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Bitcoin Spot ETFs Attract $3 Billion in One Month

Bitcoin Spot ETFs: A New Era in Investment The recent launch of Bitcoin spot exchange-traded funds (ETFs) in the United States has ushered in a remarkable financial phenomenon, capturing the attention of investors and analysts alike. Within just a month, these pioneering investment vehicles have attracted over $3 billion in net flows, a figure that notably eclipses the initial performance of gold ETFs when they made their market debut two decades ago. This trend signals not only a shift in investor sentiment but also a redefinition of traditional asset allocation strategies. For those looking to dive deeper into this area, the Comprehensive Guide to Spot Bitcoin ETFs offers valuable insights into navigating these new financial waters. Key Highlights Impressive Net Flows : Bitcoin spot ETFs have drawn over $3 billion in net flows within their first month, demonstrating robust market enthusiasm. Comparison to Gold ETFs : This performance surpasses that of gold ETFs at their inc

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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