MBA Mortgage Application Activity Index in the week ending July 11 in the United States

Run, train, earn: which GameFi projects to watch in 2024 According to the Messari report, in 2023, about 3.4 billion gamers brought the gaming industry $184 billion. The latter undergoes a paradigm shift once every 10 years, so the rise of the GameFi financial sector can be compared to

Overview of DA Solutions and Project Introduction In the current Web3 ecosystem, Layer 2(L2) faces data availability (DA) issues, mainly reflected in the trade-off between security and cost. To address this problem, the industry has proposed two DA solutions: on-chain and off-chain. On-chain solutions: Proto-Danksharding On-chain solutions refer to L2 continuing to use Ethereum as the DA layer and reducing data availability costs through Ethereum. Proto-Danksharding(EIP-4844) is the main solution for Ethereum to lower DA costs, introducing a new Blob transaction format. Transaction data uploaded by L2 will be temporarily stored in Blob format on the Ethereum consensus layer, providing L2 with more cost-effective data availability. This means that Ethereum will become a real-time bulletin board in the future, and the data on the board will be updated at intervals.

Data Availability Wars: Background, Ecology, and Prospect Analysis Overview Data availability ( DA ) projects are emerging rapidly, among which competitive ones include Celestia, EigenLayer, Avail, NearDA, and Covalent. According to the analysis, the core of DA is not complicated. The simplest solution can be implemented on a single machine, while the most complex, like Celestia, achieves decentralization through sampling modes. DA is essentially storage, which is costly. If Ethereum-level security is not required, choosing a DA solution involves a trade-off between cost and security. The key principle of using the DA layer is: the higher the value of the service, the more secure the DA should be. This report analyzes the background, ecology, and prospects of the DA track, including Vitalik Buterin's views on DA and an overview of major DA projects. Based on a comprehensive analysis, DA may become decentralized in the future, with 7-8 major DA players in the market.

FTX Liquidation Controversy: Disputes and Reflections on Creditors' Rights Recently, a screenshot of a document regarding the bankruptcy liquidation of FTX has caused a heated discussion on social media. According to the document, if users belong to restricted foreign jurisdictions, their claimed funds may be confiscated. Even more shocking is that among the claimed funds from "restricted countries", as much as 82% comes from Chinese users. Due to China's strict regulations on cryptocurrency trading, these users may face the risk of being classified as "illegal", thereby losing their eligibility for claims. This situation implies that these users not only cannot recover their losses, but their assets may also be "legally confiscated". This message, once announced, immediately sparked a strong reaction in the community. Many questioned the actions of the liquidation team, believing it to be an excuse to shirk responsibility. Some comments described this decision as "American-style robbery," expressing dissatisfaction with the treatment of Chinese citizens. Despite China's strict limits on cryptocurrency trading.

Grass Network: Decentralization platform for web scraping Grass is a decentralized network scraping platform deployed on the Solana chain that combines AI, Depin, and Solana technology. It positions itself as the data layer for AI, aiming to help organizations train AI models by utilizing individuals' unused internet bandwidth. Grass achieves web scraping through a browser extension and rewards users with Grass Points for contributing bandwidth resources. The project's goal is to redefine the internet incentive structure, allowing users to benefit directly from the web and ensuring that the value of the internet is held in the hands of users. Currently, the Grass network has over 2 million user-operated nodes, providing a vast amount of data for AI models.