后坎昆升级时代,Rollup 们过的如何?

深潮Publicado em 2024-07-23Última atualização em 2024-07-23

坎昆升级极大削减了 Dapp Rollup 的运行成本,促进更多的 Dapp 选择采用 Rollup 范式重构。

撰文:NingNing

坎昆升级的核心升级 EIP4844,将 L2 batch 到以太坊主网的状态数据存储到新增 Blob 空间。

我们知道,Rollup 的设计架构,实际上是在转售以太坊主网区块空间给开发者和消费者。

Rollup 的核心商业模式有两种:

  • 构建一个真正繁荣的 Rollup 生态,然后从开发者和消费者的交互之中赚钱 L1<>L2 Gas 差价和序列器 MEV 收益。采用这种商业模式的有:Arbitrum、Optimism(via 超级链生态)、Base 等

  • 利用空投预期不断搞奥德赛 PUA 用户,然后从开发者和消费者的交互之中赚钱 L1<>L2 Gas 差价和序列器 MEV 收益。采用这种商业模式的有:ZkSync、Scroll、Linea 等。

坎昆升级极致压缩了 L2→L1 的数据可用性费,使得部署坎昆升级的 L2 平均 Gas 费下降了 1 个数量级。

以太坊,试图利用坎昆升级加强 L1 对目前 Rollup Centric 的以太坊生态的主导权,通过降本增效促进 Rollup 数量爆发式增长。

但却在有意无意之间,顺手摧毁了采用第二种核心商业模式的 Rollup 的经济基础。

首当其冲的便是 ZkSync。坎昆升级之后,ZkSync 第一时间支持了这个新特性,但效果糟糕。Gas 费几何级降低,并没有带来预期中的用户增长和生态繁荣,反而导致 Rollup L1-L2 Gas 差价收入的腰斩。

由于 ZkSync 一直以来生态发育不良,MEV 收入增长无法弥补 L1-L2 Gas 差价收入的锐减。在无可奈何之中,ZkSync 只好选择 6 月反弹的时间窗口空投,以在二级市场抛售代币的形式实现部分退出。

而它的友商 Scroll 和 Linea,则抗住社区和市场压力,一尽可能拖延部署坎昆升级,继续维持 L2 高 gas 费赚差价;二守住不空投不发币的底线,继续 PUA 用户交互。

从链上数据来看,2 月之后,Scroll 和 Linea 的 Rollup 收入大大超越 ZkSync。

我想,在下次以太坊主网 Pectra 升级改变游戏规则之前,Scroll 和 Linea 的团队不会有任何空投发币的想法,「电子乞丐」们做好心理准备和预期管理。

而采用第一种商业模式的 Rollup 则遇到了不同挑战,最主要是 Solana MeMe 热潮对以太坊生态的资金和用户的虹吸效应。

好在 Base 链的 SocialFi 和 MeMe 板块异军突起,成为以太坊生态抵抗 Solana 野蛮人冲击的盾牌,Rollup 收入也水涨船高,连续 4 个月份维持第一。

由于 Base 采用 OP Stack 构建,属于 Optimism 的超级链成员,Optimism 的 Rollup 收入也吃到 Base 生繁荣态正外部性的红利,在坎昆升级之后实现增长。

而曾经龙头老大 Arbitrum,由于过度押宝于 Web3 游戏,且在 Rollup Stack 战略行动迟缓,Rollup 收入在 4、5 月份有所下滑,但在 7 月迅速恢复。

最后,坎昆升级极大削减了 Dapp Rollup 的运行成本,促进更多的 Dapp 选择采用 Rollup 范式重构(如 Frax、Uniswap、AAVE 等)。

