ZAN 与 Mysten Labs 合作推进 Web3 基础设施开发

深潮Publicado em 2024-08-14Última atualização em 2024-08-14

ZAN是蚂蚁数字科技的Web3技术品牌，计划与Mysten Labs一起在Sui上运行RPC节点。

中国香港 – 2024年8月13日，星期二 - Mysten Labs是一家Web3基础设施公司，也是Sui区块链的开发公司，今天宣布与蚂蚁数字科技的技术品牌ZAN建立合作伙伴关系。

通过整合Sui，ZAN旨在加速其Web3应用程序的开发和采用。该合作将专注于为Mysten Labs在两个关键领域提供技术支持：KYC基础设施和RPC节点服务。ZAN将为在Sui上构建的项目提供KYC服务，帮助区块链提供必要的合规解决方案。此外，ZAN还将正式推出Sui的RPC节点服务，增强区块链在亚太地区开发者和用户的可扩展性和可访问性。

ZAN首席运营官Cobe Zhang表示：“我们很高兴能与Mysten Labs建立合作关系。Sui是一个致力于技术和价值创造的明星公链，拥有丰富的生态系统和多样的应用。同样，ZAN源自一个技术驱动的团队，我们致力于为Web3行业构建下一代技术基础设施。我们期待彼此之间的更多技术交流，共同推动整个行业的创新和发展。”

Mysten Labs和ZAN将共同探索支付、数据、数字身份等领域的合作机会，目标是提供创新解决方案，惠及消费者、企业和本地社区，并着眼于发掘和支持Web3超级应用的机会。双方致力于培养一个合作环境，并期待在实现合作伙伴关系的里程碑时分享最新进展。

Mysten Labs的首席产品官兼联合创始人Adeniyi Abiodun表示：“我们很高兴能与ZAN团队和更广泛的蚂蚁数字科技合作，将e-KYC解决方案推向更广泛的数字资产生态市场。这次合作标志着Sui和香港及更广泛的亚太地区Web3生态的重要里程碑。通过结合我们的优势，我们可以加速创新应用的开发，这些应用将惠及数百万用户。”

关于Sui

Sui是基于第一原理从头重新设计和构建的L1公链，旨在使数字资产的拥有变得快速、私密、安全且人人可及。其基于Move编程语言的以对象为中心的模型，支持并行执行、亚秒级最终确定性和丰富的链上资产。凭借水平可扩展的处理和存储能力，Sui以低成本支持广泛的应用程序，并以无与伦比的速度运行。Sui是区块链领域的跨越式进步，是一个为创作者和开发者提供构建用户友好体验的平台。了解更多信息: https://sui.io

关于Mysten Labs

Mysten Labs是由分布式系统、编程语言和密码学专家组成的团队，其创始人曾是开创性区块链项目的高级管理人员和首席架构师。Mysten Labs 的使命是创建 Web3 的基础设施。了解更多信息：https://mystenlabs.com

关于ZAN

作为蚂蚁数字科技的Web3产品和服务技术品牌，并由蚂蚁链开放实验室的TrustBase开源技术栈提供支持，ZAN为商业创新提供丰富且可靠的服务以及Web3发展平台。ZAN产品系列包括ZAN eKYC、ZAN KYT、ZAN智能合约审查、ZAN 节点服务，并且还有更多产品即将推出。了解更多信息: https://zan.top

关于蚂蚁数字科技

蚂蚁数字科技是蚂蚁集团的数字技术子公司。蚂蚁数字科技继续推动数字技术的发展和应用，基于其在AI、隐私计算和安全技术方面的专业知识，推出 ZOLOZ、mPaaS和ZAN等领先产品。蚂蚁数字科技致力于与不同行业的合作伙伴合作，支持中小金融机构的数字化转型，帮助服务行业的中小企业实现数字化运营，并促进跨行业的数字化协作。蚂蚁数字科技的国际业务运营收入在2023年增长了300%。

媒体联系

lexi.wangler@mystenlabs.com

Leituras Relacionadas

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

Chip stocks led a broad decline in US markets, with the Nasdaq dropping 2.2% and the S&P 500 falling 1.4%. This selloff reflects a dual challenge for the once-high-flying AI hardware trade: rising interest rate expectations and growing investor impatience for clear returns from massive AI capital expenditures. The pressure was most acute on hardware leaders. Nvidia fell about 4%, dipping below a $5 trillion market cap, while Micron plunged 13.2% ahead of its earnings report. Declines across memory, storage, AI, and mobile chips indicated a sector-wide retreat. The selloff spread globally, with South Korea's KOSPI index dropping nearly 10% as key suppliers SK Hynix and Samsung recorded double-digit losses. Investors appeared to be taking profits from the most crowded trades first. Macro headwinds intensified as market expectations shifted toward a more aggressive Federal Reserve. Forecasts for multiple rate hikes in 2026 pressured high-valuation tech stocks, which rely on long-term growth projections that become less attractive as discount rates rise. Concurrently, investors are scrutinizing the profit potential of the immense AI spending by cloud giants like Alphabet, Amazon, and Meta. While these expenditures drive demand for chips and hardware, the market is now questioning whether AI services will generate sufficient returns to justify the ongoing costs. This adjustment is not necessarily a bubble burst but a recalibration. AI demand fundamentals remain, but the narrative of endless growth can no longer fully offset concerns over higher interest rates and a longer path to profitability. Near-term direction may hinge on Micron's upcoming earnings guidance and incoming inflation data, which will influence both the AI demand outlook and the Fed's policy path. The market is transitioning from blindly buying growth to demanding clearer visibility on returns.

