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BTC $76,519.85 -1.64%
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XRP $1.39 -2.01%
SOL $83.72 -1.75%
TRX $0.3235 -0.54%
DOGE $0.0990 +1.05%
ADA $0.2462 -0.65%
BCH $447.07 -0.21%
LINK $9.23 -1.10%
HYPE $40.01 -5.40%
AAVE $97.36 +1.32%
SUI $0.9229 -0.77%
XLM $0.1630 -3.11%
ZEC $336.17 -5.96%

earn

Bitget launches the first course of the Blockchain4Youth Learning Center, collaborating with Bondex to build a Web3 talent channel

Bitget officially launched the "Blockchain4Youth Learning Center" with its first course, aimed at providing a systematic blockchain learning path for young people and further connecting knowledge learning with career development. After completing the course and passing the assessment, students will receive a certificate of completion issued by Bitget's Chief Marketing Officer Ignacio Aguirre Franco, serving as an official certification of their Web3 capabilities. Certificate holders can gain more industry exposure and priority employment opportunities among the partner employers of the Blockchain4Youth Talent Alliance.To bridge the gap from learning to employment, Bitget has partnered with the Web3 recruitment platform Bondex to provide students with a transparent job-seeking channel and a talent pool connection mechanism. Bondex co-founder Ignacio Palomera stated that the project aims to address the real issues faced by learners, such as "lack of industry connections after completing the course, insufficient effective certification, and unclear job-seeking paths," building a more direct bridge between young talent and recruiting companies.So far, the Bitget Blockchain4Youth series of projects has attracted over 15,000 young participants. Bitget CMO Ignacio Aguirre Franco pointed out that the goal of the learning center is to transform young people's interest in Web3 into a tangible entry path. As the program continues to advance, Blockchain4Youth is gradually evolving from a single event project into a long-term infrastructure focused on Web3 education, career guidance, and talent development.

first_img Chief Economist of New Fire Group, Fu Peng: The essence of Bitcoin perpetual contracts is that large holders earn rent from long-term positions, while retail investors pay for leverage to go long

The newly appointed chief economist of New Fire Group, Fu Peng, stated on Twitter that the underlying business model of Bitcoin perpetual contracts is essentially the same as the "rollover fee/overnight fee" in traditional finance's gold and industrial commodity spot exchanges.Fu Peng pointed out that back in the day, gold exchanges settled through daily forced liquidation, with longs and shorts paying each other rollover fees. When retail investors held a large number of high-leverage long positions, the rollover fee became the most stable and hidden source of income for the platform. Nowadays, Bitcoin spot platforms mainly rely on perpetual contracts, with both sides settling the funding rate every 8 hours. When longs dominate, retail investors holding long positions continuously pay funding rates to shorts.Although the platform does not directly collect this fee, it significantly enhances trading activity, open interest, and liquidity, indirectly generating a large amount of fee income and forming a stable and substantial cash flow. Essentially, it is a business model where large players/institutions "collect rent" from long-term holdings, retail investors pay for leverage to go long, and the platform indirectly takes a cut.

Lido discloses the impact of the Kelp security incident: approximately 9% of EarnETH exposure affected, core staking assets are secure

Lido has released the latest developments regarding the Kelp security incident, stating that its Earn series vaults are working with the management to address the issues, which involve two major risk points: the rsETH exposure and the liquidity tension in the lending market. Lido emphasizes that the core staking protocol has not been affected, and both stETH and wstETH remain safe and stable.Currently, only the EarnETH vault has an approximately 9% TVL exposure to rsETH, and related deposits and withdrawals have been suspended by the management, awaiting a solution. Approximately $70 million in ETH has been recovered from the previous attack, and the subsequent asset recovery and loss distribution are still in progress. In response to liquidity pressure, the management has reduced leverage and optimized the position structure, significantly decreasing the wETH debt exposure. If losses ultimately occur, EarnETH will activate a $3 million "first loss protection mechanism" (funded by the DAO). As for other vaults, DVV and EarnUSD have not been affected and are operating normally; the GGV sub-vault is currently experiencing negative returns due to the combination of circular staking strategies and rising lending rates, but adjustments are ongoing. Withdrawal requests submitted by users will be processed based on valuations prior to the incident.

Coinbase upgrades its anti-fraud system, integrating machine learning with a rules engine, reducing response time to a few hours

Coinbase stated that it is optimizing the rule creation process in its anti-fraud system by integrating machine learning models with a rules engine, achieving more efficient risk management. It also proposed a dual-track strategy of "models responsible for long-term defense, rules responsible for rapid response," and built a unified framework to create a feedback loop between the two: rules are used to capture new types of fraud and train the model in reverse, thereby continuously enhancing overall defense capabilities.In terms of specific optimizations, Coinbase has transformed the previously manual rule creation process into a data-driven and automated recommendation system by restructuring data, automating schema evolution, and introducing notebook-based analytical tools, significantly improving efficiency. Among these improvements, the performance of rule backtesting has increased by more than 10 times, and the overall response time has been reduced from several days to a few hours. Additionally, the new system uses machine learning to recommend parameters, helping to reduce false positive rates while combating fraud and minimizing the impact on normal users. Coinbase indicated that the next step will be to advance event-driven automatic rule generation and explore the "one-click conversion" of efficient rules into model features, further moving towards an automated risk management system.
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