AR · 930

Anthropic 再收死亡威胁：有人扬言带 AR-15 步枪上门

比推消息， AI 快讯，旧金山警方接到 911 报警，称一名 Anthropic 用户威胁公司 CEO，并扬言携带 AR-15 步枪前往 500 Howard St. 的办公室，伤害在那里工作的员工。警方随后介入处置，但报警人身份尚未公开，目前也没有后续处置结果。这已经不是 Anthropic 近几个月第一次收到暴力威胁。《华尔街日报》4 月曾有人闯入其总部大堂，声称一名高管将被杀；同月还有人威胁员工家属，6 月又有人因退款问题扬言带手枪去公司。Citizen⁠

3天前

投入八年突然转向，以太坊为何放弃Poseidon？

作者：ChandlerZ，Foresight News原标题：八年投入急转弯，以太坊为何突然放弃Poseidon？8 月 13 日，以太坊研究员 Justin Drake 在 X 发文称，以太坊基金决定在 L1 层放弃 SNARK 友好型哈希算法 Poseidon，转而采用 SHA2 或 BLAKE2 等传统哈希函数。这个决定背后是八年研究、数千万美元投入的积累，以及对后量子密码学路线图的一次重大修正。Poseidon 自 2019 年推出以来，一直被视为 zkRollup 与 zkVM 等应用的理想哈希方案，它的结构使其在 SNARK 电路中比传统的基于二进制运算的哈希函数更便宜、更高效。但当后量子安全成为以太坊的硬性要求时，Poseidon 的局限性开始暴露。Justin Drake 表示，这一转变得益于突破性的 SNARK 设计进展，即通过「二进制域」运算方式，使传统哈希函数在 SNARK 电路中的性能可媲美此前专为 SNARK 优化设计的 Poseidon，单台笔记本电脑每秒可完成约 100 万次传统哈希调用验证。文中介绍，Poseidon 自 2019 年推出以来一直是主流的 SNARK 友好型哈希方案，为 zkRollup 与 zkVM 等应用提供安全保障。Justin Drake 称，规划显示生产级 leanVM 预计将于 2027 年推出，共识层、数据层与执行层的相关部署预计在 2028 年完成，以太坊基金会后量子团队也在加速推进二进制域相关研究。为什么是现在？传统哈希长期难以进入 SNARK，主要障碍来自计算语言差异。SHA2、BLAKE2s 和 Keccak 大量使用异或、移位等布尔运算，传统 SNARK 通常在大素数域上处理算术，模拟每一项位运算会产生高昂约束成本。Poseidon 直接围绕素数域算术设计，以更少约束换取更高证明速度，代价则是算法历史较短，需要持续密码分析。二进制域把底层数学切换到仅含 0 和 1 的最小素域，并用二进制扩域承载更大数据。位运算因此可以直接进入证明系统，SNARK 开始适配传统哈希，技术重点从设计 SNARK 友好型哈希转向设计哈希友好型 SNARK。Jim Posen 与 Benjamin Diamond 2023 年提出的 Binius 展示二进制塔域 SNARK 路径，Benedikt Bünz、Ron Rothblum 与 William Wang 的 Flock 论文于 2026 年 7 月 29 日上传 arXiv，其 M4 Max 基准为单核每秒证明 8.2 万次 BLAKE3 压缩、4.2 万次 SHA-256 压缩和 3 万次 Keccak 置换，10 核 BLAKE3 吞吐量超过 66 万次。Drake 称，笔记本电脑每秒可证明约 100 万次传统哈希调用，开销约为原生 CPU 布尔计算的 100 倍；SNARK.fast 数日前在 M3 Max 上达到每秒 180 万次 BLAKE3。2027 年 leanVM，2028 年三层部署Poseidon 被放弃的另一个关键原因是后量子安全的时间表正在加速，Project Eleven 发布的《The Quantum Threat to Blockchains - 2026 Report》指出，量子计算机的快速发展正对区块链安全构成严重威胁。一旦出现「密码学相关量子计算机」（CRQC），Shor 算法可快速破解 ECDSA（比特币及多数公链采用）和 RSA 等非对称加密，预计 Q-Day（量子破译日）可能在 2030 至 203...

