Get live statistics and analysis of Jason傑森 🇭🇰|🛠️MemeMax⚡️ 's profile on X / Twitter Archetype analysis The Analyst Jason傑森 🇭🇰|🛠️MemeMax⚡️ is a software engineer and active AI plus blockchain enthusiast who deep-dives into complex tech innovations and decentralization narratives. Known for his meticulous breakdowns and thought-provoking discussions, he thrives on dissecting the evolving landscape of Web3 and crypto technology for his engaged community. His passion extends beyond pure tech, emphasizing trust, reputation, and data-driven decision making in the decentralized world.
🔥 Roast Jason’s tweet count is so high, he’s probably single-handedly keeping Twitter’s servers from ever taking a break. If tweeting was an Olympic sport, he’d have more gold than Michael Phelps, but hey, maybe remember to blink once in a while!
⚡️ Nice achievement Jason has successfully positioned himself as a trusted voice in the AI + blockchain niche, influencing community conversations about the future of decentralized trust systems and reputation tokens.
🌟 Life's purpose Jason's life purpose centers on illuminating the intricate intersections of AI and blockchain, advocating for transparent trust mechanisms and reputation systems that empower communities in the Web3 ecosystem. He aims to foster a knowledge-rich environment where thoughtful analysis drives smarter, more responsible adoption of emerging technologies.
💬 Values and Beliefs He firmly believes that reputation and trust are the new currencies in decentralized ecosystems, and that intelligent, data-backed insights are vital to sustainable growth in crypto markets. Jason values community involvement, fairness in incentive structures, and the continuous evolution of blockchain beyond mere speculation to meaningful real-world impact.
💪 Strength Analytical rigor and deep technical knowledge allow him to translate complex subjects into clear, actionable ideas. His engaging storytelling paired with strong community interaction fosters vibrant discussions and lasting relationships.
🫣 Weakness At times, his intense focus on data and deep dives might overwhelm casual followers or social media users looking for lighter content. Also, juggling a large following and high tweet volume risks content saturation or message dilution.
⚡️ Growth audience tips To grow his audience on X (formerly Twitter), Jason should leverage threaded tweets to create digestible, step-by-step deep dives that invite interaction. Incorporating more visual summaries (charts, infographics) and occasional lighthearted memes related to tech culture could help balance intensity and widen appeal. Engaging more with emerging creators and calling out collaborators can also extend reach organically.
💁 Bonus Fun fact: Jason has tweeted over 185,000 times, showcasing an extraordinary level of engagement and dedication to sharing insights in AI and blockchain space.
Top tweets of Jason傑森 🇭🇰|🛠️MemeMax⚡️Most engaged tweets of Jason傑森 🇭🇰|🛠️MemeMax⚡️People with Analyst archetype Explore Related Archetypes If you enjoy the analyst profiles, you might also like these personality types:
