Avail的模块化之路:致力成为以太坊rollup默认的DA层

Odaily星球日报Опубліковано о 2023-11-04Востаннє оновлено о 2023-11-04

Анотація

Avail在今年3月份宣布从Polygon独立出来,将专注于成为整个区块链生态系统的首选共识和数据可用性层。

Avail的模块化之路:致力成为以太坊rollup默认的DA层

Avail 正在为未来的区块链创建基础层,使开发者能够以可扩展性、灵活性和简易性来构建 rollups 和 appchains。Avail 对未来的设想是,为 Web3 开发者提供像 Web2 一样轻松构建的环境,并将传统基于中心化信任的系统转化为无需信任的系统。

Avail 在今年 3 月份宣布从 Polygon 独立出来,将专注于成为整个区块链生态系统的首选共识和数据可用性层。

截止到目前,已经发生很多新的进展,在今年 6 月份发布了第二个长期运行的测试网:Kate 测试网。一组新的外部验证人也已经加入了系统,参与者已经开始运行验证者和完整节点,以及轻客户端。

选择自己独特的发展路径需要很大的勇气。因此,当有其他项目和新的合作伙伴主动联系 Avail 并希望进行合作时,他们十分积极。他们与多家公司,如 Sovereign Labs、Dymension 和 Madara 等,正在进行或计划进行合作集成。当他们与这些可能的合作伙伴接触时,这些合作伙伴给予的正面反馈和对产品的匹配度评价让他们感到很受鼓舞。这些积极的反馈再次强化了他们的信心,相信他们作为一个主要集中于达成共识和提供数据可用性的区块链,拥有数据可用性采样功能,他们正在为未来的区块链技术打下坚实的基础。

走向模块化

在过去的一年中,用于执行扩展的 rollup 已经成为焦点。现在,Rollups 被公认为是在基础层之外进行链下计算的主要方式。

随着这些和其他技术的发展,我们正在说的区块链构造本质上正在变得模块化。Ethereum 的 rollups 本质上是在链下执行,并依赖基础层处理数据可用性和结算,最常见的是通过 rollup 智能合约。

Rollups 专注于扩展执行,但如何扩展数据可用性呢?Rollups 的数据扩展是所有基础层区块链发展的下一个前沿领域。Avail 正处于这个前沿领域,拥有无与伦比的数据可用性接口和创新的安全方法。

简化区块链集成并转化为信任

我们的全球社会在构建过程中依赖于一些被认为是可信赖的系统和组织。但这些系统和组织因为涉及人的参与,所以固有地存在人为因素。这种人为因素会限制系统和组织的扩展能力,并可能引入某种形式的偏见或不公平。例如,即使在技术发展的今天,很多公司和个人在转移资金时,仍然依赖于如第三方托管这样的受信任的机制。我们有能力并应该寻找更好的方法和系统来完成这些任务,而不是仅仅依赖现有的受信任的方法。

区块链技术,借助点对点交互,让我们能够从信任中移除人为元素,并依赖数学来减少偏见,并扩展信任,不仅仅是在一个国家内,而是在全世界范围内。

归根结底,区块链技术有力量转化所有权、信任和价值交换,我们深受这一信念的驱动,致力于使无需信任的计算基础设施对开发者开放,并推动可能性的边界。

Avail 希望构建能够让应用开发者将无需信任的组件集成到他们的应用中的基础设施。

“ 我们的目标是最终实现异步应用链。我们相信,就像 Web2 微服务扩展了互联网一样,带有异步组合性的应用链将扩展区块链和无需信任的计算。” —— Anurag Arjun

未来,终端应用将由跨多个应用链的组件组成,而不是在单一的大型链上,从而增加了可扩展性、灵活性和互操作性。Avail 有一个巨大的设计空间,用于实现异步组合性:构建能够(异步地)从多个应用链整合功能的应用的能力。

这可能的例子包括:作为服务的支付通道、作为服务的第三方托管、作为服务的分类账(例如,NFTs、可替代的代币账户)以及替代今天在日常应用中的受信任解决方案的更复杂的应用。

为了实现广泛使用,应用链中异步组合性的开发者体验需要等同于在常规应用中集成 APIs。当我们实现这一点时,我们会看到开发者在日常应用中如何嵌入无需信任的服务上展现出他们的创意。为了达到这个目标,Avail 将通过强大的数据可用性基础层赋权链开发者,使他们轻松地启动自己的链。Avail 正在努力简化区块链集成过程,推动创新,并重塑区块链的未来。

已在 Avail 上实现的功能

Avail 的重点是成为 Ethereum rollup 基础设施默认的 DA 层,当他们想要部署 Validium、Optimistic 链或 L3 链时,可以直接使用 Avail 提供的服务,并使他们部署一个独立的应用程序就像在 Ethereum 上部署智能合约一样简单。

Avail 是其他区块链的基础模块层,最明显的是 rollups。这些 rollups 可以基于有效性证明、乐观的(基于欺诈证明)或简单的悲观 rollups(所有状态转换都被重新执行)。Avail 为 rollups 提供大规模的共识和数据可用性服务。

Avail的模块化之路:致力成为以太坊rollup默认的DA层

 

Avail Light Clients 利用 KZG 多项式承诺、擦除编码和数据可用性采样(DAS)来允许验证而不下载区块数据(除了一个小的随机样本)。它们还可以下载单个应用程序/rollup 的所有交易,从而实现应用程序完整节点。https://blog.availproject.org/avail-light-clients/

这将可以在 Avail 上构建一个庞大、充满活力的生态系统:

1. 利用 Avail 区块空间的不同类型的 rollups,但可以根据独特需求进行调整。

  • 独立的 rollups:有效性证明/ZK rollups、乐观 rollups、悲观 rollups

  • 应用特定链:自定义执行和状态(考虑使用有效性证明或乐观构造的 Cosmos 样式应用链)

  • 具有复杂环境的通用链,如 EVM、SVM

2. 基础设施应用程序,如 Validiums 和 L3 排序器

  • Validiums

  • Optimistic 链

  • L3 链

3. 状态验证桥,是最强大的互操作方法,可以在 rollups 之间实现异步组合。

4. 共享安全性:应用程序不需要自己的验证人集。可以一键新链。

5. 轻客户端将被构建到应用程序客户端(和钱包)中,我们将拥有数百万这样的客户端。

展望未来 

Avail 的愿景包括通过稳健的共识和数据可用性层来改变区块链的格局。通过为模块化链提供原始块空间,Avail 使开发者能够以可扩展性、灵活性和简便性来构建 rollups 和 appchains。随着 Avail 的演进,它为可验证的计算、异步消息传递和一个广泛的生态系统打开了大门。

与众不同的道路需要勇气、决心和承诺。Avail 团队或许拥有达到他们想要的目标所需要的能力。

原文链接:https://blog.availproject.org/the-avail-vision-reshaping-the-blockchain-landscape/

编译:Modular 101 

阅读更多:

从单链到模块化:Avail 如何改变区块链应用开发

新手小白如何在 30 秒内安装运行 Celestia 轻节点?

速看!关于模块化区块链的 7 个误解及真相!

Пов'язані матеріали

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit1 год тому

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit1 год тому

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit1 год тому

Token Inefficient, Economy Tokenless

marsbit1 год тому

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit1 год тому

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit1 год тому

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbit1 год тому

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbit1 год тому

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbit1 год тому

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbit1 год тому

Торгівля

Спот
Ф'ючерси
活动图片