5天前Foresight News#L1 #以太坊

Jeff Dean 离职前最后一次对话：我低估了AI，也看清了创业者的唯一生路

来源 | InfoQ编译 | 宇琪 策划 | Tina一年前，Google 首席科学家 Jeff Dean 在 AI Ascent 2025 峰会上预测：到 2026 年，可能出现能够全天候工作、能力接近初级软件工程师的 AI 系统。一年后，也就是 6 天前，他在一期 YC 访谈中承认，自己低估了 AI 的进展速度。模型处理复杂任务的能力，增长得远比他当时预想的更快。那么，在 Jeff Dean 看来，AI 接下来还会以多快的速度向前推进？创业公司又该如何在通用模型不断扩张能力边界的时代生存下来？今天凌晨，这期访谈又有了不同的分量。Jeff Dean 宣布，明天将是自己在 Google 的最后一天。效力 Google 27 年后，这位被誉为硅谷“程序员中的程序员”、深度参与 Google 系统架构和 AI 技术建设的传奇工程师，将与长期合作伙伴 Sanjay Ghemawat、Oriol Vinyals 和 Quoc Le 共同创立 Discovery Loop，一家聚焦机器学习、科学和工程前沿研究的公益性公司。Google 将作为创始投资方和云计算合作伙伴继续与他们合作。据 WIRED 披露，这个创业想法其实只是几周前才浮出水面。为了挽留这支核心团队，Alphabet CEO Sundar Pichai 还曾在多次会面中试图说服他们“别摘工牌”。但最终，几人还是决定离开大公司体系，去换取一家初创公司才有的乐趣、速度和自由。截图来自：https://x.com/JeffDean/status/2085035498222002595/photo/1Jeff Dean 在告别信中写道，自己见证了 Google 从一家只有 25 人的公司，成长为拥有 19 万多名员工的科技巨头。如今，Google 已有 13 款产品的用户规模超过 10 亿。从搜索、邮件、翻译和视频，到大规模计算、自动驾驶和 AI 系统，他参与建设的技术几乎贯穿了 Google 的整个发展历程。而促使他们离开的一个重要原因，也恰恰是大公司难以摆脱的惯性。正如 Oriol Vinyals 所说，在大型组织内部，要推动任何激进变化，都必须先克服层层阻力；他们想做点不一样的东西。颇有意思的是，迄今为止，这家新公司甚至还没来得及招人、租办公室，至于谁来当 CEO，团队内部短暂停顿后，大家把目光投向了 Jeff Dean——“我想应该是我吧。”他说。因此，这期发布于 Jeff Dean 离职前夕的访谈，也像是他站在职业生涯转折点上，对 AI 下一阶段的一次集中判断。节目中，他与 YC 合伙人 Diana Hu 讨论了 AI 从“模型为中心”转向“上下文工程”的范式变化、推理硬件正在出现的巨大机会，以及创业者如何在通用模型越来越强、越来越多应用可能被模型直接吞并的时代，找到真正值得坚守的生存空间。本文基于该访谈视频整理，经 InfoQ 编辑。太长不看版Q：去年你说到 2026 年会出现能力接近初级工程师的 AI。一年过去了，这个预测打脸了吗？A：相当准确，但我低估了一件事：模型处理越来越复杂任务的能力，增长速度比我预想的快得多。而且这种能力正在溢出到编码之外的领域，基于 Agent 的系统开始真正崭露头角。Q：对 2027 年的大胆预测是什么？A：深度学习系统将实现全自动的问题分解和自动化实验循环：把问题拆成子问题，自动跑实验，整合结果，得到一个改进后的系统。而且这不只适用于机器学习，任何有可衡量目标的科学和工程领域都能用上。Q：2001 年 Google 把搜索索引装进内存，...

