"GIZA"

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GIZA Protocol is the underlying infrastructure designed for Decentralized Finance smart agents, featuring non-custodial operations, decentralized execution, and protocol interoperability, empowering users to participate in on-chain finance more efficiently and securely.

The ZK coprocessor can be regarded as an off-chain computing plug-in derived from the modular concept. Its function is similar to the GPU in our traditional computers that shares graphics computing tasks for the CPU, that is, a processor that shares computing tasks in specific scenarios. . Under this design architecture, heavy data and complex calculation logic tasks that public chains are not good at can be calculated through the ZK coprocessor. The chain only needs to receive the returned calculation results, and its correctness is determined by ZK proof guarantees, and ultimately enables trusted off-chain calculations of complex tasks.

This article explores the intersection of cryptocurrency and artificial intelligence, highlighting the emergence of public blockchains as one of the most profound advancements in computer science history. It discusses how the development of AI is already having a significant impact on our world.

The experiments in the intersection of cryptocurrency and artificial intelligence are driven by the same forces that underlie the most promising use cases for cryptocurrencies—access to permissionless and trustless coordination layers, facilitating value transfer more effectively.

In the past two years, the development speed of ZK has far exceeded expectations. The two leading ZK Rollups, ZkSync, and Starknet, have made significant progress in performance and costs. This article explores Starknet's technical advantages in a straightforward manner and simplifies the explanation of the ZK proof process.

This article will explore the future development of AI and cryptocurrency, as well as explore investment opportunities, through seven modules: computing power cloud, computing power market, model assetization and training, AI Agent, data assetization, ZKML, and AI applications.

The core of DeFAI can be summarized into three key areas: the abstraction layer, the agent layer, and the application layer. These areas aim to push DeFi toward a more intelligent and automated future.

This article explores ten recent advancements and innovations in the DeFi space, covering technological upgrades, protocol innovations, ecosystem development, and integration with AI.

This article discusses how the decentralized features of Web3 can balance the centralization problem of AI, and proposes how to create new industrial value through Web3+AI in terms of computing power, data, platform, application, etc.

HyperAGI is a community-driven decentralized AI project aimed at creating true AI agents and fostering an autonomous cryptocurrency economy. It achieves this by integrating Bitcoin Layer 2 solutions, an innovative Proof of Useful Work (PoUW) consensus mechanism, and large language models (LLMs). The project is dedicated to realizing Unconditional Basic Agent Income (UBAI) and advancing a decentralized and equitable digital society through AI technology.

The intersection of blockchain and machine learning is close, but in decentralized reasoning, balancing cost and trust is a key challenge.

This article provides an in-depth analysis of 67 Crypto+AI projects, categorizing them from the perspective of Generative AI (GenAI), and explores how cryptocurrency supports AI and its associated benefits.

This article analyzes the combination of Web3 and AI Agents, focusing on the potential and development direction of this emerging field, and examines representative projects such as Spectral and GaiaNet.

Besides Depin, what kind of sparks can the interweaving of Web3 and AI ignite? What opportunities lie within the Sora track? This article also contemplates the possibilities of Web3 in the era of AI.

This article explains how blockchain technology can solve the current bottlenecks in training large AI models, including: data volume and privacy balance, cost and computing power, etc., and explores the interactive relationship between AI and Social.