usual.money ：RWA去中心化稳定币

Odaily星球日报Pubblicato 2024-08-10Pubblicato ultima volta 2024-08-10

Introduzione

稳定币是加密货币行业的基础，主要用于大规模支付和推动行业普及。截至2024年7月30日，稳定币总市值为1680亿美元，其中USDT和USDC占约90%。Tether和Circle在2023年创造了超过100亿美元的收入。去中心化稳定币项目不断涌现，旨在打破这种垄断。usual.money引入美国国库券作为抵押物，结合以太坊智能合约，提供透明度和安全性。Usual Labs在2024年4月完成700万美元融资，并于7月宣布启动主网。

Why usual?

  • 稳定币可以说是加密货币行业的根基，人们需要稳定币来作为大规模支付的用例，同时稳定币也在行业的 Mass Adoption 中发挥着关键作用。截至 2024 年 7 月 30 日，稳定币总市值为 $ 168 B，而从下面这张稳定币市值概览图中可以看出，两大中心化稳定币，USDT 和 USDC 加起来占据了稳定币总市值的约 90% 。

usual.money ：RWA去中心化稳定币

图源 Defillama， 2024 年 7 月 30 日数据

  • 稳定币是 Crypto 的印钞机，两大巨头 Tether 和 Circle 在 2023 年创造了超过 100 亿美元的收入，估值超过 2000 亿美元。Tether 在 2024 年 Q1，更是创纪录地获利 45.2 亿美元。如此巨大的获利垄断与加密精神显然相悖，因此各类去中心化稳定币项目源源不断。根据抵押额度，去中心化稳定币可以分为以 MakerDAO（DAI）为代表的超额抵押，以 Ethena（USDe）为代表的等额抵押，以及不足额抵押（目前没有跑出大市值项目）。

  • 去中心化稳定币有过不少成功的项目，然而这些项目的抵押物普遍是加密资产，因此需要设计一套精密的机制来对抗币价的波动。而如果引入 RWA（现实世界资产），这个问题将迎刃而解。RWA 正在增长且方兴未艾， 2023 年区块链上的 RWA 增长超过 800% 。usual.money 引入了美国国库券作为抵押物，同时基于以太坊智能合约提供透明度和安全性，将利润归还给社区和贡献者。这一设计可以说是 tether-on-chain，兼具了中心化稳定币协议的 1: 1 RWA 特性以及链上的安全与透明。

usual.money ：RWA去中心化稳定币

图源 https://docs.usual.money/start-here/why-usual

背景

  • 2024 年 4 月 17 日，Usual Labs 完成 700 万美元融资，IOSG 和 Kraken Ventures 领投，GSR、Mantle、Starkware、Flowdesk、Avid 3、Bing Ventures、Breed、Hypersphere、Kima Ventures、Psalion、Public Works 和 X Ventures 等参投。

  • 创始人 Pierre Person 曾是法国政治家、国民议会议员，最近还担任总统政党副主席，带头推动了该国的加密资产立法。

  • 7 月 10 日，Usual 宣布其主网启动。截至 8 月 6 日，项目 TVL 为 $ 146 M。

  • 根据 Coinmarketcap 8 月 6 日的数据，目前 USD 0 接近 95% 的交易都在 Curve 的 USD 0/USDC 池子，有着 $ 11.33 M 的流动性。同时，在 Maverick Protocol 上有 Aave 的稳定币 GHO 和 USD 0 的 LP，目前 TVL 为 $ 100 k。

  • 目前 USD 0 在借贷项目 Morpho 上有由 MEV Capital 策划的金库，USD 0 USD 0++/USDC 和 USD 0++/USDC 的流动性池，以 86% 的 LLTV（Liquidation Loan-to-Value，清算贷款价值比）有着总共接近 $ 30 M 的抵押。通过这个池子可以同时获得 Usual 的 Pills 奖励（详见文末交互章节）以及 Morpho 代币奖励。