9天前burnking#AI #Jeff Dean #谷歌

闭源AI的“思考过程”被扒开，最值钱的护城河怎么塌了？

作者：克洛德，深潮 TechFlow原标题：最新论文引爆讨论：AI 「深度思考」过程可被免费蒸馏，闭源厂商最值钱的训练资产正被搬空深潮导读：每次你向 AI 抛出问题，它都会在后台先「深度思考」一遍再开口，这段没人看见的思考过程，是 OpenAI、Anthropic 压箱底的护城河。现在，一群研究者公开了把这段思考完整扒出来的方法。被一起扒出来的，还有用户贴进去的信用卡号、密码和邮箱。这不是一篇离你很远的安全论文，而是你以后跟 AI 说话的方式，可能要变的前兆。8 月 10 日，一篇论文提交到 arXiv，第二天项目网站 stolen-thoughts.com 上线，逐条展示从多家闭源模型里取出的「思考记录」，在 Hacker News 冲上 500 分。项目负责人 Alexander Panfilov 在 X 上写道：「我们找到了一种方法，利用所有前沿 AI 公司 API 中的漏洞，提取前沿模型的隐藏推理。」换句话说：你以为只有 AI 自己知道它在想什么，其实有人能把它摊开来看。你贴进 AI 的东西，可能正跟着它的「思考」一起漏出来研究者扫描了网上公开分享的约 7000 个 AI 助手会话记录，把其中被加密的「思考过程」一一解开，然后发现了一些本不该出现的东西。Panfilov 在推文中说：「我们初步扫描了约 7000 个公开会话，发现了 62 个唯一 API 密钥、33 个邮箱地址、33 个密码，以及其他敏感信息。」更刺眼的是细节。一个航班预订任务的「思考」里，躺着完整的姓名、邮箱、护照号、出生日期，以及带安全码的信用卡号。Anthropic、AWS、GitHub 等平台的密钥也出现在案例里。也就是说，你为了让 AI 帮你干活而贴进去的凭据，会顺着它的思考过程一起被存下来，再被别人一把抓走。「如果你曾在网上分享过带加密推理块的 Claude Code 或 Codex 会话，它们都可以被解码，并泄露你的个人数据。」Panfilov 写道。对普通用户最直接的提醒就是：别再把机密往 AI 里贴了，哪怕它说「我不会外传」。厂商先说「没事」，再偷偷修这件事不是突然发生的。今年 5 月，约翰霍普金斯大学密码学教授 Matthew Green 就已经向厂商报告了类似的漏洞，当时得到的回应是「没有看到任何安全影响」。等到 Panfilov 团队正式披露，厂商的态度变了。「我们随后走了负责任披露流程，厂商已经修补了这一漏洞引发的若干问题，据我所知仍在继续。」Panfilov 说。论文也确认，披露之后，研究者已经无法再复现同样的攻击。问题在于：漏洞存在了几个月，用户并不知情。等到被公开、被讨论，修补才真正落地。这不是某一家公司的错，而是整个行业「加密即安全」的假设第一次被公开戳破。对读者而言，真正值得记住的是一句话：你信任的那家 AI 公司，可能没把全部风险告诉你。你用的模型，可能不再那么「独家」了更长的变化在产业层面。推理能力是 OpenAI、Anthropic 们定价的地基，也是它们最不愿对外示人的部分。一旦这段思考能被批量提取，竞争对手就能以极低的成本，把最强模型的「思路」喂给自己的模型去学。论文里还提到一个未经同行评审的初步观察：用少量最强模型的「思考」去引导另一个模型，会明显把后者的回答拉向前者的方向。这意味着什么？闭源模型的护城河，原本是「你用钱买不到我的脑子」。现在这道墙出现了裂缝。对使用者来说，短期谈不上坏事：更强的竞品可能更快冒头，价格也可能被压下来。但代价是，你再也分不清一个模型是真的聪明，还是抄了别人的思路。「付费却看不到的思考」到底属于谁，...

10天前burnking#AI #Anthropic #OpenAI

长鑫科技 7 月 27 日科创板上市，估值约 5800 亿元，产业链设备材料封测全环节或将受益

比推消息，国内 DRAM 存储 IDM 龙头长鑫科技将于 7 月 27 日正式在科创板挂牌上市，发行估值约 5800 亿元，募资总额 295 亿元，其中设备购置及安装费约 220.66 亿元。业内认为，2026-2027 年是国产设备导入黄金窗口期。 产业链方面，设备端北方华创刻蚀/薄膜沉积等多品类已大批量出货，中微公司刻蚀及薄膜设备持续验证并量产采购，华海清科 CMP设备已进入核心供应链，精测电子、精智达检测设备同步受益。材料端雅克科技前驱体产品覆盖先进制程需求，广钢气体配套特气及大宗气体，金宏气体现场制气订单周期达 15 年，彤程新材 ArF/KrF光刻胶已量产，晶瑞电材高纯双氧水市占率突破 40% 但高端光刻胶仍处验证阶段，德邦科技处于送样测试阶段。封测及模组端华天科技为封装供应商，江波龙、德明利已达成采购，麦捷科技仍处导入阶段。分销端商络电子已获授权，上市后供货策略调整存预期。多家上市公司通过产业基金间接持股，上峰水泥合计持有发行前约 0.1517% 股权。  

