Raven Protocol
Crypto Overview
Project Overview
Raven Protocol was designed as a decentralized and distributed deep learning training protocol. The project aimed to create a network where users could contribute their computing resources to train AI models in a distributed manner, receiving RAVEN tokens as rewards.
Purpose and Utility
The intended purpose was to create a distributed computing network for AI training, with RAVEN tokens serving as the network's utility token to compensate computing resource providers. However, current research indicates that the project appears to be inactive, with minimal to no development activity in recent years.
Business Model Evaluation
The original business model was halal-compliant as it involved exchanging real computing resources for tokens. The concept involved legitimate value exchange between participants - computing power providers and those needing AI training resources.
Development and Adoption
The project's development appears to have stalled. The official website is no longer accessible, and there is no recent activity on development repositories or social media channels. This suggests the project may be abandoned, though this does not affect its halal classification as the underlying concept remained permissible.
Token Distribution and Governance
Due to the project's current inactive status, detailed information about token distribution and recent governance activities is not readily available. The original token distribution model involved rewards for computing resource providers.
Crypto Impact
To assign a comfort level investing in Raven Protocol from a halal perspective we need to determine whether it has a net positive or negative impact.
Towards this end, the table below is used to tally all the positive and negative considerations our members can think of related to Raven Protocol.
If you don’t see something that should be listed, positive or negative, feel free to submit your own contribution using the button “Contribute”.
We will review your contribution for accuracy before publication.
Importantly, you can review listed contributions and vote on which one you think has the greatest impact, positive or negative.
You only have one vote per asset so use it wisely.
You can change your vote if you change your mind.