Layer for Web3 that democratizes access to decentralized hardware.
Alpha-π§΅
1/ Decentralized AI/ML is the 100x narrative of this cycle.
If you manage to catch a leading play early, it can translate into life-changing gains.
Today, I want to unveil on of my main bets on the narrative.
[Pepe Image]
2/ @vistaralabs originally started out as a RaaS provider.
However, the RaaS market is a crowded game & while Vistara was quick to build a strong product that would def have been competitive, @_mayurc had a bigger vision.
Vistara wouldn't be "just another" RaaS framework.
Move rollups will be an epicentre of the bull market
My focus lies on @movementlabsxyz, a modular execution layer with @CelestiaOrg underneath that runs a parallel & EVM-compatible MoveVM
This will be valued in the billions and one of the biggest launches this year
Alpha-π§΅
1/ To reach mass adoption, execution layer innovation is key.
High-performance chains enable new use cases and provide a seamless UX to the end user with fast and cheap transactions.
2/ The EVM was the 1st programmable execution environment built on top of a blockchain-based trust layer. However, it does have significant limitations.
Execution layers that can overcome these limitations are hence highly sought after & will be the epicentres of this bull.
The modular expansion is just getting started and game-changing infrastructure projects are emerging
@dymension is a modular settlement layer (big)
@Celestia is a modular DA layer (bigger)
ββββββ is the modular interoperability layer (π€―)
Alpha-π§΅
1/ I spend 100s of hours doing research to stay ahead of the curve on the leading narratives.
For >2 years already, I have been putting a strong focus on the modular design space.
All with the goal to identify key pain points and the leading projects that address them early.
2/ Examples? π€π
Modular execution layers need scalable DA
> @CelestiaOrg solves this
Modular execution layers need scalable settlement
> @Dymension solves this