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The rise and innovation of AI Layer 1 Blockchain platform With the rapid development of artificial intelligence technology, traditional Blockchain architectures can no longer meet the demands of AI applications for high-performance computing and complex data processing. This has led to the rise of Layer 1 Blockchain platforms optimized specifically for AI, which exhibit diverse characteristics in terms of technical architecture, application scenarios, and business models. This article provides an in-depth analysis of five leading AI Layer1 projects: Bittensor, Vana, Kite AI, Nillion, and Sahara. Bittensor: Decentralized AI Network Infrastructure Bittensor, as an early explorer in the Blockchain AI field, is committed to building an open decentralized artificial intelligence collaborative network. Its goal is to break the centralized barriers in traditional AI research and development, allowing more participants to contribute together.

The Southeast Asian encryption ecosystem is thriving: Insights and observations from the Devcon conference. Thailand is becoming a key player in the rapidly growing encryption and Web3 ecosystem in Southeast Asia. With a population of over 70 million, Thailand has widely embraced blockchain technology under government policies that support innovation and the growth of the digital economy. Tax exemptions for digital token investments further enhance Thailand's attractiveness as a blockchain investment hub. The adoption of blockchain across the Southeast Asian region is rapidly developing, thanks to a tech-savvy population and continuously improving infrastructure. This development momentum was particularly evident during the recent Devcon week, which hosted several important events, including the Thailand Blockchain Week, the 2024 EVM Summit, and activities related to NEAR. These gatherings highlighted Thailand's leadership position in the vibrant Web3 space in Southeast Asia, paving the way for further growth and innovation.

The combination of AI and Web3 brings new opportunities for Computing Power and data sharing, promoting the development of decentralized markets, improving AI model training and inference, and advancing finance, infrastructure, and new narratives. This integration aims to achieve fair and transparent resource allocation, better serving humanity.

The potential of the AI data track and the rise of Web3 DataFi In an era where the global competition is to build the best foundational models, while computing power and model architecture are certainly important, the true moat is the training data. This article will explore the potential of the AI data track and how Web3 DataFi is emerging as a new force in this field. The Importance of AI Data Computing power, models, and data are the three pillars of AI models. With the popularization of the transformer architecture and the gradual resolution of computing power issues, the importance of data is becoming increasingly prominent. Model training is divided into two stages: pre-training and fine-tuning, each requiring different types of data. 1. Pre-training stage: Requires a large amount of information such as text and code crawled from the internet. 2. Fine-tuning stage: A dataset that requires meticulous design and selection to cultivate specific capabilities of the model. These two types of data constitute the main body of the AI data track. Following

The rankings for author influence and article popularity in March 2025 have been announced, with five authors including Web3 Xiao Lu and Shi Si Jun making the influence list. Popular articles include "The Momentum of XRP, the Potential of Ripple," reflecting the current market trends and technological dynamics.