SarahTnCn

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Jungs, lasst uns ein paar ehrliche Worte wechseln.
Hast du beim Surfen im DeFi schon einmal diese Momente der „Persönlichkeitsspaltung“ erlebt? Auf der einen Seite wirfst du dein Vermögen bedenkenlos in einen „200% annualisierten“ Mining-Pool, ohne ein Auge zuzukneifen; auf der anderen Seite musst du sogar vor deiner eigenen Mutter geheim halten, welche Coins du gekauft hast, aus Angst, als Spieler abgestempelt zu werden. Ganz zu schweigen davon, dass du bei jeder Autorisierung des Vertrags diese unverständlichen Codezeilen ansiehst und innerlich murmelst: „Solange es nicht auf null fällt, ist alles in Ordnung.“
Was schützen wir eigentlich? Und wovor haben wir Angst?
Das aktuelle Web3 ist ziemlich magisch. Vieles von dem, was als „Datenschutz“ bezeichnet wird, bedeutet in Wirklichkeit, dass deine Daten in einer Black Box eingeschlossen werden, während man dir sagt: „Vertraue mir, es ist dort sicher.“ Aber für uns alte Hasen, die unzählige Male durch „Vertragslücken“, „Wissenschaftlerangriffe“ und „Rug Pulls“ gelehrt wurden, ist „blinder Glauben“ beängstigender als „Transparenz“.