29天前

价值 1.31 亿美元 BTC 转移，持仓 6 年比特币巨鲸将 800 枚转至 Cumberland 用于 OTC

比推消息，据 Onchain Lens 监测，某持仓 6 年的比特币巨鲸转移价值 1.31 亿美元的 2000 枚 BTC，其中 800 枚 BTC 价值 5220 万美元转至 Cumberland 用于 OTC，1200 枚 BTC 价值约 7830 万美元转至新地址。最新接收钱包为 37wN4vH3FyciU521vQ4mGWqmq7cYrof4XH，新钱包为 37arJMPgqqgXHcBPdFYos8pLrX1fH4sx6R。

32天前

Binance Japan 任命新总经理

比推消息，Binance Japan 宣布于 2026 年 7 月 1 日起任命丰崎亚里纱为新总经理（代表取缔役），前任千野刚司转任名誉会长兼取缔役，继续提供战略指导。公开信显示，丰崎亚里纱毕业于美国西北大学计算机科学与经济学专业，曾在 UBS 香港任衍生品交易员、Google 日本负责搜索与 AR 业务，并于 2022 年创立 DeFi 项目 Cega 并于 2025 年出售，获福布斯 30 Under 30 认可。

53天前

Saylor的“高息神话”，摇摇欲坠

来源：The Breakdown作者：Byron Gilliam 编译及整理：BitpushNewsBitpush 注：2026 年 6 月 8 日早些时候，Strategy 宣布在比特币价格上周下跌约 15%（一度跌破 60,000 美元后反弹至 62,000 美元上方）之后，以均价 65,332 美元购入了 1,550 枚比特币，总价值约 1.01 亿美元。此次买入发生在该公司于 6 月 1 日卖出 32 枚比特币之后，是那次出售后的首次增持。至此，Strategy 的比特币总持仓升至 845,256 枚，累计买入成本接近 640 亿美元，整体平均成本约为 75,680 美元。为筹集这笔买入资金并增加现金储备，Strategy 在此期间通过发行普通股募集了 1.81 亿美元，并将其美元现金储备增加 1 亿美元，使总现金储备达到 10 亿美元。以下是正文：$STRC 的真相时刻？上一次金融危机期间被曝光的第一个银行业丑闻，是“拍卖利率证券”（Auction Rate Securities，简称 ARS）的误导销售。ARS 是一种固定收益证券，其收益率会定期通过拍卖重置为更高水平，以确保其始终以面值交易。其设计逻辑是：让收益率变动，从而价格不必变动。  不幸的是，这给投资者造成了一种印象，即 ARS 是一种高收益的现金等价物。  银行鼓励这种误解，建议零售客户将 ARS 用作短期资金存放工具，例如应对医疗开支或每周工资发放等需求。  一位瑞银客户将用于支付大学学费的 7.5 万美元积蓄投入了 ARS，她表示：“我的经纪人一直跟我说，没有比这更安全的了。”  但实际上，比她想象中安全的东西要多得多。  随着金融危机逐步发酵，仅靠收益率重置已不足以让 ARS 维持面值交易。银行便亲自入场购买 ARS，以此来维持稳定性的假象。但到了 2008 年 2 月，危机对其资产负债表的压力已经过大——它们停止购买，于是 ARS 市场彻底崩溃。  那些以为自己持有的是现金类资产、随时可以按面值卖出的投资者突然发现，自己持有的东西根本卖不出去。  监管的回应来得异常迅速。仅仅十个月后，花旗集团和瑞银就与美国证券交易委员会（SEC）就其 ARS 营销行为达成和解，同意向零售客户返还 300 亿美元。  这是一起证据确凿的银行不当行为案件。SEC 指出，两家投行“向客户错误地陈述 ARS 是安全、高流动性的投资，可与货币市场相媲美”。金融正义得到了伸张，我们也从中吸取了教训。  不幸的是，金融领域的每一个教训，最终都会被人们重新忘记。  最近，Michael Saylor 正在瞄准同一类投资者，使用类似的利率重置机制，并采用同样误导性的销售话术：$STRC 与当年的 ARS 一样，与银行账户没有任何相似之处。  宣传话术 vs. 招股说明书阅读过招股说明书的投资者知道 STRC 到底是什么。  在其中，Strategy 表示，其“当前意图”是以一种它认为能使 STRC 交易价格“保持或接近其规定的每股 100 美元”的方式来调整 STRC 的股息收益率。  但它并未作出任何承诺：“我们可随时……放弃此意图。”并警告称“STRC 股票的交易价格可能大幅波动”。  那些跳过招股说明书、只听...