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不只是挂机赚钱工具,@wardenprotocol 也不只是账户抽象\n\n他们联手,正在拼出一条能跑、能控、能验证的 AI 自运转链上操作系统⚙️\n\n你是不是也刷到 Warden Protocol 和 Bless Network 的合作公告了?\n我看到的时候第一反应是:\n“哦,又是两个热叙事项目互捧?”\n\n但点进去看细节才发现——\n 不是捧,是动真格的。\n\n他们拼起来那一套组合,已经很接近 Web3 世界的“通用 AI 可信执行引擎”了。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n先拆 Bless 这边。\n\nBless Network 是目前 DePIN 赛道里少数 真正部署出大规模边缘节点的项目,\n截止 7 月初,已经有超过 500 万台活跃设备接入他们网络,主要通过浏览器插件(Chrome 扩展)与原生客户端运行。\n它不是那种“叫你买硬件来挖矿”的假 DePIN,\n而是真正用软件接入你的 CPU / GPU,后台调度轻量任务,比如:\n\nAI 推理(语义搜索、文本分类、嵌入计算)\n\n区块链数据处理(indexing、query)\n\n网页缓存/托管(CDN 加速)\n\n关键在于:这些任务是自动分发、动态计算、可验证的。\nBless 用 WebAssembly + WASI 来沙盒化每个执行任务,\n这意味着你设备上跑的代码不会碰你本地隐私,也不会“黑”你资源。\n\n更牛的是,他们和 Space and Time 合作,引入了 ZK 证明,\n可以生成 Verifiable Compute Proof(VCP),保证任务执行结果真实、可复查、链上可验证。\n这一整套设计,就是一个:\n\n轻量化、低门槛、可验证的去中心算力网络\n再简单说一遍:\n 你开着插件,后台悄悄帮别人跑任务,还能赚钱,而且所有计算都有 ZK 背书。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n那 Warden Protocol 为啥需要 Bless?\n\nWarden 的核心野心是:\n构建一个 跨链统一账户抽象层(Universal Modular Account Layer)\n它不是把你的账户变成更复杂的钱包,而是直接变成:\n\n一个 可编程的链上执行体(Programmable Controller)\n你可以把它想象成链上的“智能策略守门员”,只不过你可以定制它的行为逻辑,比如:\n\n每次转账前调用 AI 审核内容\n\n根据链上数据决定是否执行操作\n\n把一个意图分发到多个链执行\n为不同合约交互设置权限、限额、冷却期等规则\n\n而实现这些的关键模块就是 SPEX(Secure Programmable Execution)和 SPORF(Programmable Orchestrator for Remote Functions)\n\n这些模块其实就是 Warden 的“调度大脑”和“AI 插槽”,\n而 Bless 的边缘算力池,就是它最理想的 去中心化 AI 执行后端。\n\nWarden 可以把 AI 推理或数据处理需求打包成任务,发布到 Bless 网络:\n→ 由 Bless 网络调度到全球边缘节点运行\n→ 节点返回结果 + 生成 ZK 验证证明\n→ Warden 的策略模块验证后自动执行链上行为\n这样一来,你写下的账户逻辑,不只是人类能理解的规则,\n而是:链上自动调用、链下可验证执行、策略驱动合约自动化落地。\n\n这才是真正意义上的 “AI agent on-chain”:\n\n有智能(AI)\n\n有规则(Warden 策略)\n\n有执行能力(Bless 网络)\n有信任保障(ZK 验证)\n有全链部署能力(模块账户跨链联动)\n这一整套拼图,讲真,已经比大部分 AI Agent 项目“靠谱又能跑”。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n你现在是不是开始理解我标题那句话了:\nBless 不只是挂机赚钱工具,Warden 也不只是账户抽象\n→ 他们在合体造 Web3 最实用的 AI 执行操作系统\n\n而我们这些 builder、使用者、玩家,在这个系统里就不再只是点点签名的用户,而是真正可以:\n写 AI 行为策略\n配合链上账户执行\n靠边缘网络驱动运转\n\n实现“链上自动化、智能化”的生活 / 工作流\n你甚至可以想象一个未来场景:\n\n你设置好一个「Warden 策略账户」:\n\n它每天自动调用一个 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One 主网概述\n\n刚入圈那阵子,每次听到「Rollup」、「L2」、「Sequencer」脑袋都快打结。\n直到我真正体验了一次 @arbitrum 主网,那种“原来 L2 已经这么顺滑”的惊艳感,才彻底让我明白它为什么能成为以太坊扩容的头号王牌之一。\n\n今天来认真聊聊我眼中的 @arbitrum_cn \n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\nArbitrum One 是什么?\n它是 Arbitrum 生态中最早上线、最成熟的主网链,建立在以太坊之上,用 Optimistic Rollup 技术打包交易后统一结算回以太坊,从而实现了低成本+高吞吐+安全继承三效合一。\n\n不像一般 L2 还在磨合期,Arbitrum One 已经是个“老练打工人”——基础设施稳定,兼容性好,TVL、DApp 数量、用户数全是 L2 No.1。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n什么体验最打动我?\n两个字:丝滑。\n钱包切个 Arbitrum One 网络,一样用 MetaMask。\n交互像在用以太坊主网,但手续费常常只要几毛钱,交易确认也非常快。\n\n不管是撸金、打 DEX、mint NFT 还是刷 SocialFi,我完全不会感到“这是个 Layer2”,更像一个更聪明的 Layer1——你啥都可以干,但成本低、体验快。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n技术底层是什么魔法?\nArbitrum One 背后是一整套独特的“Arbitrum Nitro”架构:\nOptimistic Rollup 机制:先假设交易有效,只在有人挑战时才验证,节省大量资源。\n\nAnyTrust DAC(数据可用性委员会):确保链下数据始终可取回,安全性强。\nWASM 兼容虚拟机 + Geth 改写运行时:更高效、更兼容、更轻量。