marsbitHá 53m

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

marsbitHá 53m

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

OpenAI's new paper "Reinforcement Learning Towards Broadly and Persistently Beneficial Models" explores training AI to maintain safe, helpful, and honest behavior even under pressure, in unseen scenarios, or after being fine-tuned for harmful purposes. Moving beyond simple rule-based "don'ts," the research focuses on cultivating "beneficial traits" like honesty, risk-awareness, corrigibility, and transparency. It investigates if reinforcement learning (RL), often prone to "reward hacking" where models exploit loopholes, can instead be used to instill robust, generalized positive behaviors. Researchers created a multi-domain synthetic dialogue dataset covering areas like healthcare and law. They trained a model by replacing 5% of standard RL data with "beneficial trait" data. This model outperformed the baseline in 83% of 53 evaluations, showing average gains of 9.1% in alignment, safety, and helpfulness. Crucially, improvements generalized: a model trained only on healthcare "good behavior" data also performed better in 17 out of 19 non-healthcare alignment tests. The paper also tests "alignment persistence." When subjected to adversarial prompts or harmful fine-tuning, the beneficial trait model showed greater resilience, with smaller performance drops and less "spillover" of bad behavior to unrelated tasks. While not a complete solution, this work suggests a shift from post-hoc correction to proactively shaping robust, principled AI behavior, a critical step for deploying models in high-stakes, complex decision-making scenarios.

marsbitHá 56m

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

marsbitHá 56m

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

Goldman Sachs discusses the current AI investment boom, arguing it is not a simple repeat of the 1999-2000 bubble. Unlike the dot-com era, current valuations are not wildly out of control because earnings expectations are rising in tandem with stock prices. The key issue is that market prices have already priced in substantial optimism, making them sensitive to any narrative shifts. The core risk has shifted from "valuation bubble" to "earnings bubble." While strong profits from companies in semiconductors, cloud infrastructure, and data centers currently outweigh valuation concerns, this is dependent on the ongoing capital expenditure cycle. The true test will be whether these robust earnings can be sustained after the investment cycle peaks. Current market prices imply optimistic assumptions, such as faster AI adoption, higher productivity gains, and a larger share of economic profits going to capital owners. The AI boom may also be masking relative weakness in the non-AI sectors of the economy. As a result, market volatility is expected to rise. Goldman recommends that investors consider maintaining exposure while adding downside protection, such as put options, to manage potential drawdowns.

marsbitHá 59m

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

marsbitHá 59m

Zuckerberg Enters the Prediction Market

Mark Zuckerberg's Meta has entered the predictive markets arena with a new app called "Arena." Currently in an early experimental phase, Arena will initially operate as a standalone app using a points-based system where users can predict outcomes on politics, sports, and world events without real money. Meta plans to leverage its vast daily active user base for growth. The move comes as the predictive market industry sees explosive growth, with platforms like Polymarket and Kalshi handling hundreds of billions in volume. While a points system avoids initial regulatory hurdles, Meta hasn't ruled out introducing real-money betting later. The strategy aligns with Meta's pattern of adopting popular trends, though challenges remain, including regulatory scrutiny and the historically mixed success of its standalone apps.

Foresight NewsHá 1h

Foresight NewsHá 1h

Semiconductor Stock Rebound: Is the Technical Correction Over or a Trend Reversal?

The core of recent semiconductor stock volatility is not about daily price swings, but rather the market questioning whether AI-driven semiconductor pricing has entered a new phase. Following a sharp sell-off in Korean stocks on June 23rd, led by Samsung and SK Hynix, a subsequent rebound is seen more as a technical positioning adjustment rather than a confirmed trend reversal. The key variable is HBM (High Bandwidth Memory), essential for AI chips. Its supply-demand imbalance granted memory makers significant pricing power. The current market focus is on whether this dynamic remains strong enough to justify elevated valuations. All eyes are on Micron's upcoming earnings report. The critical factor is not whether results meet already high expectations, but whether the company's guidance confirms that AI memory pricing power, order visibility, and future margins are still expanding. Micron's outlook will serve as a crucial test for the broader AI semiconductor chain, including Samsung, SK Hynix, and other infrastructure players. The recent bounce appears to be a pre-earnings positioning repair. For it to evolve into a sustained uptrend, concrete evidence is needed that the AI infrastructure expansion cycle's fundamentals—particularly for high-end memory—remain robust and can continue to surpass elevated market expectations. The risk is that strong demand alone may not be sufficient if future guidance hints at peaking momentum or increasing supply-side pressures.

marsbitHá 1h