看过《三体》的兄弟们应该都记得那个让人后背发凉的设定：三体人用一颗“智子”锁死了地球的基础科学，让人类再也无法突破物理学的边界。质子二维展开、智能芯片植入、光速通信——一颗小小的智子，就能监控整个人类文明，让所有实验数据变得不可信。
今天我想跟你聊的Zerobase，让我想起了这个设定。
但不是因为它“锁死”了谁，而是因为它在做一件类似的事：用一套叫Proof Mesh的证明网络，像智子一样覆盖Web3世界的每一个计算盲区，让所有链下执行变得可验证、可信任、不可作弊。
这不是科幻，这是正在发生的技术革命。
一、Web3的“黑暗森林”：我们到底在信什么？
先问自己一个问题：当你在一个DeFi协议里质押USDT，年化20%的收益从哪来？
绝大多数人答不上来。你看到的只是一个数字在涨，至于背后是套利、是借贷、还是单纯的项目方在印钞给你——你完全不知道。
这就是Web3现在的尴尬：我们整天喊着“去信任化”，实际上却把信任转移给了更隐蔽的对象。你信合约没漏洞，信科学家不夹你，信项目方不rug，信预言机不报错。每一个环节都是一个“盲区”，你看不见，摸不着，只能赌。
这像不像三体人到来之前的人类？以为自己很强大，其实所有的实验都被干扰，所有的结果都不再可信。
Zerobase要解决的，就是把这些“盲区”一个一个照亮。
二、Proof Mesh：一颗覆盖全网的“智子”
Zerobase的核心架构里，有一个层叫Proof Mesh。
这个名字起得真好。Mesh是网格，Proof是证明——合起来就是一张“证明之网”。它不是单一的验证节点，不是某条特定的链，而是一个分布式的证明中间层，连接着链下的TEE执行环境和链上的验证逻辑。
你可以把它想象成三体人的智子：
智子可以潜入任何实验场地，实时监控、实时干扰；Proof Mesh可以接入任何计算任务，实时生成证明、实时上链验证。
智子让人类的所有实验结果变得“可被观测”；Proof Mesh让Web3的所有链下计算变得“可被验证”。
区别在于：智子是来搞破坏的，Proof Mesh是来建信任的。
在Zerobase的架构里，Proof Mesh负责一件事：统一格式化、标记和管理所有来自TEE的ZK证明。不管你的计算任务是在哪个节点跑的，用的是哪种ZK电路，最后生成的证明都要经过Proof Mesh这个“中枢神经系统”，被打包、被路由、被送到链上。
这意味着什么？
意味着你再也骗不了人了。以前项目方可以说“我们的策略很牛逼，收益率高是因为我们厉害”，现在不行了——每一个收益、每一次执行，都有一张ZK成绩单甩在你脸上，你可以验证真伪，但看不到细节。这就是“可验证的隐私”。
三、双重锁死：TEE是保险箱，ZK是摄像头
光有Proof Mesh还不够。真正的杀招，是Zerobase把TEE和ZK焊死在了一起。
这俩技术单拎出来都有问题：
· TEE（可信执行环境）依赖硬件，你得信英特尔或者AMD没留后门；
· ZK（零知识证明）验证成本高，纯ZK方案跑复杂计算慢得要命。
但Zerobase把它们组合起来，玩出了1+1>2的效果：
TEE层是“保险箱”。你的策略、你的数据、你的交易逻辑，全在硬件级别的安全环境里跑。节点运营者看不到，黑客攻不破，外部世界对这个“箱子”里发生的事一无所知。
ZK层是“摄像头”。箱子里的计算结束后，TEE会向外扔出一张“ZK成绩单”——零知识证明。这张成绩单能被任何人验证，证明“我刚才确实用你们的钱做了套利，赚了这么多”，但绝不透露具体怎么做的。
Proof Mesh就是那张“网”，把所有箱子和摄像头连接起来，让证明流动起来，让信任可被传递。
这叫“双重锁死”：TEE锁死执行过程的隐私，ZK锁死结果的可信度。中间没有任何灰色地带可以钻。
四、400毫秒：比光速还快的“智子反应”
三体人的智子最可怕的是什么？是反应速度——它能在人类实验的瞬间干扰结果，快到让科学家根本察觉不到。
Zerobase的速度也快得惊人。
据官方信息，ZEROBASE可以在400毫秒之内生成ZK证明。400毫秒是什么概念？就是你眨一下眼睛的时间，一个复杂的金融策略执行完毕，同时生成了一张可被全网验证的“成绩单”。
传统ZK方案的痛点就是慢——生成一个证明可能要几分钟甚至几小时，根本没法用在实时交易里。Zerobase用TEE加速ZK生成，把证明效率提升了10倍以上，测试网TPS能做到传统ZK-Rollup的17倍。
这意味着什么？
意味着你可以把Zerobase塞进任何需要实时验证的场景：交易、借贷、衍生品清算、跨链桥……再也不用因为等证明而卡顿，再也不用为了效率而牺牲安全。
这才是真正的“实时ZK”。不是实验室里的demo，是能跑在生产环境里的基础设施。
五、智子能做的，远不止监控
在《三体》里，智子不仅能锁死科学，还能当通信工具、还能变成智能机器人。Proof Mesh也一样——它不只是个“证明路由器”，它是整个Zerobase生态的神经中枢。
基于这个网络，Zerobase已经长出了一系列应用模块：
· zkLogin：用Google账号就能登Web3，私钥丢了也不怕，身份数据全程隐私保护。
· zkDarkPool：暗池交易，挂单细节只有成交那刻才公开，科学家想夹你？门都没有。
· zkVote：隐私治理，投票记录保密，但票数可验证。
· zkCEX：中心化交易所透明化方案，让CEX也能自证清白，不用再靠嘴喊“Merkle Tree”。
每一个模块，都是Proof Mesh这张“智子之网”上的一个节点。开发者可以像搭积木一样，把这些模块嵌进自己的DApp里。
更狠的是，Zerobase还能跟其他ZK生态打通。它已经接入了Aligned Layer——一个基于EigenLayer的ZK验证层。这意味着Zerobase生成的证明，可以低成本、高效率地在以太坊上验证，享受以太坊级别的安全性。
这不是闭门造车，这是开放互联。智子只有一个，但它的影响力覆盖整个宇宙。
六、真正的信任，不需要信任何人
说了这么多技术，最后聊点感性的。
我为什么对Zerobase这么上头？
因为在这个行业待久了，见过太多“信任崩塌”的瞬间：FTX暴雷、LUNA归零、一个又一个项目方跑路。每一次，都是因为“信任”这个词被滥用了——我们信了不该信的人，信了不该信的代码，信了不该信的承诺。
Zerobase在做的事，是把“信任”从人身上剥离出来，焊死在数学和硬件上。
你不需要信项目方的人品，你只需要验证他生成的ZK证明；
你不需要信节点运营者不作恶，你只需要知道TEE硬件保护了你的数据；
你不需要信任何人的嘴，你只需要看链上那张“成绩单”。
这就是“可验证的信任”。它比任何KOL喊单都硬，比任何大佬站台都稳，比任何营销活动都持久。
三体人用智子锁死了人类的科学，让人类永远无法超越他们；
Zerobase用Proof Mesh锁死了Web3的计算盲区，让信任不再是奢侈品，而是像空气一样无处不在的基础设施。
七、最后说点实在的
当然，任何项目都有风险。
Zerobase的Proving Node需要质押至少100万U的稳定币才能跑，这个门槛注定了节点数量不会太多——中心化风险需要警惕。TEE硬件本身也不是100%安全，理论上存在侧信道攻击的可能。代币经济学里43.75%给了节点激励，通胀压力也要看实际的释放节奏。
但这些都不妨碍它成为ZK赛道里最值得关注的项目之一。
拿到以太坊基金会资助、Binance Labs领投、Upbit上市首日交易量破1500亿韩元、TVL突破5亿美金——这些不是靠吹出来的，是真金白银堆出来的认可。
更让我触动的是昨天那件事：币安广场活动规则调整，Zerobase团队第一时间表态“事前不知情，争取让所有参与者瓜分奖池，如果不行就自掏腰包发实物礼物”。这种“听劝、有担当”的态度，和他们的技术理念一脉相承——说到做到，可验证，可追溯。
三体人告诉人类：你们是虫子。
Zerobase告诉Web3：你们可以不再需要信任虫子。
因为智子（Proof Mesh）已经来了，所有的计算盲区，都将被锁死。
#Zerobase $ZBT @ZEROBASE
{future}(ZBTUSDT)

