Cambrian Use Cases: Making the Most for dApps Developers
Let's examine potential Cambrian use cases for decentralized applications (dApps) in the following fields: Intent Solver, AI Inference, Training AI/ML Models, and Threat Detection Network. This article will also describe Cambrian's overall capability in helping developers build, deploy, and manage specific Actively Validating Services across the Solana blockchain.
Introduction
Cambrian lets developers launch AVSs across multiple Solana restaking protocols. Based on the Solana blockchain network, we have designed a comprehensive solution that streamlines developing, deploying, and managing Actively Validated Services (AVS). Another term for it is Node Consensus Network (NCN), which is also used in the business.
Using our SDK, articulated in a single command line solution, developers can consolidate the entire AVS lifecycle from concept to production within the Solana blockchain. Our modular solution, which includes pre-built models and standardized interfaces, enables AVSs to access a larger pool of staked assets across various Solana restaking protocols. This way, developers can significantly optimize their costs and save time, gaining a competitive edge in their business. In other words, Cambrian makes restaking as easy as ever for the Solana community, becoming an invaluable tool for developers, restakers, and operators.
Use Cases: Discovering the Cambrian Potential
Now, it is time to see how the Cambrian solution can be implemented in various AVS deployment and maintenance cases. Let’s dive into the sea of Cambrian!
Intent Solver Networks
Subject: Developing and deploying intent solvers network to implement workflows under predefined conditions to improve UX.
Reason: The lack of solvers to implement user intents is a well-known issue, though, in an ideal world, all intents must meet their solvers. The arising deficit may lead to the development of unwelcome centralized solutions. As ‘simple’ transactions are limited in functionality, intents are the next step to improving the UX of blockchain applications.
Action: The key is that intent solver architecture can be efficiently developed as a Cambrian-based AVS. For example, the task is to improve the UX based on the set of conditions submitted by end users. This triggers on-chain actions fueled by preset logic. Then the magic happens: task performers discover intents and analyze several routes to execute them. Finally, they propose the best possible one. After that, attestations verify if that is the most optimal and correct route and approve the whole process.
To provide economic security to the user, inactive intent solvers or those that attest wrong routes will be slashed. So, this is a simple use case of how the UX can be improved based on the Cambrian AVS solution.
AI Inference
Subject: Building an AI-based network of nodes that offers a secure solution to protect user privacy.
Reason: The introduction of AI has brought plenty of benefits, making our lives easier in many ways, from design creation to content development, and beyond. However, significant security concerns remain with private data, which is an apparent downside of AI. Users need a safe and decentralized way to ensure that AI models like LLMs (Large Language Models), analyzing their data, do not collect or transfer it to a third party at the same time.
Action: Developers can rely on Cambrian to help build an AVS as a network of nodes which get requests from users to execute LLM prompts. This solution can help keep personal data secured, preventing it from leaks, thus making the whole scheme safer.
Training AI/ML Models
Subject: Developing a secure AI/ML training network that protects users’ privacy.
Reason: Training correct AI/ML models requires plenty of data to gather and use. However, this data may not be available because the owners strive to ensure privacy. For example, Google introduced a new methodology of training models called Federated Learning that keeps data on-device, making it secure.
Action: Nodes in the network can train models' in-house' without sharing their sensitive data, which can then be aggregated to form an external model. Cambrian can help implement this solution with a network of performers to train a model. Attestations validate the model accuracy increases in each stage to guarantee no improper activity has occurred.
Threat Detection Network
Subject: In the real world, code may also not be safe because of human error. This statement can be applied for blockchain where the codebase is public and immutable. Threat detection is a proactive process to identify unauthorized access to network data and resources. So the task is to shield your network from potential hacks.
Reason: You can’t prevent hacks in web3; however, detecting them is an achievable task. This kind of service seems to be in great demand from the crypto community.
Action: Using Cambrian, developers can build a network of hack detectors to monitor transactions on the blockchain. Task performers can scan block data and run AI models to identify potential hacks and notify the users as soon as possible for protective action.
Other Use Cases
It is worth noting that the use cases mentioned above do not limit the Cambrian AVS solution's potential future applications. It can be engaged in the development, deployment, and maintenance of a variety of other Solana blockchain-based architectures, like GameFi, dynamic NFTs, sports betting apps, including gambling, or tokenized assets.
For example, football fans are passionate about their clubs and it can be natural for them to use their expertise about the sport to earn money by betting online on game outcomes. Users need such fantasy platforms on blockchain to ensure they can utilize their skills without getting disturbed by centralized authorities. Using Cambrian AVS solution, developers can build a network of oracles to provide all the relevant sports data for your betting app. Then, attestators verify if the data is correct and sign off on the task using digital signatures.
Conclusion
Simply put, Cambrian offers an efficient tool for AVS developers regarding intent solving, AI inference, training AI/ML models, threat detection networks, and other use cases. With its go-to solution, you are just a click away from developing, deploying, and maintaining an AVS of your dream based on the Solana blockchain.