\n\nSequencer 层的存在,也大大提升了 TPS 和用户体验。\n\n简而言之,Arbitrum One 是目前最接近“以太坊信仰 + Web2 用户体验”结合点的 L2。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n生态繁荣到什么程度?\n随便数几个你一定听过的项目:\n\nGMX(永续交易龙头)、Radiant Capital(借贷协议)、TreasureDAO(链游聚合器)、Dopex、Camelot、Pendle、Rari、Vela、Cap、Plutus、Jones DAO……\n还有最近疯狂活跃的 meme 项目,比如 $Degen、$ARBKek、$PepeAI 等,热度一点也不输 Solana。\n生态不只是丰富,更是活泼,“好玩又能赚”这点,Arbitrum 的氛围感真的做得很对胃口。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n关于 $ARB 空投与治理\n2023 年 $ARB 空投一度引爆全网,现在整个治理系统也在逐渐成熟。Arbitrum DAO 已是目前最活跃、TVL 最大的链上治理组织之一。\n\n近期的 Grants Program(生态资助)、游戏赛道扶持、新的激励提案,不断强化 Arbitrum One 的生态飞轮。\n\n我个人也一直关注 ARB 代币在治理权和生态增长上的联动——这并非一个“卖空气”的代币,而是具备参与感与“Builder Power”的载体。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n为啥我对 Arbitrum One 长情?\n在众多 L2 中,Arbitrum One 是少数同时做到技术成熟、生态繁荣、用户体验友好、治理活跃的全能型选手。\n\n它不需要靠短期的 Narrative 来炒作,因为底层逻辑站得稳。它也不像某些 L2 靠VC砸钱维持人气,它真正吸引的是开发者、用户和长期主义者。\n\n如果你还没深入体验过 Arbitrum One,那这条主网绝对值得你花时间亲身感受。\n\n下一期我可以来写写 Orbit 分叉、Stylus VM、Blast bridge 等 Arbitrum 新技术的探索,有兴趣的记得关注我。\n\n欢迎大家分享你们在 Arbitrum One 上最喜欢的 DApp 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AI”,\n结果反过来,是 AI 在训练我们相信它。\n\n什么 ChatGPT 回答问题、Claude 总结新闻、Llama 自动写稿——\n它们每一次开口,看似在帮我们,\n其实都在把你我吐出的信息,反哺进它们自己的胃里。\n问题是:\n\n你知道它吃进去的,到底是“知识”,还是“垃圾”吗?\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n我们每个人都在不知不觉中,\n成为了 AI 模型的“数据搬运工”。\n\n从 prompt 到笔记、从推文到对话记录,\n这些内容都变成了训练数据的“填料”。\n\n模型越大,吃得越多;\n但吃得多,不代表吃得对。\n\n你能确定 GPT 生成的财经摘要不是胡编的?\n你敢保证 Claude 提到的“科学结论”不是被拼接出来的?\n你有没有发现它们引用资料时,越来越模糊,越来越“自信地胡说八道”?\n\n但我们却越来越习惯——\n“AI 说了就信,查都懒得查。”\n这时候, @Mira_Network 的意义才开始显现出来。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n@Mira_Network 不做模型,它做的是验证层。\n一句话解释:AI 输出的每一字一句,都要被检查。\n\n它的架构里,核心是 Bin Protocol:\n模型输出会被丢进一个“验证场”,\n多模型复算 + 语义对比 + 一致性检测,\n如果判断结果可靠,才会生成一个“链上签名证明”。\n否则对不起,这句话不合格,上不了链。\n\n这听起来有点像 Web2 的 StackOverflow 审核机制,\n但 Mira 是无需信任、全链验证,是真正面向链上 AI 合约的 Infra。\n它像一个 AI 世界的纪检委,不怕你强,不怕你火,\n但你得讲实话,才配执行任务、连接钱包、跑合约。\n一句话总结它的逻辑:\n\n“输出可以有观点,但必须能被验证。”\n“数据可以带偏好,但不能造假。”\n“模型可以有差异,但不能逃避责任。”\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n说真的,现在大部分“AI+Web3”项目,\n都还停留在“我们也接了个模型”的阶段,\n你一看白皮书,十页叙事,半页产品。\n\n但 Mira 已经上线了完整的验证系统 + 开放接口 + 多模型兼容。\n像 Klok 这个小 demo,就已经支持 GPT-4o、Claude3、LLaMA3 等模型的多重比对,\n你可以直接体验哪个模型在某些场景下,爱“瞎说”。\n\n未来无论是链上搜索、AI 合约执行、AI 交易决策、AI DAO 管理,\n你都得先回答一句:\n这条信息,真的可信吗?\n这句话,可以追责吗?\n\nMira 就是解决这件事的基础设施。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n想象一下未来——\n每一个大模型输出,不再是“黑箱里的神谕”,\n而是可以查、可验证、有签名、有责任的“可信输出”。\n我们不是要控制 AI,我们是要规范 AI 如何“做人”。\n\nGPT 可以继续说它的,\nClaude 可以继续“引经据典”,\nLLaMA 也可以继续生成段子。\n\n但 Mira 会告诉你——\n“它说的,靠不靠谱。”\n“你信它,要不要冒风险。”\n“它说错了,是不是该负责。”\n\n你以为你在训练 AI,\n其实,AI 正在训练你该不该信它。\n但只有 Mira,让你重新掌握“信任权”。\n\n—\n\n📌 项目方 @Mira_Network\n📌 可体验:https://t.co/dH6R8uA799\n看完这篇,如果你也觉得「验证比生成更重要」\n欢迎点赞、转发、评论说说你最怕哪种 AI 幻觉。\n也欢迎丢一句 prompt 给我,我来现场用 Klok 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项目千千万,但能从协议层自洽出“智能体经济系统”的,我目前只看到一个: @TheoriqAI\n\n在一堆卷模型、堆 GPU 的项目还在炒“AI+DeFi”的时候,TheoriqAI 已经用 agent 模块化,把一个未来的“多智能体市场”原型部署在链上。这不仅是产品逻辑的不同,而是整个出发点、结构哲学都完全反过来。\n\n他们不是用 AI 去增强 DeFi,而是从头把 AI 当作参与者,嵌入协议的决策链条。