Recently, Bitcoin's price has surpassed the historical high of 112,000 USD, but the global financial market is facing new challenges that may lead to a short-term adjustment. Concerns over the U.S. debt issue have arisen, causing the stock market to fall. Nevertheless, the inflow of funds into the Crypto Assets market remains strong, and regulatory progress has a positive impact on the industry's development. Investors should follow changes in the external environment.

During the US election, Crypto Assets became a hot topic, and candidates' attitudes influenced the prices of related Tokens. One candidate has shifted to support encryption, increasing the appeal of donations, while another has taken a conservative stance. Despite the rise in the importance of Crypto Assets, economic issues remain the core concern for voters.

HighFreedom is a cryptocurrency trader with a macroeconomic background, sharing his trading strategies and insights into the market. His main strategies include holding Bitcoin during a bull run to gain overall profits, as well as going long at the right time to increase his Bitcoin holdings. He emphasizes the importance of controlling leverage and risk management, and follows the impact of the US stock market on the crypto market. Newbies should improve their trading skills through systematic learning and multidimensional analysis.

The crypto world is undergoing alienation, with the core business shifting to selling coins, while innovation and user experience are gradually being neglected. The industry's focus has shifted to the circulation link, causing a loss of attention from the project party and investors on product development, resulting in a monopoly by intermediaries and increased risks. The cyclical characteristics of the market will drive a future reconstruction of innovation and value.

This week, the crypto market saw a significant pump, but a certain project collapsed due to issues with the market-making protocol, prompting a reflection on industry ethics. Meanwhile, the fields of AI and payments are accelerating development, and the regulatory trends related to policies are also revealing new opportunities and challenges for the future market.

Last week, there were 13 financing events in the global Blockchain sector, with a total scale exceeding 137 million USD, a decrease from the previous week. Major investments were concentrated in the DeFi, Web3+AI, and infrastructure sectors, with projects such as Dinari and Camp Network receiving funding of 12.7 million and 25 million USD, respectively. At the same time, the crypto venture capital firm dao5 completed a fundraising of 222 million USD, focusing on stablecoins and asset tokenization.

The evolution of AI training paradigms: from centralized control to a technological revolution of decentralized collaboration
In the entire value chain of AI, model training is the most resource-consuming and technically challenging stage, directly determining the model's capability ceiling and actual application effectiveness. Compared to the lightweight calls during the inference phase, the training process requires continuous large-scale computing power input, complex data processing workflows, and high-intensity optimization algorithm support, making it the true "heavy industry" of AI system construction. From an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the focus of this article, Decentralization training.
Centralized training is the most common traditional method, completed entirely by a single organization within a local high-performance cluster, where all training processes, from hardware, underlying software, cluster scheduling systems, to all components of the training framework, are coordinated and operated by a unified control system. This deeply collaborative architecture enables memory sharing and gradient synchronization.

Aliyun has recently significantly dropped the API call prices for the Tongyi Qianwen model, promoting the development of AI applications. The company has launched multiple large language models with different parameters to meet various needs. This "drop + Open Source" strategy addresses developers' pricing and quality issues, prompting other companies to join the price reduction trend. With technological advancements, the related industry chain is expected to welcome opportunities, but the commercialization process still faces uncertainties.