Leituras Relacionadas

Just now, DeepSeek V4 updates with DSpark, improving inference speed by 80%

DeepSeek has updated its DeepSeek V4 model with the DSpark speculative decoding framework, achieving a significant 60-85% speedup in generation for Flash models and 57-78% for Pro models while maintaining the same overall throughput. This engineering-focused update, rather than a core architectural change, introduces DSpark to address latency and throughput bottlenecks in high-concurrency production environments. DSpark combines high-throughput parallel generation with adaptive load-aware verification. Its key innovations include a semi-autoregressive generation architecture to model dependencies within token blocks and a hardware-aware confidence-scheduled verification system. This system uses a confidence head to predict token acceptance probabilities, allowing it to dynamically optimize verification length per request and allocate compute only to tokens with the highest expected payoff. The asynchronous scheduler is designed for real-world deployment, ensuring zero-overhead scheduling and continuous CUDA graph replay while preserving the target model's output distribution. In tests across mathematical reasoning, code generation, and daily dialogue, DSpark outperformed state-of-the-art models like Eagle3 and DFlash, increasing average acceptance length by 26.7%-30.9% and 16.3%-18.4% respectively on Qwen3 target models. DeepSeek also open-sourced DeepSpec, a full-stack codebase for training and evaluating speculative decoding draft models, providing a standardized toolkit that includes data preparation tools, model implementations, training code, and evaluation scripts.

marsbitHá 1h

Just now, DeepSeek V4 updates with DSpark, improving inference speed by 80%

marsbitHá 1h

BIT Research: The 2028 Halving Is Not the End, the Real Shake-Up of the Bitcoin Mining Industry Is Just Beginning

The Bitcoin mining industry is undergoing its most complex structural adjustment since inception. Despite Bitcoin's price holding near $61,000 and the network hash rate approaching a record 1 ZH/s, miner profitability is deteriorating. The industry is operating close to its breakeven point, with the 2028 halving expected to accelerate consolidation. The challenges extend beyond the halving's subsidy reduction; the industry's revenue model has yet to successfully transition towards a fee-driven structure. Increasingly, mining companies are evolving from simple Bitcoin producers into infrastructure and energy operators, including providers of AI/HPC computing power. Competition is shifting from pure hash rate expansion to business model upgrades. Economic pressure is evident. The theoretical daily mining revenue at current prices is around $78 million, yet the actual figure is only about $33 million—a 136% gap. Transaction fees remain low at roughly $220k daily, far below historical implied levels. With a current estimated industry-wide breakeven price near $65,000, mining alone is struggling to generate ideal profits. The 2028 halving is projected to push the fundamental production cost floor to approximately $93,289. This will likely accelerate a shift towards consolidation among larger, well-capitalized miners with diversified revenue streams. Competitive advantage will belong to institutionalized players with access to low-cost energy, AI/HPC hosting operations, and stronger balance sheets. In essence, Bitcoin mining is transitioning from a "mining business" to an "infrastructure business." Future profitability and resilience will depend less on block rewards and more on diversified income sources like energy management and computational infrastructure services. For investors, the key question is not the halving itself, but which miners can successfully navigate this business model transformation.

marsbitHá 2h

BIT Research: The 2028 Halving Is Not the End, the Real Shake-Up of the Bitcoin Mining Industry Is Just Beginning

marsbitHá 2h

This is How God Karpathy Uses Claude?

Andrej Karpathy, a prominent figure in AI, has reportedly joined Anthropic, leading to a noticeable decrease in his open-source contributions and social media activity. A document claiming to be his personal "CLAUDE.md" file—a set of instructions for the Claude AI to follow within a specific codebase—has been circulating online. While its authenticity is unverified, the content aligns closely with Karpathy's publicly shared principles on effective AI-assisted programming. The document outlines key rules for AI coding assistants, emphasizing the importance of reading existing code thoroughly before writing new code to maintain consistency. It advises against over-engineering, advocating for simple, surgical modifications that match the project's existing style. Other guidelines include clarifying assumptions upfront, writing meaningful tests, thoughtful debugging, and carefully considering dependencies. The core message is that these principles help prevent common AI coding failures, such as introducing unnecessary abstractions, style drift, or making invisible architectural decisions. The community has noted that even experts like Karpathy require detailed instructions to guide AI effectively, akin to managing a junior developer. A related GitHub repository, "andrej-karpathy-skills," which encapsulates these ideas, is reported to significantly reduce Claude's code error rate. Ultimately, the advice stresses that the best CLAUDE.md is tailored to one's own tech stack and coding practices.

marsbitHá 2h

This is How God Karpathy Uses Claude?

marsbitHá 2h

Trading

Spot
活动图片