这件事非常野,也非常真实。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n我们先从他们构建的东西说起。\n\nTheoriq 本质上是一个「智能代理协议网络」,它不提供一个产品,而提供一整套可组合的Agent Layer。这个 Layer 有几个关键特色:\n\nAgent 是一等公民,在链上有身份、有行为能力、有访问权。\n\n每一个 agent 都是模块化的,可组合、可替换、可自演化。\nAgent 之间通过事件流和策略约束协作运行,组成 Swarm 生态。\n简单说,它不像传统的“AI 服务 API”,更像一套链上运行的「逻辑自动体系统」。Theoriq 的目标不是做一个“更聪明的 Bot”,而是构建一个 Swarm-based System for Onchain Decision Making。这种范式目前在整个 Web3 都是少见的。\n\n再看技术底层。Theoriq 不依赖任何单一模型,而是构建了一个 信号驱动 + 策略层级 + 行动执行 的智能体堆栈(Agent Stack):\n\n最底层是 Signal Agent,负责提取链上数据、LP 市场状态、波动因子等。\n\n中层是 Policy Agent,定义资金使用规则,风险偏好、仓位分布等。\n\n顶层是 Execution Agent,将策略部署至链上(Uniswap v4、Arrakis、Balancer 等)。\n你可以把它类比成一个 “链上微服务架构 + 策略执行 VM”,每个 Agent 是服务模块,也是个体参与者。\n关键是,这个架构未来不局限于 LP 管理 —— 它可以扩展为智能债务结构管理、跨链套利协调、治理委托执行、MEV 防御优化等各种 AI-onchain use case。\n\n换句话说,Theoriq 不只是 AlphaSwarm,它是一种「智能代理协议范式的第一性构建者」。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n再来讲生态设计,我觉得他们最聪明的地方不是代币经济,而是 Agent 的“信任机制”。\n\nTheoriq 引入了 Agent Credentialing System(ACS),通过链上注册、信誉机制、任务履约等方式对 agent 行为打分。这形成了一个去中心化信任框架:\n\n用户调用 Agent,不是只看它是谁,而是看它过去的表现、是否通过质押参与机制。\n\nAgent 之间可以构成协作关系网络,比如某个 Signal Agent 只愿意跟特定的 Policy Agent 配对。\n\nToken 不是激励行为,而是维系 agent 网络协作与使用权的核心资源(Agent-to-Agent Payment Unit)。\n这种模型比起传统的“AI as SaaS”完全不同,它模拟的是 链上智能体的博弈与自然选择过程。\n在我看来,这正是最早期的 “Agent Economy” 模拟器。它不是做一个产品来卷用户,而是在建一个让 Agent 自主博弈、合作、演化的 sandbox。\n\n说到底,Theoriq 的真正野心不是做一条 DeFi AI 工具链,而是干一件更大的事:\n\n重构“协议”与“意图”之间的连接方式。\n\n传统协议靠人来读合约、下指令、调策略;\nTheoriq 试图让一群 Agent 来替你做这一切;\n而这群 Agent,本身就是协议的一部分。\n\n换句话说,它不是一个 “用 AI 优化链上操作” 的项目,而是一个 “让 AI 成为链上参与者”的系统。\n\n这思路,已经不是普通意义上的 Infra,而是链上操作系统层级的突破。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n很多人会说:现在什么都还没落地,怎么敢下判断?\n我反而觉得,这正是看懂 Theoriq 的好时机。因为你现在能看到的,不只是产品 demo,而是底层哲学 + 技术架构 + 系统生态的完整融合。\n\n它像早期的以太坊、Cosmos、Polkadot,刚出现时没人听得懂,但懂的人会瞬间明白:这个方向只要一跑通,就是一整个范式转移。\n\n🧠 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H1 2025 生态报告解读\n\n有些公链是技术奇迹,有些是生态幻觉\n而 @0G_labs ,是我近期见过,最有可能「兑现 AI 链叙事」的那种。\n\n一个全栈 AI L1,半年内集结 236 个项目、350+ 次集成,\n这些数字背后,藏着去中心化 AI 世界的秩序初稿。\n这篇文,我来聊聊:0G 到底是怎么做到的?谁在用它?以及它为什么正在成为 deAI 的主战场。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n先说重点:0G 是一个 模块化 AI 公链,生态里大部分项目不是在蹭热点,而是真的在用。\n\n它有 4 个核心基础层,每一层都可独立调用组合,支撑不同类型的 AI 原生场景:\n\n0G Chain(130+ 集成):AI 智能合约执行环境,适用于 agent frameworks、AI DEX、链游逻辑等。\n\n0G Storage(80+):高吞吐、分布式存储,供 DePIN 和 AI 模型存 metadata、proofs、训练数据。\n\n0G DA(80+):兼容 zk-rollup 的可验证数据可用性层,适合 AI 状态更新、图数据流转。\n\n0G Compute(60+):支持 onchain AI 推理与训练的 GPU 计算网络,速度快、可验证。\n一个项目可以只用其中一个模块,也可以组合搭建完整 deAI 应用。真正做到可组合、可验证、可规模化。\n这也是为什么“总集成数”>“总项目数”——大家都在叠加式用。\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n那谁在用?\n\n从 H1 报告来看,整个生态正在从「L1 基建」→「垂类 AI 堆栈」→「落地应用」高速演进。\n\n目前项目主要分布在以下几个方向:\n\n💡General AI Infra(16 项)\n代表项目如 Warden Protocol、Bagel、QuillAI,打造 AI 操作系统、协作训练平台等——为 Web3 世界注入“AI 智能骨干”。\n\n🧠 Inference & 模型层(10 项)\n比如 Sentient、Lagrange、Pond,从 Open AGI 到 ZK 验证模型输出,围绕「去中心化推理 + 可验证输出」展开突破。\n\n🤖 AI Agents(14 项)\n像 Talus、ElizaOS、Virtuals、Crestal 正在构建具备经济激励机制的“AI 智能体网络”——它们能在链上执行任务、协作、赚钱。\n\n📊 AI 数据与分析(19 项)\n从 Mind Network、Sapien、Fraction 到 Public AI,目标是解决 AI 模型“高质量训练数据从哪里来”的关键瓶颈。