Smart Game Finance价格SMART

根据 Aave 治理论坛的一份提案，Aave 正在考虑集成 Chainlink 新推出的预言机，该预言机旨在将交易抢跑获得的利润重定向给去中心化金融（DeFi）协议的用户。Chainlink 于昨日发布了智能价值回收（Smart Value Recapture，SVR）服务，这是一项专门用于捕获最大可提取价值（MEV）利润并使 DeFi…

前段时间， RWA协议 $OM 在二级市场的闪亮表现，让其之前给社区的空投从“小鸡腿”变成了“大礼包”。 这让高融资且未发币的RWA协议成为市场VC、机构、羊毛党等等各色玩家趋之若鹜的对象。 其中的Rock Star之一，就是我们今天要介绍的Plume Network。24年5月，Plume Network 宣布完成由Haun Ventures 领投的高额种子轮融资。 注意一点：现在Plume的官方文档将自身定位为RWAfi L1，而不是一些中文融资信息里的模块化L2. 过去两年，我们见证了RWA的赛道快速迭代 - 从最初简单的实物资产映射到美债代币化，再到私人借贷、商品代币化、证券代币化等。目前最为成功的两个细分领域是美债代币化（广泛意义上包括USDT、USDC等美元稳定币）和私人信贷。 而Plume在尝试利用AppChain的范式探索另一条道路RWAfi。RWAfi与传统的RWA协议不同之处在于： - Composability优先：所有RWA资产都可以无缝接入DeFi生态； - 多层流动性：除了资产交易外，还包括收益权、使用权等衍生品的流动性； - 无需许可：任何人都可以参与资产的发行和交易，打破传统金融的准入门槛。 因此Plume的设计架构与其他RWA协议也大有不同。它的堆栈由ARC资产Tokenization引擎、Smart Wallet资产流动性层、Nexus数据高速公路（系统内置预言机）和RWA L1共识层组成。 在L1架构设计中，Plume借鉴行业最新潮流，大胆进行创新，引入RWA资产作为主网节点的Staking资产，保障主网安全性。这让系统内的RWA资产从单纯的投资标的变成复合资产，既能捕获底层资产的收益，又能获得Staking奖励，再通过LST协议解锁其流动性，在生态DeFi协议中赚取Yield收益或者LP收入。 这种设计正在重塑RWA的价值获取模式。在Plume生态中，RWA不再是静态的价值储存，而是可以产生复杂金融活动的基础资产。 另一个让Plume与众不同的架构设计，是Smart Wallet资产流动性层，这样Plume公链地址原生支持自动复利、一键Loop等高级DeFi活动，这可是以太坊Pectra升级之后需要用户手动签名操作才能有的功能。 从测试网数据来看，市场对这种新范式表现出极大兴趣： - 18M独立地址，3.75M+活跃用户 - 270M笔交易 - $4B+承诺资产 - 30分钟内$30M预充值 更重要的是，机构层面的参与度很高。从Neobank到传统金融机构，都在积极拥抱这种新的RWA范式。这印证了市场对RWAfi这个方向的认可。 中长期来看，随着美国加密监管框架将日益明细和确定，传统金融加速拥抱区块链，像Plume这样能提供完整RWAfi解决方案的项目将获得更大发展空间。其去中心化、可组合、流动性优化的特性，也将让其在加密友好的新系统环境中如鱼得水。 以上。 #RWAfi

AI agents on blockchain could redefine trustless automation. Imagine DAOs that use AI for real-time decision-making or agents trading assets autonomously on DeFi platforms. Are we ready for a future where smart contracts get even smarter? 🤖💡 #AI #Web3 #CryptoInnovation

As AI demand surges and GPUs become the bottleneck, decentralized compute offers a new way forward. Could crypto provide the infrastructure for scalable, permissionless AI? Just finished reading researcher Shlok Khemani's incredible deep dive into decentralized compute capabilities in the AI space. Let’s explore the current state of decentralized compute and its future potential. 🧵 1. The GPU Bottleneck - Training GPT-4 required 25,000 A100 GPUs and cost $60M. - The next generation of models will demand $100B+ and over 1M GPUs by 2026. Current challenges in the GPU market: - Supply shortages - Months-long wait times - Price opacity As AI scales, traditional infrastructure is strained. This creates an opening for decentralized solutions. 2. Why Decentralized Compute? The compute market today is fragmented and inefficient as Idle GPU capacity in data centers and individual devices remains underutilized. Decentralized compute platforms aim to aggregate global supply, offering: Lower costs. Geographic diversity to reduce latency. Permissionless access for developers worldwide. Crypto projects leverage blockchain incentives to align supply and demand, creating robust network effects. 3. Which AI Workloads Can It Support? Decentralized networks cannot support ultra-large models like GPT-4, which require tens of thousands of GPUs and dedicated infrastructure. However, smaller models are viable. Projects like Nous Research’s DisTrO and Prime Intellect’s OpenDiLoCo have demonstrated training models with up to 1B+ parameters across global GPU clusters. With globally distributed GPUs, decentralized networks are well-suited for inference tasks that require low latency and high redundancy. 4. How Does Crypto Add Value? Token Incentives: - GPU providers and users are rewarded in tokens, encouraging participation and liquidity. - Dynamic reward structures address geographic and hardware imbalances (e.g., higher rewards for scarce GPUs or underserved regions). Trust and Verifiability: - Smart contracts enforce transparent agreements, preventing price hikes or contract changes. - Cryptographic proofs ensure developers receive the compute resources they pay for. Censorship Resistance: - Decentralized networks reduce the risk of service suspension due to policy or external pressures, creating a more resilient infrastructure. 5. The Road Ahead While decentralized compute cannot yet challenge Big Tech’s dominance in large-scale training, its value in fine-tuning, inference, and smaller-scale training is clear. As AI adoption accelerates, decentralized compute may evolve into a critical infrastructure for ensuring the technology’s openness and accessibility.

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