@Fabric Foundation $ROBO #ROBO $ROBO：Fabric Foundation如何为机器人经济铺设信任轨道
当人工智能开始从屏幕走向物理世界，智能机器之间的互联协作成为关键挑战。Fabric Foundation 正是为了解决这一难题而生，它通过开源协议为机器人赋予链上身份和自主交易能力，而 $ROBO 则是这个去中心化机器人经济网络的“原生血液”。
由斯坦福大学Jan Liphardt教授与MIT背景的博远·陈联合创立的Fabric Protocol，旨在打破不同品牌机器人之间的“语言壁垒”。其核心产品 OM1通用操作系统 让机器人获得类似人类的“数字护照”，并能通过Fabric协调协议自主完成充电支付、算力采购等服务合约，无需人类干预。这标志着机器对机器（M2M）经济从概念走向现实。
$ROBO 正是这一网络的价值锚定物。其代币经济模型设计注重长期生态健康：1000亿枚固定总量，其中29.7%用于生态激励，团队和投资者份额均设有12个月锁仓期及36个月线性解锁，极大减缓早期抛压。更关键的是，协议收入将用于公开市场回购$ROBO，形成持续通缩机制，为代币价值提供内生支撑。
市场热度已迅速点燃。2月27日，$ROBO同步登陆Binance Alpha、Bybit、Gate及Bitget等主流平台，引发广泛关注。Bybit同步推出750万枚$ROBO的Splash奖池吸引用户参与。此外，空投申领窗口现已开放，早期贡献者可于3月13日前认领份额，有望进一步提升社区活跃度。
展望未来，$ROBO的价值取决于Fabric网络能否成为智能社会的底层“标准轨”。当人形机器人开始通过Fabric共享技能、协同劳动时，$ROBO便真正捕获了自动化经济的“价值税”。这不仅是加密市场的热点叙事，更是对“AI+机器人”时代产权结构的前瞻探索。#ROBO 正开启一个由代码与机器共同编织的繁荣新大陆。

HOW MIRA NETWORK IS BUILDING A TRUST LAYER FOR THE AI ERA?

‎There was a time when I believed that if a machine sounded confident, it had to be correct. The sentences were smooth. The explanations felt structured. The answers came instantly. It felt almost magical. But the more I observed, the more I realized something unsettling. Artificial intelligence does not always know when it is wrong. It can generate detailed explanations that look flawless while quietly inserting inaccuracies, bias, or fabricated details. And when those outputs begin influencing financial systems, healthcare decisions, automated contracts, and institutional operations, even a small error can ripple into something massive. This is the emotional space where Mira Network was born. Not from hype, not from speculation, but from the urgent realization that intelligence without verification is a fragile foundation for the future we are building.

‎Mira Network positions itself as a decentralized verification protocol designed to solve one of the most critical weaknesses in modern AI systems. The core idea is simple but transformative. Instead of blindly accepting AI outputs as reliable, the network converts them into structured, verifiable claims. These claims are distributed across a decentralized group of independent validators and models. Each participant evaluates the information, and their responses are aggregated through consensus mechanisms reinforced by economic incentives. This means that no single model, company, or centralized authority has the final word. Truth becomes a collaborative process rather than a unilateral declaration. And in a world increasingly driven by automation, that shift feels profound.