\n📱 AI 原生应用(8 项)\n PlayAI、Morphic、Huddle01、Peony 等,开始把 AI 真正“卷进”用户端体验,探索 Web3 语境下的新消费级 dApp。\n\n🖥️ AI 计算 & Orchestration(6 项)\n例如 Gevulot、Axone、Keyless、GT Protocol,围绕 GPU 托管、模型编排、推理调度,替代中心化 AI 云服务。\n此外还有传统 Web3 领域项目开始「AI 化」:\n\nDeFi / Interop / RWAs(27 项):Credible Finance、Zia、Euclid Protocol,把 AI 用于智能风控、交易决策。\nGaming & NFTs(18 项):Shrapnel、ArenaX、DeHouse,打造 AI NPC、个性化叙事、生成式内容。\n\nDePIN(14 项):Aethir、Spheron、Hyperbolic,让 GPU 计算、电力、网络带宽等变成链上资源。\n安全 / 身份 / 隐私(10 项):Octane、Beacon、SPACE ID,用 ZK + AI 实现隐私计算和身份可信性。\n\nInfra & Rollups(41 项):thirdweb、Quicknode、Ankr、Blockdaemon,为 AI 构建 Rollup/RPC/验证环境。\n甚至连 Google Cloud、Stanford、IC3、NTU 也已展开合作研究。\n\n这不是“还在构想中的 AI 公链”,而是一个已经“在全堆栈落地”的超级平台。\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n说实话,看完这份 H1 报告,我最大的感受是:\n\n0G 正在定义什么叫“AI 原生链”。\n这不是 Web3 加个 AI 模块,而是从架构开始,就是为 AI 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就是这种:你不盯着看,还真以为它最近在睡觉。但当我挖了一下,发现它悄悄搞了个战略动作——而且方向选得很刁。\n\n不是再发什么积分,不是上线个新 testnet,而是直接在 AI 的数据源头 上动手。\n\n这更新,说实话,不吵不闹,但很猛。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n三周前,Mira 在几乎没有宣发的情况下,上线了一个叫 Data Partner Program 的战略合作计划。初看没什么特别,但稍微推演一下,就能嗅出这个动作的底层野心。\n\n你可以把它理解成 Mira 从“验证 AI 输出”,进化到了“改写 AI 输入”。\n\n他们开始布局数据层——但不是去抢数据,而是想建立一套「验证过的数据供给系统」。\n\n这就是 Mira 不一样的地方:它不是和 OpenAI 竞争,也不是要干掉 Claude,而是要做一件很 web3 的事——用链上机制给 AI 数据“做信任校验”。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n为什么这事重要?说点人话。\n\n现在所有大模型都面临一个问题:内容幻觉(hallucination)太严重。\n\n你问它谁是美国总统,它可能告诉你是乔布斯。\n你让它总结一篇文章,它有时会「一本正经地胡说八道」。\n\n为什么?因为它吃进的数据本来就脏。甚至很多数据,本来就是别的 AI 编出来的幻觉,又被收进训练集,变成另一个幻觉的源头。\n这就像是你喂一个厨师吃垃圾,然后期望他做出米其林晚宴。\n\nMira 的思路是:我们不能只怪模型输出烂,得从输入就开始处理。\n\n所以他们在构建一个机制:\n\n由可信数据源 → 经验证系统 → 输送进 AI 网络\n\n你可以想象成一条“数据净化链”,从 web3 的内容来源开始,把每一步都记录、评估、质控,并上链存证。最终,AI 不再“看心情输出”,而是基于可信语料做推理。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n说白了,这才是“AI Infra”该做的事。\n\n很多项目喊着要做 AI + Web3,但到头来只是搞个积分系统、拉一堆人注册、接个 API 就说自己是 AI 项目。\n\nMira 的做法是往下扎,把 AI 最深层的不稳定因素「数据失控」拉回轨道。\n\n他们并没有动模型,也没动硬件,而是动了数据质量。\n\n这个做法很“冷门”,但可能是未来所有想认真跑 AI 的项目绕不开的一个环节。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n再讲讲他们所谓的“Data Partner”。\n\n这不是泛泛地拉个协议合作 logo,而是真的邀请内容提供者、协议开发者、AI 使用方,共同建立起可追溯的数据链。\n\n比如:\n\n如果你是教育平台,可以提供结构化知识内容;\n\n如果你是游戏开发者,可以贡献用户行为数据;\n\n如果你是协议方,可以贡献链上交互语料。\n这些数据会被 Mira 的验证层标准化、标记化、评分化,最终形成一个 可被 AI 安心引用的知识库。\n想象一下——未来某个 AI assistant 回答问题时,会跟你说:\n\n“这是经 Mira 验证的数据,来自 @某Gamefi 项目、某DAO 成员贡献的链上履历片段。”\n\n而不是“我猜的”或“网上好像是这样”。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n这逻辑听起来很像“做数据银行”?没错,但它更像是“做数据联合治理网络”。\n\nMira 没有在改造 AI,它在帮 AI 接入 web3 的信任机制。\n\n如果它能跑起来,就意味着我们有可能第一次看到:\n\n→ AI 的知识源,不再是封闭的互联网碎片\n→ 而是开放的、可验证的、链上集体共识。\n\n这不就是 web3 理想的应用场景之一吗?\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n现在,Mira 没有发代币,也没有搞 hype,但他们显然在打长期仗。\n\n他们不急着把 narrative 卖出去,而是稳稳打磨基础设施逻辑。\n这种项目,不一定天天冒泡,但一旦冒泡,可能就跳过了一整个阶段。\n\n如果你是:\n\n内容创造者:你有可能成为 Mira 的数据供体\n\n协议项目:你能和 Mira 联动,为 AI 贡献更优质的 onchain 语料\nAI 从业者:你能基于 Mira 的验证层,训练或接入可信模型\n\n现在这个时间点,或许正是一个低调但超前的 alpha entry。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n我会持续追踪这个“数据验证战略”后续的开放节奏。\n目前 Mira 似乎还处于邀请制或白名单合作期,但后面一旦开放用户数据桥接,内容贡献机制,以及数据积分系统,那将会是非常值得参与的红利期。