74天前Wendy#Michael Saylor #Strategy #Strategy专题 #STRC #微策略 #拍卖利率证券 #比特币 #金融危机

美联储 6 月维持利率不变概率达 97.3%，7 月概率为 84.8%

比推消息，据金十报道，CME “美联储观察”显示，美联储到 6 月维持利率不变的概率为 97.3%，累计加息 25 个基点的概率为 2.7%。到 7 月，维持利率不变的概率为 84.8%，累计加息 25 个基点的概率为 14.8%，累计加息 50 个基点的概率为 0.3%。预测事件卡片        美联储 6 月维持利率不变概率大于97%        小于97%      Powered by GUESSX编辑删除分类：politics美联储 6 月维持利率不变概率大于97%小于97%## 判定规则YES：FOMC 官方宣布的六月政策声明明确将联邦基金目标区间保持不变（无加息、无降息、无目标区间调整）。NO：FOMC 官方宣布对联邦基金目标区间进行调整（加息或降息，或以其他方式改变目标区间条款）。若声明仅改变前瞻指引或经济展望但未改变利率目标，仍记为 YES。## 判定依据- 官方数据来源：美国联邦储备系统（Federal Reserve）官方网站发布的 FOMC 声明/新闻稿，及其时序信息。- 判定以官方新闻稿对“联邦基金目标区间”是否有变动为准；若官方文本有多份版本，以最终正式文本为准。- 时间以发布的官方时刻为准，统一显示为 UTC+8。## 特殊情况- 如同日发布多份文本，请以最终版官方新闻稿为准。- 若声明中仅对前瞻指引、经济展望等进行修改而明确写明“保持利率不变”，仍视为 YES。- 如紧急/非常规公告导致同日出现多次文本，请以最终官方发布版本为判定依据。## 无效条件（Invalid）- 官方来源不可核验、链接跳转失败或文本缺失的情况。- 官方文本未明确涉及联邦基金目标区间的变动（仅提及其他非利率内容），此时市场应视为待定，暂停结算。- 非官方媒体报道、推特帖文等二级来源为唯一依据的情况。## 时间规则- 结果以官方 FOMC 声明/新闻稿的发布时间为准，统一标注为 UTC+8。- 如同日存在多版本文本，以最终正式官方新闻稿的发布时间为准。politics2026-05-30T20:552026-05-30T12:55:00.000Zdata:image/png;base64,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89天前

Nous Research 发布 Lighthouse Attention，长序列预训练提速 1.4-1.7 倍

比推消息，5 月 17 日（UTC+8），Nous Research推出Lighthouse Attention方法，通过选择式层级注意力机制解决长序列预训练中注意力计算成本二次方增长的问题。该方法对Query、Key、Value进行对称池化，选择逻辑置于注意力内核外可复用FlashAttention内核，采用两阶段训练策略。实测在NVIDIA B200上，512K上下文长度下前向传播提速21倍，前向+反向联合提速17.3倍，第一阶段吞吐量达12.6万tokens/s/GPU（vs 密集SDPA的4.6万），端到端加速达1.40×至1.69×，同时保持匹配或更低的训练loss。在530M参数Llama-3风格模型上验证，三组Lighthouse运行最终loss（0.698-0.71）优于从头训练的密集SDPA基准（0.7237），节省22.5-27小时训练时间。论文arXiv:2605.06554。

97天前