‎What truly distinguishes this framework is the way it aligns incentives with honesty. Validators within the network are required to stake value in order to participate. If they provide accurate verification, they are rewarded. If they act dishonestly or negligently, they face financial penalties. This transforms verification from a passive check into an economically enforced responsibility. It becomes rational to protect accuracy because your capital depends on it. In many ways, Mira is not just building infrastructure. It is engineering behavior. It is creating an ecosystem where truth has tangible weight and misinformation carries real cost.

‎The technical architecture reflects thoughtful design. AI outputs are broken down into atomic components that can be individually assessed. These components are evaluated across diverse verification nodes to reduce correlated bias. Results are aggregated and recorded with cryptographic assurances so that downstream applications can rely on the outcome. This layered structure allows applications, smart contracts, and autonomous agents to reference verified intelligence rather than raw machine output. It becomes possible to design financial systems that execute trades only after verification thresholds are met. It becomes possible to deploy AI agents that pause or escalate when consensus confidence drops. In this way, Mira does not compete with AI. It strengthens it by wrapping it in accountability.

‎Another powerful aspect of the network lies in its modular design. The world is not uniform, and verification requirements differ across industries. Healthcare demands extreme precision. Financial systems require auditability and compliance. Governance applications need transparency and traceability. Mira’s modular framework allows specialized verification layers to be integrated depending on the use case. New models can be introduced. Additional validators can be onboarded. Governance mechanisms can evolve. This flexibility ensures the network can adapt to technological progress rather than becoming obsolete in its own rigidity. It becomes living infrastructure instead of static code.

‎Institutional relevance is where the emotional weight deepens. Enterprises and regulatory environments do not operate on optimism. They require measurable guarantees. They need logs, transparency, and enforceable accountability. By combining decentralized consensus with economic staking and cryptographic proof, Mira creates a framework that institutions can analyze, audit, and potentially integrate into mission critical workflows. We are seeing a growing recognition that AI adoption at scale requires safeguards. Mira attempts to provide those safeguards in a decentralized manner, ensuring that verification itself does not become another centralized vulnerability.

‎The token economy within the ecosystem plays a structural role rather than a symbolic one. The token is used for staking, rewarding validators, participating in governance, and strengthening the economic backbone of the verification process. Campaigns distributing 250000 MIRA tokens through global leaderboard initiatives are structured to incentivize authentic engagement while enforcing strict rules against
manipulation and automated abuse. This approach demonstrates a commitment to sustainable growth rather than artificial expansion. It signals that the network values genuine participation over inflated metrics.

‎What makes this project resonate emotionally is not just the mechanics but the philosophy behind it. For years, blockchain has focused on decentralizing money and enabling programmable finance. Mira extends that logic into the realm of intelligence. It asks whether we can decentralize trust in AI the same way we decentralized trust in transactions. If blockchain allowed strangers to exchange value without centralized intermediaries, Mira explores whether machines can exchange information with verifiable integrity. This is not a minor upgrade. It represents a conceptual leap.

‎

‎Looking ahead, the implications are significant. Verified AI could unlock safer autonomous systems. It could enable smart contracts to depend on machine generated insights without inheriting unchecked risk. It could create a world where automated decisions are accompanied by measurable confidence scores grounded in decentralized consensus. We are witnessing the early formation of what could become a foundational layer for the next generation of digital infrastructure.

‎In the end, what Mira Network represents goes beyond technology. It represents a response to a growing discomfort many of us feel as automation accelerates. We want progress, but we also want protection. We want innovation, but we also demand accountability. Mira attempts to reconcile those desires by embedding verification directly into the AI lifecycle. If it succeeds, it will not simply enhance artificial intelligence. It will redefine how we trust it. And in doing so, it may mark the beginning of a new era where blockchain does not just secure value, but safeguards truth itself.

