链抽象:多链时代下的三棱镜

Odaily星球日报Publicado a 2024-03-27Actualizado a 2024-03-27

Resumen

模块化L1 Particle Network提供了实现链抽象的SDK平台。

作为 Crypto/Web3 生态系统的关键入口,钱包一直是创业团队竞争的焦点。除了以支持新公链为首的钱包产品外,还有很多团队希望能彻底改善 MetaMask 的体验(比如 Rabby)。当钱包团队还在关注账户抽象时,更高维度的链抽象(Chain Abstraction)叙事逐渐引起社区的关注。

NEAR 联合创始人 Illia Polosukhin 前不久撰文探讨链抽象的概念,并表明 NEAR 在实现链抽象方面的愿景。此外上个月上线主网的 ZetaChain、模块化 L1 Particle Network、全链账本 Cycle Network 等项目都在为实现链抽象而探索。

链抽象为什么重要?

思考一个问题,为什么 Web3 的产品用户体验感和用户数量远低于 Web2?其中一个重要原因是现在很多项目的用户需要了解各种原理和概念,而 Web3 发展速度之快大家也有目共睹,新叙事新概念层出不穷,即使 Web3 老玩家也会有「热点变化太快学习起来太麻烦」的感觉。

举个简单的例子,跨链桥协议首先需要用户理解之间资产不能互通,所以需要这样子的桥接协议来实现资产在不同链之间的转移,其次得明白如果想将资产从以太坊转移到 Solana,需要需要支付 ETH 作为 Gas 费,而不是 SOL。我只是想用以太坊上的 100 USDT 去买 Solana 生态里的 NFT 怎么这么麻烦?

相比之下,Web2 的产品几乎都是包装好的,用户无需知道原理,也不知道背后谁和谁的合作关系。按照 Web2 产品体验的逻辑,我想买 Solana 生态里的 NFT,只要在这个 NFT 项目界面登入我的钱包,拿钱包里 100 USDT 直接买,最后我只要知道钱包里还剩多少钱,NFT 到账没就可以了。

链抽象将不同区块链之间的差异和复杂性抽象为一种统一的接口,使得用户和开发者可以在不同的链上无缝地进行交互和操作,最终目标就是让用户感知不到链的存在,同时又享受区块链去中心化带来的好处,与意图相结合,用户体验会更加丝滑。想象一下,用户只需拥有足够的资产,不管什么代币不管在什么链上,想怎么交易都行,并且可以设置交易的时间点、条件,同时还能确保交易是安全的、隐私的、完全由自己掌控的。

链抽象:多链时代下的三棱镜

链抽象的逐步实现

在当下多链环境中,不仅用户体验变得复杂,而且流动性分散,这些都阻碍了区块链技术的大规模采用。整合流动性的有效方式是建立一个独立的平台,提供可交互操作的统一接口,即实现链抽象的平台。

Particle Network 就是这样一个提供实现链抽象 SDK 的 L1,它通过抽象出账户、Gas 和流动性,打造了一个 dapp 背后用户无感知的开放网络接入层:

  • Universal Accounts(通用账户): 用户可以在不同的链上使用相同的账户进行交易和操作。母合约控制着 50 多条链上的 AA 账户,将账户地址打通,使得打包交易更快,跨链更快(5 s 内)。此外这也意味着释放更多的应用场景,比如 omnichain 的稳定币。

  • Universal Gas Token(通用 Gas 代币): 引入了通用 Gas 代币(SPARTI),用户可以使用该代币在不同的链上支付交易费用。不必持有每个链上的特定 Gas 代币,降低了跨链交易的成本和复杂性。并且 Particle Network 整合了 EigenLayer,通过将 SPARTI 代币和以太坊实现双重质押,从而增强网络的安全性。

  • Universal Liquidity(通用流动性): 通过实现流动性抽象,将不同链上的流动性聚合起来。比如用 USDT 在比特币生态里一建购买 BTC。Particle Network 充当流动性聚合协议,支持跨链原子交易执行。

Particle Network 的底层架构采用了 Cosmos SDK,并通过 CometBFT 共识引擎确保网络的安全性。这种模块化的底层架构使得 Particle Network 可以灵活地扩展和定制,满足不同应用场景的需求。此外, Particle Network 采用聚合式 DA,DA 不依赖于某一方(比如 Celestia 或者 EigenDA),通过在多个地方存储数据的副本来增强数据可用性。

链抽象为应用爆发做好准备

多链的时代也意味着多层套娃(rollup)的时代,在这样的背景下,链抽象就像三棱镜将多束光聚合成一束一样,将多个链统一起来,解决准入门槛高、教育用户困难等问题,避免了用户需要处理多个账户、网络切换、手续费等影响体验的操作。随着越来越多的项目和技术团队加入到链抽象的构建中,我们期待链抽象成为推动 Web3 生态系统向前发展的关键驱动力,为 Web3 带来更加统一、便捷和安全的用户体验,促进主流采用的进程。

Lecturas Relacionadas

A Chip Company Releases AIDC Energy Storage Certification Standards. Why NVIDIA? Computing Power Reshapes Power Supply Logic. Who's in the Lead and Who's Left Out?

