一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

Odaily星球日报Pubblicato 2023-11-12Pubblicato ultima volta 2023-11-12

Introduzione

CYBER解锁超10%,哪些大额解锁值得关注?

整理 | Odaily星球日报

编辑 | Loopy Lu

一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

CyberConnect

项目官网:https://cyberconnect.me/

官方推特:https://twitter.com/CyberConnectHQ

本次解锁数量: 126 万枚

本次解锁金额:(约) 856 万美元

CyberConnect 是一个多链去中心化社交图谱协议,目前已在以太坊主网、 Polygon、 Solana  和 BNB Chain  上部署。 该协议旨在为开发人员创造社交应用的机会,让用户能够拥有自己的数字身份 DID、内容、连接和代币化渠道的平台。

CYBER 是一个非常“年轻”的代币。8 月 15 日,该项目在币安   Launchpool 上线。而自上线以后,CYBER 就以被争议所包围,被冠以“妖”币的称呼。CYBER 一度在韩国 CEX 创下 36 美元的最高价格,较其他主流交易平台溢价高达 120% 。随后又以 12 小时的闪电般速度通过了“乌龙指”治理提案——解锁超流通盘数量的代币。其间一度更有头部做市商 DWF 的身影闪现。

目前来看,CYBER 仍是一个低流通的代币,目前总量 86% 的代币都在锁定之中,等待未来释放。

CYBER 代币经济学显示,CYBER 总量 1 亿枚,其中 9% 用于社区奖励, 34% 用于生态发展, 25.12% 分配给私募投资者, 15% 分配给团队和顾问, 10.88% 纳入社区财库。  CoinList   公售和币安 Launchpool 各分配 3% 代币。

一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

本次解锁代币数量约占流通量的 11.43% ,解锁数额巨大。这一解锁数量无遗已足以对流通的代币造成冲击。

但一个较为特殊的情况是,CYBER 此前为数不多的数次解锁,均与本次规模相当。CYBER 是一个解锁规模庞大的代币,此前每次解锁代币数量均超过了流通量的 10% 。投资者可从历史数据中得到参考。

本周,CYBER 超过 126 万枚代币的解锁,究竟会对行情何种影响呢?

ApeCoin

项目官网:https://apecoin.com/

官方推特:https://instagram.com/apecoindao

本次解锁数量: 1560 万枚

本次解锁金额:(约) 2293 万美元

BAYC 项目最近刚刚因为最近的“激光事故”风波而备受争议。

ApeCoin (APE)是 APE 生态系统的治理和效用代币,总供应量达 10 亿枚。该项目由社区控制和建立,ApeCoin DAO 为其治理组织,允许所有 APE 持有者进行治理决策投票。除 DAO 外,Yuga 还设立了 APE 基金会作为 DAO 的法律监管人。基金会董事会由 5 名成员组成。本代币初始发行时,BAYC 持有者被空投了大量 APE 代币。

一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

从解锁历史来看,APE 已经度过了历史上规模最大的三次代币解锁。目前解锁数量已趋于平稳。

尽管如此,若横向比较其他项目,超过流通量 4.2% 的解锁规模仍是一次值得重视的大额解锁。

目前,约 44% 的 APE 代币仍在锁定之中,等待未来解锁。

Oasis Network

项目官网:https://oasisprotocol.org/

官方推特:https://twitter.com/OasisProtocol

本次解锁数量: 1.96 亿枚

本次解锁金额:(约) 1487 万美元

Oasis 网络是一个可为用户提供隐私计算的 Layer 1 网络,兼容 EVM。用户有权决定自己隐私数据的使用,并可通过数据获取收益。Oasis 网络将共识和计算分离为共识层和计算层,使 Oasis 网络可以在保证主链扩展性和隐私性的前提下,支持计算密集性的用例,比如机器学习和深度学习,未来应用前景广泛。

作为底层 Layer 1 协议的证明,允许多个并行执行环境(ParaTimes)同时向网络提交事务,这提高了网络的可扩展性以及网络的适应性,可满足广泛的需求。

ROSE 代币经济学显示,本次解锁的代币来自于基金会、核心贡献者、社区、合作伙伴&储备。

一周代币解锁预告:APE、ROSE千万美金级解锁,5项目解锁值得关注

本次解锁的 ROSE 代币中,约 2500 万枚均为基金会所有的代币。合作伙伴&储备获得 1250 万枚代币归属。

而规模最为庞大的则为核心贡献者,贡献者可以获得 1.4 枚、约合 1062 万美元的 ROSE。

本次解锁的代币中,绝大多数代币均归属于核心贡献者所有。这一数字较为可观。若全部抛售或将带来较大的抛压。

Letture associate

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手8 min fa

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手8 min fa

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News1 h fa

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News1 h fa

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4 h fa

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4 h fa

Trading

Spot
Futures
活动图片