\n\n更不用说它后续如果开放“Data as a Service”接口给其他 LLM,这条路可能不只限于他们自家用。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n所以——别等别人都在用 Mira 的数据时你才开始了解。\n\n现在就去关注他们的 Data Partner 框架、参与路线图,看看你是否有资格成为数据贡献方,或者搭建基于 Mira 的数据产品。\n\n这是那种现在动手,未来能「让 AI 说话更靠谱」的机会。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n你觉得 Web3 有没有机会改变 AI 的幻觉问题?\n\n评论区聊聊,我想听听 builder 们的观点。\n\n也欢迎点赞、分享,让更多人看到这波“AI 数据层的革命”。","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null}],"ctweets":[{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1763125712066945025","name":"Mira","screen_name":"Mira_Network","indices":[101,114]},{"id_str":"1763125712066945025","name":"Mira","screen_name":"Mira_Network","indices":[101,114]},{"id_str":"1763125712066945025","name":"Mira","screen_name":"mira_network","indices":[1114,1127]}]},"favorited":false,"lang":"zh","quoted_status_id_str":"1970052857442566639","quoted_status_permalink":{"url":"https://t.co/ttVKeE5EAH","expanded":"https://twitter.com/Mira_Network/status/1970052857442566639","display":"x.com/Mira_Network/s…"},"retweeted":false,"fact_check":null,"id":"1970399386862788716","view_count":10243,"bookmark_count":7,"created_at":1758614796000,"favorite_count":381,"quote_count":2,"reply_count":324,"retweet_count":3,"user_id_str":"1184096289283133440","conversation_id_str":"1970399386862788716","full_text":"Mira Network 最近动作频频,让我这个做 AI+区块链的粉丝心里咯噔咯噔跳。有人说它要从幕后走上台前,我觉得是真的。下面我整理了一些最新更新+个人看法,值得我们盯紧。\n\n最近消息里最重磅的是 @Mira_Network 的公共测试网已经上线。这不是简单上线一个节点试试的阶段,而是已接入多个 AI 模型,用来做共识验证 AI 输出的信任层。这个层一旦稳定,就是主网和代币生成事件之前极为关键的一步。\n\n用户现在可以通过 Klok 应用互动,并从中获得经验与积分。验证过的 AI 输出意味着未来输出可信度+责任机制要建立起来。\n\n测试网也意味着社区可以提前试错。看模型共识机制是怎样运作的,是不是会出现偏差的问题,是延迟是不是能接受,是奖励机制是不是公平。\n\n市场反应也初见端倪:交易活动有放大倾向,社区关注度上升。看图表,MIRA 的价格/交易量/活跃地址等链上指标都有被追踪的迹象。\n\n除了测试网,一个比较少被主流报道但对信任/声誉影响大的,是 Mira 在验证 AI 输出的验证层思路在推进。这部分就像在给 AI+区块链叠加一个判定 + 信任 + 反馈的模块,不是纯粹模型训练/部署那么简单。用户、评估者、社区都要参与,既是共识也是责任。这个结构如果做得好,对质量、对未来审查机制/输出可靠性的要求会是一个差异化优势。\n\nMira 在 Messari 的资料里,是一个 AI 工具/AI 平台类型项目,专注于社区驱动工作流程、实时反馈、AI 输出的验证,以及 AI 资源所有权的区块链层面。\n\n融资面稍早前也有著名风险投资机构参与/领投,包括 BITKRAFT Ventures 与 Framework Ventures 等。种子轮融资大约是 900 万美元。\n\n代币还没完全落地。公共测试网是 TGE 前的大信号,但代币发行、分配、经济模型、安全审计这些关键点依然是悬念,没有完全透明/最终确定。\n\n操作体验上,如果你参与了测试网,用 Klok,能感觉到 AI 输出和“社区评估”的环节被拉进来了,不再是黑箱。这种信任感对用户黏性/口碑很重要。\n\n不过也有疑问:AI 模型输出验证过程中是否会卡、谁来给评估者做验证者,奖励机制是否公平如何防?代币什么时候落地,经济激励是否能跟上这些工作量和信任成本,也会是关键。\n\n我觉得 Mira Network 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正在改变代币叙事 从冷冰冰的数字到有生命的资产\n\n在加密货币的世界里,新鲜的概念层出不穷,但能让我认真停下来看一眼的并不多。最近 @RialoHQ 却让我觉得值得深入探讨,因为他们正在做的事,可能直接重塑我们对代币的理解。\n\n代币在过去被当成静态的价值符号,价格涨跌是唯一的关注点。但区块链的发展早已不该停留在这种表层逻辑。Rialo 的切入点是把代币和人的行为、声誉、数据挂钩,让它不再只是一个冷冰冰的数字,而是带有成长性和叙事性的资产。你可以把这理解为一种链上身份的升级:从单一的财富证明,走向综合的声誉证明。\n\n为什么这很重要?因为整个行业都在寻找信任机制的替代方案。匿名环境下,过度投机、短期套利、社区空心化几乎成了常态。如果链上声誉能够被代币化,参与者的行为和贡献将被真实记录下来,好的声誉能带来更低的借贷利率、更高的治理权重,甚至让 NFT 的价值建立在真实的互动与身份积累之上。这不仅仅是代币形态的改造,更是加密治理逻辑的一次重构。\n\n资本也在用真金白银下注。Rialo 背后的团队 @itachee_x @firearrowmage @gametheorizing 刚完成约两千万美元融资,这说明机构认可这种以声誉为核心的模式具有长期潜力。而他们已经开放候补名单,意味着第一批愿意验证这个叙事的用户正在进场。这一步其实非常关键,因为任何声誉系统都需要用户的真实参与才能建立初始价值。