NVIDIA has released a "Battery Energy Storage System Self-Certification Guide," setting strict technical standards for energy storage systems specifically for AI data centers (AIDC). The guide focuses solely on certifying the Power Conversion System (PCS), not the batteries, with 10 mandatory performance metrics and 12 validation tests requiring real-world and simulation comparisons. Key requirements include rapid dynamic response to AI workloads, high-frequency system telemetry, and detailed electromagnetic transient models. The move is driven by the extreme and fluctuating power demands of next-generation AI hardware. Modern AIDCs require energy storage systems to act as intelligent, controllable grid assets, not just passive backup, to manage instantaneous, massive power load shifts that traditional UPS systems cannot handle. This redefines the competitive landscape for energy storage providers, shifting focus from capacity and cost to advanced control capabilities and system integration. While the market potential is significant—with forecasts of hundreds of GWh in new demand by 2030—the certification creates a high barrier to entry. It requires proven PCS delivery volumes and credible plans for rapid capacity scaling, favoring established, well-resourced players. Early movers like Fluence (partnering with Siemens) and several Chinese companies have secured projects ahead of the standard, but new entrants must now navigate this rigorous, costly, and time-intensive certification process to compete in the AIDC energy storage market.

marsbitHace 16 min(s)

A Chip Company Releases AIDC Energy Storage Certification Standards. Why NVIDIA? Computing Power Reshapes Power Supply Logic. Who's in the Lead and Who's Left Out?

marsbitHace 16 min(s)

After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

**Missing the 20x Opportunity: A Simple 'Dumb' Approach to AI Investing** The AI boom, driving NVIDIA's revenue from $60B to $216B in two years, creates immense investment pressure. However, like the internet bubble of 2000, the largest AI opportunities likely lie ahead, perhaps after a correction. Instead of rushing in now or waiting paralyzed for a crash, the author proposes a third way: building a "knowledge warehouse" by systematically mapping the AI industry to be ready when opportunities arise. The core of the strategy is understanding AI's four-layer value chain: 1. **Compute Infrastructure (The "Engine"):** This foundational layer, where all money eventually flows, includes: a) **Chip Design:** NVIDIA's dominance via its CUDA ecosystem, b) **Chip Manufacturing/Packaging/Memory:** TSMC's near-monopoly in advanced manufacturing and SK Hynix's lead in High Bandwidth Memory (HBM), c) **Optical Interconnects:** Essential for large-scale AI clusters (e.g., Lumentum, Coherent), d) **Cooling & Power:** Critical for high-density AI data centers (e.g., Vertiv), e) **Servers/Data Centers & Cloud Platforms:** The physical and virtual wholesale providers. 2. **Models & Tools (The "OS"):** The competitive layer of foundation models (OpenAI, Anthropic, Google, Meta, xAI), now generating real revenue. A key shift is the center of gravity moving from **Training** models to **Inference** (running models), which demands different chip characteristics and could challenge NVIDIA's monopoly. 3. **Middleware & Platform ("The Glue"):** Connects models and applications (e.g., Scale AI, Hugging Face). This layer could explode if applications take off. 4. **Vertical Applications ("The Cash Register"):** Where AI meets end-users (e.g., enterprise AI, coding tools, medical AI, robotics). A critical cross-cutting constraint is **Energy**, as AI's massive power consumption drives investment in nuclear and other energy infrastructure. The author identifies four key questions for further research: 1) How will the shift from Training to Inference reshape the competitive landscape? 2) With tech giants spending over $600B on capex, where is the ROI from AI applications? 3) What are the under-the-radar opportunities in the "second" and "third" circles of the value chain (e.g., cooling, specialty foundries)? 4) How will geopolitics (e.g., U.S.-China chip restrictions) bifurcate the supply chain? The conclusion is that missed opportunities stem from insufficient research, not slow timing. By methodically studying each layer—its business models, competition, and valuations—investors can build the "killer intuition" needed to act decisively when the market presents its chance.

marsbitHace 37 min(s)

After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

marsbitHace 37 min(s)

Trading

Spot
Futuros
活动图片