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SOL $83.16 -0.48%
TRX $0.3231 -0.05%
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ADA $0.2438 -0.89%
BCH $447.43 +0.27%
LINK $9.10 -1.39%
HYPE $39.49 -0.58%
AAVE $93.86 -2.66%
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earning

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.

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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