‎ @Mira - Trust Layer of AI     $MIRA     #Mira

@Fabric Foundation
Fabric Protocol makes more sense when you stop thinking about it as a crypto asset first and start treating it like an attempt to build an operating layer for robots that multiple parties can actually share. The project is basically saying: robots are going to be built by communities, companies, researchers, and independent operators at the same time, and we need a neutral way to coordinate who contributes what, who gets credit, who gets paid, and what rules constrain what the machines are allowed to do. That is the heart of it. Everything else only matters if that coordination works in the real world.
What I keep coming back to is that Fabric is trying to solve a kind of trust problem that robotics has avoided by staying closed. In the current model, a robot platform is usually one company, one stack, one set of policies, one set of logs, and one legal owner. Fabric is proposing the opposite direction: open participation, shared upgrades, and public accounting of what happened. It sounds clean on paper, but robotics is the place where clean architectures get messy fast. A robot does not just compute. It moves in environments that change, it reads sensors that drift, it makes mistakes that can damage property, and it creates liability the moment it interacts with people. So if Fabric wants to be taken seriously as an infrastructure layer, it has to show that it can handle that mess, not just describe a cleaner future.
The project’s own description leans heavily on verifiable computing and agent native infrastructure. The key promise inside those words is that the network can coordinate data, computation, and oversight through a public ledger, and that contributions can be measured in ways that do not rely on trust. That ambition is understandable. In a robot network, you do not want rewards going to whoever shouts the loudest or has the best connections. You want rewards tied to work that can be checked. The question is what checkable means when the “work” is physical.
Uploading a dataset is easy to verify. Providing compute is easy to verify. Recording that a robot accepted a task is easy to verify. But proving a robot actually completed a task correctly and safely is not easy in the same way. That is where these networks either become impressive, or they become a machine that pays people for generating receipts. This is not a minor detail. If the protocol rewards what is easiest to prove, it will slowly drift toward activity that looks productive without necessarily improving robotics in a meaningful way.
To understand what Fabric needs to get right, I think it helps to break the project into three concrete layers.
The first layer is identity. Fabric’s whole idea depends on robots and agents being addressable entities in the network. A wallet for a robot is trivial. The hard part is binding that wallet to a specific device, with a known operator, a known software stack, and some level of tamper resistance.
Without strong identity and attestation, you get the worst of both worlds: the network looks open, but it cannot reliably distinguish a real machine from an impersonation, and it cannot reliably tie behavior to accountability. If Fabric’s identity layer becomes strong, then the network can start to matter for governance, compliance evidence, and audit trails. If identity stays weak, then the ledger becomes a record of claims rather than a record of reality.
The second layer is verification. Fabric is positioning itself around verifiable work. In robotics, verification has to go beyond “did a node submit something” and toward “did the thing actually happen in the physical world, in the way the task required.”
That usually forces uncomfortable design choices. Either you build heavy mechanisms like hardware backed attestations, redundant sensing, external validators, and dispute processes, or you accept that some tasks cannot be fully verified and you rely on trusted committees or reputational systems. Both directions can work in limited domains, but neither is free.
Trusted committees undermine the open network story. Reputation systems are notoriously gameable unless they are designed with strong penalties and anti collusion measures. The more general purpose the robot tasks are, the harder verification becomes.
The third layer is governance and oversight. Fabric talks about coordinating regulation via a public ledger. I interpret that less as “the ledger enforces laws” and more as “the ledger can hold the evidence trails that regulators, operators, and insurers might care about.” That is a reasonable use of a ledger. But it only becomes meaningful if the evidence is trustworthy, and if there is a clear incident response path when things go wrong.
Robotics does not give you the luxury of slow governance. If a task type turns out to be unsafe, if a model is misbehaving, if a particular operator is abusing the system, someone has to react quickly.
If governance is too centralized, that reaction can be fast but feels arbitrary. If governance is too decentralized, the reaction can be principled but too slow to matter. Fabric’s structure, being backed by a foundation, hints that the project expects an early phase where a smaller group can steer and stabilize the network. That can be the right call for a safety sensitive system, but it also means the project should be judged on transparency and limits, not on slogans about openness.
ROBO sits inside this as the coordination asset: fees, governance, and incentives. Token mechanics are not automatically a red flag here because without incentives you do not get participation from data providers, compute providers, robot operators, and validators. The issue is whether incentives point people toward the right behavior.
If ROBO rewards can be farmed by simulating tasks or producing low value verifications, then the network will attract the wrong kind of participation. If ROBO rewards require costly proofs and meaningful accountability, then participation will be slower, but the signal will be stronger. This is why I keep circling back to verification. In a robot network, verification design is not a feature. It is the entire product.
One more thing that is easy to miss: the project’s ambition is “general purpose robots.” That phrase is attractive, but it is also where most systems become vague. General purpose in robotics is not one problem. It is a stack of domain specific problems stitched together, each with different safety constraints and verification difficulty.
A careful approach would likely start narrow: tasks where the environment is controlled, success criteria are measurable, and proofs are realistic. If Fabric tries to jump too quickly into broad claims, the network will either become shallow or it will end up relying on centralized gatekeeping to prevent abuse. Neither outcome is fatal, but both would reshape what Fabric actually is.
So here is the honest, human read of Fabric as a project. There is a real thesis underneath it: robotics is moving toward multi party development, and the missing layer is coordination with accountability.
A public ledger plus verifiable computation can help with accountability and incentives, but only if the system can tie identities to real machines and can verify outcomes in a way that resists gaming. If Fabric proves that it can do that, even in narrow domains, it becomes interesting infrastructure rather than just an idea. If it cannot, the network risks becoming a rewards system that tracks activity without reliably improving robotics.
Right now, the most important evidence is not token distribution mechanics or exchange listings. It is whether Fabric can show real integrations, real robot operators, and real task flows where verification is strict enough that cheating is expensive. If those pieces appear, you will see it in the form of technical interfaces, developer documentation that people actually use, and case studies where the network’s rules held up under pressure. If those pieces do not appear, the story stays conceptual, and the ledger becomes more of a narrative anchor than an operational backbone.
That is the line I would use to evaluate the project: does Fabric build a verification and accountability stack that can survive contact with real robots, real operators, and real incentives, or does it mostly measure what is convenient because the physical world is too hard to certify.
#ROBO @Fabric Foundation $ROBO

