获得 5500 万美元融资的链上风控公司 Chaos Labs:创始人系以色列特种兵出身,为 Layer zero 设计反女巫机制

链捕手2024-08-19 tarihinde yayınlandı2024-08-19 tarihinde güncellendi

作者:Frank,PANews

 

专注于链上风险管理工具的纽约加密初创公司Chaos Labs近日宣布完成了5500万美元的A轮融资。5500万美元的金额使Chaos Labs成为近期加密领域融资最多的初创公司,但这个颇受资本青睐的公司似乎并不被公众熟知,颇具神秘色彩。

Chaos Labs本轮融资由Haun Ventures领投,参与者包括F-Prime Capital、Slow Ventures、Spartan Capital等知名投资机构,以及Lightspeed Venture Partners、Galaxy Ventures和PayPal Ventures等大型投资者。Chaos Labs还得到了Solana 的 Anatoly Yakovenko 和 Phantom 的 Francesco Agosti 等天使投资者的支持。值得一提的是,Haun Ventures是Chaos Labs本轮融资的领投者,Haun Ventures的创始人Katie Haun曾是a16z的普通合伙人。Haun Ventures还曾投资了OpenSea、Zora、Aptos Labs 等项目。

2023年,Chaos Labs就曾获得由 PayPal Ventures 和 Galaxy Digital 领投的2000 万美元的种子轮资金。截至目前,该公司已累计获得7500万美元的融资金额。

创始人特种兵出身 自建蒙特卡洛模拟系统获数百万收入

Omer Goldberg的履历颇为丰富,他曾在2009年至2012年在以色列特种部队服役,在“Nahal”特种部队侦察部队担任作战士兵。2015年于以色列理工学院 (Technion) 信息系统工程 本科毕业,毕业后Omer Goldberg曾自主开发了一个暗网分析引擎,构建了完整栈网络应用程序和暗网网络爬虫,以发现、分析并分类可能在线上找到的危险文档。并协助巴黎警方收集有关国内威胁的实地情报。

随后Omer Goldberg曾任职Facebook、Instagram等公司的工程师,直至2021年7月主创业。

获得5500万美元融资的链上风控公司Chaos Labs:创始人系以色列特种兵出身,为Layer zero设计反女巫机制

获得A轮融资后,Omer Goldberg在推特上分享了Chaos Labs的创业历程。

十年前Omer Goldberg开始在宿舍挖比特币,由此接触加密领域。最早做的是山寨币投资组合,通过优化LP配置获得最大收益和最小风险,自建了一套可以大规模运行基于区块链代理的蒙特卡洛(Monte Carlo)模拟系统。

随后遇到MakerDAO需要模拟测试基础设施,Omer Goldberg意识到这个产品的商业需求。于是建立了一个运行基于代理的蒙特卡洛模拟的云开发者平台,提供SaaS服务,并在短期内获得了几百万美元的收入。

2021年,Omer Goldberg带领团队正式创立了Chaos Labs,最初雇佣了近 25 名员工,其中大多数是他在以色列军队时的朋友。

在接受《财富》的采访时Omer Goldberg表示:“Chaos Labs 的软件可以“100%”阻止 Eisenberg (Eisenberg攻击是指加密货币领域中的一种市场操纵行为,由Avraham Eisenberg在2022年10月对Mango Markets平台实施的攻击而得名)的攻击”。

PayPal Ventures 是Chaos Labs种子轮的投资人,PayPal Ventures 合伙人安曼·巴辛 (Amman Bhasin) 在一份声明中表示:“随着加密货币开发的加速和环境的复杂性不断增加,潜在的漏洞和黑客攻击的范围也在不断扩大。” “Chaos Labs 通过为协议、投资者和用户提供一套风险管理和优化工具来解决这个问题。”

Chaos Labs的最初主要业务是通过构建“混沌工程”对DeFi协议进行压力测试和模拟最坏的情况。随着加密领域安全环境的复杂性不断增加,Chaos Labs也拓展了自己的业务类型风险管理、风险预言机、分析工具、激励优化、女巫检测等。

服务Layer zero筛出80万女巫,服务DeFi领域头部项目

Chaos Labs今年被社区所关注,是在今年5月,负责了规模最大的反女巫机制Layer zero的女巫监测机制和筛选工作,共筛选出80多万个女巫地址。此外,Aave、GMX、Arbitrum、dYdX、Jupiter等DeFi领域的知名项目都是Chaos Labs客户。Jupiter的风险平台,dYdX的生态奖励机制,Aave的风险参数调整方案均出自Chaos Labs之手。

在早期未获得投资之前,Chaos Labs就曾获得Chainlink、Uniswap、dYdX、AAVE的多次资助,主要的资助原因是帮助这些DeFi项目进行隔离测试和具有建设意义的提案。包括在Aave社区发起“调整风险参数”提案;dYdX Chain的激励计划; Uniswap V3 风险管理解决方案;

当前,加密领域的安全事件已发展到不可忽视的地步,区块链安全审计公司Beosin Alert监测显示,2024年7月,各类安全事件损失金额较6月大幅增长。2024年7月发生较典型安全事件超20起,因黑客攻击、钓鱼诈骗和Rug Pull造成的总损失金额达2.86亿美元,较6月增长约56.3%。其中攻击事件约2.71亿美元,增长约92.2%;其中最大事件来自于印度交易所WazirX,损失约2.3亿美元,占到了当月攻击事件金额的85%。第二大攻击事件是LI.FI因合约漏洞损失约1160万美元。

获得5500万美元融资的链上风控公司Chaos Labs:创始人系以色列特种兵出身,为Layer zero设计反女巫机制

在此背景之下,Chaos Labs也迎来巨大的业务增加。据Chaos Labs官方表示:“在过去的一年里,Chaos Labs的客户数量增加了两倍,包括Aave、GMX和Jupiter在内的20多个协议依赖Chaos Labs的技术来保障、监控和扩展其产品。迄今为止,Chaos Labs的技术已确保了8600亿美元的累计交易量、250亿美元的贷款和3500万美元的奖励。”

就目前来看,随着DeFi行业的继续发展,以及各个协议越来越复杂的安全隐患出现,似乎Chaos Labs这种卖铲子的公司反而更具有商业潜力。

İlgili Okumalar

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.

marsbit1 saat önce

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

marsbit1 saat önce

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.

marsbit3 saat önce

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

marsbit3 saat önce

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.

marsbit3 saat önce

This is How God Karpathy Uses Claude?

marsbit3 saat önce

İşlemler

Spot
活动图片