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Transformative Shift: COTI Leads the Future of Privacy with Ethereum Layer 2 Adoption

million, to fuel the holistic development of its ecosystem. This initiative is designed to support projects and developers who are aligned with COTI's mission of enhancing privacy, security, and scalability within the Ethereum ecosystem. The Ecosystem Growth Fund represents a significant investment in the future of blockchain technology and underscores COTI's dedication to fostering innovation and growth within the industry. Advancing Privacy with Garbling Circuits Technology COTI's transition to Ethereum Layer 2 signifies a strategic shift towards scalable privacy solutions within the blockchain space. The adoption of Garbling Circuits technology enhances the speed, efficiency, and security of COTI V2, positioning the firm as a pioneer in privacy-focused initiatives. Garbling Circuits technology opens doors to a wide range of applications, including privacy-preserving wallets, decentralized exchanges (DEXs), private AI training, governance mechanisms, and more. Part

Unlocking the Potential of Onchain AI and ML: Hyper Oracle Launches opML

any significant impact on performance. Additionally, opML is designed to be compatible with existing ML frameworks, making it easy for developers to integrate into their projects.

The launch of opML by Hyper Oracle is a significant milestone in the field of onchain AI and ML. By providing a flexible and performant approach for running large ML models on the Ethereum blockchain, opML opens up new possibilities for smart contract applications. With opML, developers can leverage the power of AI and ML to create smarter and more advanced smart contracts, enabling use cases that were previously thought to be impossible.

One of the key advantages of opML is its low cost and high efficiency. Unlike zkML, which requires extensive resources for proof generation, opML can run large language models on a laptop without any significant impact on performance. This makes it a practical solution for implementing large ML models like GPT-3.5 on the mainnet.

In addition to its performance advantages, opML is also designed to be compatible with existing ML frameworks. This means that developers can easily integrate opML into their projects without having to rework their entire ML pipeline. By leveraging existing frameworks, developers can take advantage of the extensive tools and libraries available for ML development, further enhancing the capabilities of opML.

While opML offers many advantages over zkML, it is important to note that both methods have their own strengths and limitations. zkML, with its use of zk proofs, provides the highest levels of security but suffers from limitations in memory usage, quantization, and circuit size limit. On the other hand, opML sacrifices some security for enhanced performance and flexibility, making it a more practical solution for running large ML models on the blockchain.

As the field of AI and ML continues to evolve, the introduction of opML by Hyper Oracle represents a significant step forward in onchain ML computing. By addressing key challenges in cost, security, and performance, opML opens up new possibilities for the practical implementation of large ML models on the Ethereum blockchain. With its low cost, high efficiency, and compatibility with existing ML frameworks, opML is poised to revolutionize the field of onchain AI and ML.

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