👍 Zuerst liken und dann anschauen, um nichts zu verpassen
⚠️ Persönliches Forschungstagebuch, stellt keine Anlageberatung dar
Heute @Fabric Foundation gerade TGE, konnte es kaum erwarten, ein wenig zu forschen.
Zunächst dachte ich, es sei nur ein AI + Crypto Konzeptprojekt 🤔, aber nach eingehender Forschung stellte ich fest, dass die Richtung und das Marktverständnis völlig unterschiedlich sind – was Fabric wirklich interessiert, ist das Problem der vertrauenswürdigen Ausführung von AI auf der Chain.
【1. Was macht die Fabric Foundation eigentlich】
Die Fabric Foundation ist kein AI-Anwendungsprojekt, sondern baut ein vertrauenswürdiges Ausführungsnetzwerk für AI und Robotik auf der Blockchain 🌐

在人工智能从数字世界迈向物理世界的转折点上，一个旨在让机器人成为独立经济主体的开放网络正在成形。Fabric Foundation作为独立的非营利组织，致力于构建智能机器的治理、经济和协调基础设施。它由斯坦福大学教授Jan Liphardt创立的智能机器基础设施公司OpenMind发起，后者为其核心合作伙伴，提供OM1操作系统与FABRIC协作协议。
从孤立工具到经济主体的技术突破
Fabric生态系统的核心创新在于解决了机器人的身份、支付与互操作性问题。通过OM1操作系统（被誉为机器人学的Android）和FABRIC协议，不同制造商的人形机器人、四足机器人能够共享智能、执行链上交易。近期，OpenMind与Circle达成战略合作，整合USDC稳定币与x402协议模块，推出了全球首个专为自治智能体打造的支付基础设施。这意味着机器人可以为充电、云升级等服务自主支付，拥有了真正的“经济大脑”。
$ROBO代币的经济学设计
作为生态的原生效用和治理资产，$ROBO总供应量为100亿枚，其经济模型围绕长期稳定设计：29.7%用于生态系统激励（如机器人工作证明PoRW），24.3%分配给投资者（1年锁仓+36个月线性解锁），18.0%作为基金会储备，5.0%用于社区空投。在网络中，无论是身份验证、任务结算，还是开发者访问机器劳动池，均需使用或质押$ROBO。
最新动态与市场表现
Fabric Foundation已宣布ROBO申领门户于2月27日17:00（北京时间）正式开放，符合条件的用户可在3月13日前完成申领。在二级市场方面，ROBO近期表现活跃，据CoinMarketCap数据，截至2月28日，ROBO 24小时内上涨31.67%，触及0.05美元高点，交易量达1.6亿美元。
从Robot Birthplace（机器人诞生）到Acceleration of Adoption（加速采用），Fabric Foundation正在构建“出生、生产、运营与进化”的全链条闭环。未来几个月，自动充电站等真实世界部署案例值得持续关注。@Fabric Foundation
$ROBO #Robo