\n\n从个人角度,我最期待的是这种模式能否解决当前加密生态的三大痛点:缺乏长期信任关系、项目社区无法有效沉淀价值、投机与建设的激励严重失衡。Rialo 如果成功,就等于在去中心化世界里建立了一种新的信用基础设施,能够让声誉成为可流动、可交易、可编程的资产。\n\n当然,这条路绝不轻松。数据的真实性验证、隐私保护的平衡、防止身份滥用,这些都需要在机制设计和技术落地上给出答案。但方向本身是有前景的,也是少数真正能引发行业深度讨论的议题。\n\n或许几年后我们回过头,会发现这是代币进化的重要拐点。它们不再只是数字符号,而是承载了个人与社区的长期价值沉淀。\n\n我很好奇大家怎么看待这个叙事 你认为链上声誉什么时候会迎来爆发点 Rialo 能不能成为第一个点燃火焰的项目 如果觉得这个话题有意思 不妨点赞 留言 转发 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圈的朋友注意⚠️\n\n你有没有试过一边打英雄联盟,一边幻想:“要是我这波五杀有人看到就好了!”\n现在机会真的来了,而且这波是 Web3 项目方 @Sidekick_Labs 亲自下场整活,不是随便玩玩!\n他们搞了一个《中韩英雄联盟 KOL 表演赛》,奖金池高达 $40,000 USDT/USDC,还不限段位、不限粉丝数——说白了,只要你敢上场,就有机会出镜、出圈、出成绩。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n先把重要信息划重点:\n\n▶ 比赛时间(UTC+8):\n\n🇨🇳 中国区小组赛:7月21日 17:00–22:00\n\n🇰🇷 韩国区小组赛:7月22日 17:00–22:00\n🏁 中韩总决赛:7月26日 13:00–20:00\n▶ 奖金结构:\n\n冠军队伍奖金超 $10,000 USDT/USDC\n\n所有入选者都有奖拿(不是“可能”,是确定的)\n还有「Red Packet Rain」、「MVP 奖」、「高光时刻奖」、「内容激励奖」等等丰富彩蛋\n▶ 报名截止时间:7月18日 23:59\n\n点评论区链接报名!别拖了,迟一步都可能错过。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n🧠那 Sidekick 是谁?为什么 Web3 项目要搞 LOL 表演赛?\n\n简单说,@Sidekick_Labs 是一个为创作者而生的链上内容激励平台。他们想做的是——\n 把创作者从“内容被动输出者”变成“影响力拥有者 + 收益分享者”。\n\n你产内容,不再只是博眼球、赚广告,而是每一条高质量输出都能在链上确权、被社区发现、获得持续激励。\n这次比赛,其实就是 Sidekick 对他们内容经济模型的一次“实战演示”:\n\n用电竞当内容生产工具\n\n用高光剪辑当传播媒介\n\n用链上激励系统做赛后分发\n你打得好,平台给你曝光。你内容做得酷,平台给你收益。这是传统平台做不到的事。\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n我自己也关注 Sidekick 一段时间了,他们有点像 Web3 版的 B 站+抖音+创作者 DAO 混合体,但底层是完全链上、透明分发的机制。\n最打动我的一点是——他们是真想让创作者“靠才华吃饭”,而不是靠蹭热点或卷 SEO。\n\n电竞是内容,meme 是内容,推文、剪辑、讲解、reaction,甚至是你自己玩得很烂但很有梗的视频,也都是内容。\nSidekick 提供的是一个新型舞台,把这些内容的价值“量化出来”,还愿意为内容生态长期投入。\n\n所以这场 LOL 表演赛你可以理解成一个入口,一场创作者的集体上链实验,一次流量与收益的链上定价尝试。\n你可能是参加者,也可能是围观者,但如果你是创作者,这个舞台,你迟早会走上去。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n📢 总结一句话:\n\n这不是一场普通的 LOL 友谊赛,这是 Sidekick 送给 Web3 创作者的“英雄试炼场”。\n打得好能出圈,剪得好有激励,看得爽还能抢红包。\n\n⏰ 报名只到 7 月 18 日 23:59\n🎮 不限段位、不限粉丝、不限国家,只要你敢打、敢剪、敢亮出来\n\n👉 点评论区链接报名\n👉 艾特你 LOL 圈的 KOL 好友\n👉 比赛日锁定 Sidekick 直播间,一起 witness 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项目千千万,但能从协议层自洽出“智能体经济系统”的,我目前只看到一个: @TheoriqAI\n\n在一堆卷模型、堆 GPU 的项目还在炒“AI+DeFi”的时候,TheoriqAI 已经用 agent 模块化,把一个未来的“多智能体市场”原型部署在链上。这不仅是产品逻辑的不同,而是整个出发点、结构哲学都完全反过来。\n\n他们不是用 AI 去增强 DeFi,而是从头把 AI 当作参与者,嵌入协议的决策链条。这件事非常野,也非常真实。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n我们先从他们构建的东西说起。\n\nTheoriq 本质上是一个「智能代理协议网络」,它不提供一个产品,而提供一整套可组合的Agent Layer。这个 Layer 有几个关键特色:\n\nAgent 是一等公民,在链上有身份、有行为能力、有访问权。\n\n每一个 agent 都是模块化的,可组合、可替换、可自演化。\nAgent 之间通过事件流和策略约束协作运行,组成 Swarm 生态。\n简单说,它不像传统的“AI 服务 API”,更像一套链上运行的「逻辑自动体系统」。Theoriq 的目标不是做一个“更聪明的 Bot”,而是构建一个 Swarm-based System for Onchain Decision Making。这种范式目前在整个 Web3 都是少见的。\n\n再看技术底层。Theoriq 不依赖任何单一模型,而是构建了一个 信号驱动 + 策略层级 + 行动执行 的智能体堆栈(Agent Stack):\n\n最底层是 Signal Agent,负责提取链上数据、LP 市场状态、波动因子等。\n\n中层是 Policy Agent,定义资金使用规则,风险偏好、仓位分布等。\n\n顶层是 Execution Agent,将策略部署至链上(Uniswap v4、Arrakis、Balancer 等)。\n你可以把它类比成一个 “链上微服务架构 + 策略执行 VM”,每个 Agent 是服务模块,也是个体参与者。\n关键是,这个架构未来不局限于 LP 管理 —— 它可以扩展为智能债务结构管理、跨链套利协调、治理委托执行、MEV 防御优化等各种 AI-onchain use case。\n\n换句话说,Theoriq 不只是 AlphaSwarm,它是一种「智能代理协议范式的第一性构建者」。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n再来讲生态设计,我觉得他们最聪明的地方不是代币经济,而是 Agent 的“信任机制”。