  • $USUAL TGE 预计在 2024 年 Q4，其中 90% 的 USUAL 代币分配给社区。

机制分析

抵押与铸造

  • $USD 0 是首个聚合各种美国国库券代币的 RWA 稳定币，可以通过两种不同的方式在 Usual 上铸造：

    a. 直接 RWA 存款：用户将符合条件的 RWA 存入协议，并以 1: 1 的比例获得等值的 USD 0 。

    b. 间接 USDC/USDT 存款：用户将 USDC/USDT 存入协议，以 1: 1 的比例接收 USD 0 。这种间接方法涉及第三方抵押品提供商，他们提供必要的 RWA 抵押品。这使用户无需直接处理 RWA 即可获得 USD 0 。

    RWA 增长显著，但仍然只占加密货币市场基础资产的一小部分，主要挑战在于流动性问题，包括机构在二级市场清算 RWA 持有量面临的困难，以及散户投资者无法在核心 DeFi 生态系统中获得 RWA 回报。通过 USD 0 ，Usual 能够无缝整合来自 Hashnote 等平台的 RWA 代币流动性。

收益

  • $USD 0++ 是一种增值型国库券，USD 0++ 是 USD 0 的包装和锁定版本。

  • $USUAL 是 Usual 协议的治理和奖励代币。

  • 收益来源于质押：用户将其 USD 0 锁定在 USD 0 Liquid Bond (USD 0++) 中一段时间。收益可以从下面两种方式中选择一种：

    1. USUAL 收益： Liquid Bond 的持有人可以以 USUAL 代币的形式每日领取其收益（每个区块计算）。

    2. 基本利息保障：保证 USD 0++ 持有者获得至少相当于 USD 0 抵押品收益的收益（无风险收益）。为实现此目的，用户必须将其 USD 0++ 锁定一段指定时间（目前设计为周期为 6 个月）。此期间结束时，用户可以选择以 USUAL 代币的形式或 USD 0 的无风险收益领取基本利息保障。

        需要注意的是，无论 USD 0++ 是通过一级发行还是二级市场获得，持有者都自动有权领取 USUAL 代币。

交互

Pills 活动概览

  • 预启动阶段从 7 月 10 日 0 时（UTC）开始，活动持续时间： 4 个月

  • 空投数额：在代币生成事件（TGE）时铸造总 $USUAL 供应量的 7.5% 

  • 用户通过推荐计划和 TVL 贡献可获得 $USUAL 空投分配。类似 Ethena 的种植 pills 活动。

  • 线性分配：存入的越多，得到的就越多。

详细交互指南

1. 打开 Usual Money dApp：https://app.usual.money/#ZHWQS 或通过官网进入然后输入早期访问代码：ZHWQS

usual.money ：RWA去中心化稳定币

2. 存入 USDC、USDT、ETH 以铸造 USD 0 （以太坊主网）。点击链接铸造：https://app.usual.money/counter/deposit

3. 赚取药丸方式：

    1. 铸造：用户将 USD 0 锁定为 USD 0++ 时，每次可以赚取 5 粒药丸。

    2. 持有：持有 USD 0++ 的用户每天可以获得 3 粒药丸。

    3. USD 0/USDC 池：用户在 Curve 的 USD 0/USDC 池中提供流动性，每天可以获得 1 粒药丸。

    4. USD 0/USD 0++ 池：

    • 向该池贡献 USD 0 的用户每天可以获得 3 粒药丸。

    • 每铸造一枚 USD 0++，还可以获得 5 粒药丸，按比例分配给该池中的 USD 0 LP。

    • 注意：向该池提供 USD 0++ 不会产生任何药丸。

    同时，还有一个时间乘数，从 1 开始，每天增加 2% 。如果用户退出其位置则重置为 1 。

4. 添加流动性获得 pills： 选择 USD 0/USD 0++ 选项，输入 USD 0 数量。链接：https://app.usual.money/desk/liquidity

Pills 活动第二期

  • 8 月 4 日，Usual 发布了第二期活动，通过在 Galxe 上完成任务来获得 Pills。活动链接：https://app.galxe.com/quest/usual/GCsgvtvTNA

Letture associate

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit25 min fa

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit25 min fa

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbit31 min fa

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbit31 min fa

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit32 min fa

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit32 min fa

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2 h fa

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2 h fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit2 h fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit2 h fa

Trading

Spot
Futures
活动图片