\n\nTheoriq 引入了 Agent Credentialing System(ACS),通过链上注册、信誉机制、任务履约等方式对 agent 行为打分。这形成了一个去中心化信任框架:\n\n用户调用 Agent,不是只看它是谁,而是看它过去的表现、是否通过质押参与机制。\n\nAgent 之间可以构成协作关系网络,比如某个 Signal Agent 只愿意跟特定的 Policy Agent 配对。\n\nToken 不是激励行为,而是维系 agent 网络协作与使用权的核心资源(Agent-to-Agent Payment Unit)。\n这种模型比起传统的“AI as SaaS”完全不同,它模拟的是 链上智能体的博弈与自然选择过程。\n在我看来,这正是最早期的 “Agent Economy” 模拟器。它不是做一个产品来卷用户,而是在建一个让 Agent 自主博弈、合作、演化的 sandbox。\n\n说到底,Theoriq 的真正野心不是做一条 DeFi AI 工具链,而是干一件更大的事:\n\n重构“协议”与“意图”之间的连接方式。\n\n传统协议靠人来读合约、下指令、调策略;\nTheoriq 试图让一群 Agent 来替你做这一切;\n而这群 Agent,本身就是协议的一部分。\n\n换句话说,它不是一个 “用 AI 优化链上操作” 的项目,而是一个 “让 AI 成为链上参与者”的系统。\n\n这思路,已经不是普通意义上的 Infra,而是链上操作系统层级的突破。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n很多人会说:现在什么都还没落地,怎么敢下判断?\n我反而觉得,这正是看懂 Theoriq 的好时机。因为你现在能看到的,不只是产品 demo,而是底层哲学 + 技术架构 + 系统生态的完整融合。\n\n它像早期的以太坊、Cosmos、Polkadot,刚出现时没人听得懂,但懂的人会瞬间明白:这个方向只要一跑通,就是一整个范式转移。\n\n🧠 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H1 2025 生态报告解读\n\n有些公链是技术奇迹,有些是生态幻觉\n而 @0G_labs ,是我近期见过,最有可能「兑现 AI 链叙事」的那种。\n\n一个全栈 AI L1,半年内集结 236 个项目、350+ 次集成,\n这些数字背后,藏着去中心化 AI 世界的秩序初稿。\n这篇文,我来聊聊:0G 到底是怎么做到的?谁在用它?以及它为什么正在成为 deAI 的主战场。\n\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n先说重点:0G 是一个 模块化 AI 公链,生态里大部分项目不是在蹭热点,而是真的在用。\n\n它有 4 个核心基础层,每一层都可独立调用组合,支撑不同类型的 AI 原生场景:\n\n0G Chain(130+ 集成):AI 智能合约执行环境,适用于 agent frameworks、AI DEX、链游逻辑等。\n\n0G Storage(80+):高吞吐、分布式存储,供 DePIN 和 AI 模型存 metadata、proofs、训练数据。\n\n0G DA(80+):兼容 zk-rollup 的可验证数据可用性层,适合 AI 状态更新、图数据流转。\n\n0G Compute(60+):支持 onchain AI 推理与训练的 GPU 计算网络,速度快、可验证。\n一个项目可以只用其中一个模块,也可以组合搭建完整 deAI 应用。真正做到可组合、可验证、可规模化。\n这也是为什么“总集成数”>“总项目数”——大家都在叠加式用。\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n那谁在用?\n\n从 H1 报告来看,整个生态正在从「L1 基建」→「垂类 AI 堆栈」→「落地应用」高速演进。\n\n目前项目主要分布在以下几个方向:\n\n💡General AI Infra(16 项)\n代表项目如 Warden Protocol、Bagel、QuillAI,打造 AI 操作系统、协作训练平台等——为 Web3 世界注入“AI 智能骨干”。\n\n🧠 Inference & 模型层(10 项)\n比如 Sentient、Lagrange、Pond,从 Open AGI 到 ZK 验证模型输出,围绕「去中心化推理 + 可验证输出」展开突破。\n\n🤖 AI Agents(14 项)\n像 Talus、ElizaOS、Virtuals、Crestal 正在构建具备经济激励机制的“AI 智能体网络”——它们能在链上执行任务、协作、赚钱。\n\n📊 AI 数据与分析(19 项)\n从 Mind Network、Sapien、Fraction 到 Public AI,目标是解决 AI 模型“高质量训练数据从哪里来”的关键瓶颈。\n📱 AI 原生应用(8 项)\n PlayAI、Morphic、Huddle01、Peony 等,开始把 AI 真正“卷进”用户端体验,探索 Web3 语境下的新消费级 dApp。\n\n🖥️ AI 计算 & Orchestration(6 项)\n例如 Gevulot、Axone、Keyless、GT Protocol,围绕 GPU 托管、模型编排、推理调度,替代中心化 AI 云服务。\n此外还有传统 Web3 领域项目开始「AI 化」:\n\nDeFi / Interop / RWAs(27 项):Credible Finance、Zia、Euclid Protocol,把 AI 用于智能风控、交易决策。\nGaming & NFTs(18 项):Shrapnel、ArenaX、DeHouse,打造 AI NPC、个性化叙事、生成式内容。\n\nDePIN(14 项):Aethir、Spheron、Hyperbolic,让 GPU 计算、电力、网络带宽等变成链上资源。\n安全 / 身份 / 隐私(10 项):Octane、Beacon、SPACE ID,用 ZK + AI 实现隐私计算和身份可信性。\n\nInfra & Rollups(41 项):thirdweb、Quicknode、Ankr、Blockdaemon,为 AI 构建 Rollup/RPC/验证环境。\n甚至连 Google Cloud、Stanford、IC3、NTU 也已展开合作研究。\n\n这不是“还在构想中的 AI 公链”,而是一个已经“在全堆栈落地”的超级平台。\n☆¤·.¸¸.·´¯·.¸¸.¤☆๑这里是分隔线๑☆¤·.¸¸.·´¯·.¸¸.¤☆\n\n说实话,看完这份 H1 报告,我最大的感受是:\n\n0G 正在定义什么叫“AI 原生链”。\n这不是 Web3 加个 AI 模块,而是从架构开始,就是为 AI 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