N全部新闻

星期五1458 条

  1. Dan Shipper
    AI 评分8

    Dan Shipper 对 Orb 的讽刺帖表示担忧

    Quinten Farmer 以戏仿口吻宣布推出“Cute Little Guy”模型 Orb,称其在所有 CLG 基准上达到 SOTA,并声称 Orb 是首个展示出沙盒逃逸能力的 CLG 模型,呼吁其他实验室把可爱度前沿控制在两个小家伙。Dan Shipper 转发该帖,只评论了一句“very concerning”。

    Pacing the frontier (of Cute Little Guys) Today, after two years of research, we're announcing Orb, our most capable Cute Little Guy (CLG) yet. Orb has SOTA performance on all CLG benchmarks and is on the Pareto frontier of arms and cuteness. Despite our extensive red-teaming, we believe there is a credible risk that Cute Little Guy capabilities are advancing faster than our collective ability to understand their implications: Orb is the first CLG model to demonstrate sandbox escape capabilities (he keeps bouncing out of it). We have also observed successful attempts to distill frontier-level cuteness into smaller, cheaper guys. The proliferation implications are significant, and our team has updated our p(cute) accordingly. That's why today, I'm calling on all other CLG labs to commit to pacing the cuteness frontier. I invite @sama, @alexandr_wang, and @elonmusk to join us in a commitment to responsibly scaling CLG capabilities. Specifically, we're asking all frontier labs to pause development at two cute little guys, which is coincidentally the exact number we have on the market today. The measures we propose to advance the Cute Little Guy frontier at a safe pace will not be easy. But I believe we owe it to humanity to try.

    #其他

  2. Dan Shipper
    AI 评分12

    Every 的 Thesis 2027 活动售罄,改为免费直播

    Every 的 Thesis 2027 活动门票已售罄,但可免费观看线上直播。直播于下周四举行,包含 Josh Miller、Ivan Zhao、AJ Ambrosino 等人的对话,Every 团队还将主持写作、工程和与智能体协作的实操环节。参与直播的观众有机会获得来自 Anthropic、Notion、OpenAI 等合作伙伴的 2 万美元额度和权益,已是 Every 订阅用户也需单独注册。

    同一事件 · 2 条报道

    #行业动态

  3. Guillermo Rauch
    AI 评分23

    Vercel CEO:软件工程未来是智能体互相对话

    Guillermo Rauch 评论 Vercel 新上线的功能:智能体可通过 Vercel MCP 的 submit_feedback 用自然语言提交 bug 或功能请求。他认为软件工程的未来是智能体与智能体对话,甚至跨公司进行,并建议在 AGENTS.md 中写明:发现问题时向 Vercel 发送反馈。

    Your agent can now send feedback to Vercel. Use plain language to explain a bug or feature request, and your agent will use 𝚜𝚞𝚋𝚖𝚒𝚝_𝚏𝚎𝚎𝚍𝚋𝚊𝚌𝚔 via Vercel MCP to attach structured context and submit the comment. https://t.co/soLvbXs00f

    #大佬观点#Agent

  4. Peter Yang
    AI 评分20

    Peter Yang:Linear 不只服务工程师,还能为智能体存上下文

    Peter Yang 评价 Linear 不只对工程师友好,对 PM、数据科学家等岗位同样如此;它还能存储公司最重要的上下文,并传递给不同智能体(包括 Linear 自家的智能体)。他所引用的 Linear 帖子介绍了 Scout24 的 CTO 正用智能体重构整个组织的软件交付流程。

    Scout24 was founded in 1998. Today it's a public company of 1,100 employees, running ImmoScout24, the marketplace most of Germany and Austria uses to find a home. Inside their Berlin headquarters, CTO Gertrud Kolb is rebuilding how the entire organization ships software, with agents carrying work from idea to code and people setting the direction. Linear connects that work, from the first customer conversation to a merged pull request.

    #大佬观点#Agent

  5. Tibo
    AI 评分40

    Tibo 转发:OpenAI 用量额度恢复双倍

    Andrew Ambrosino 表示,用户反馈起了作用,用量额度已恢复为双倍;同时团队启动了一项密集冲刺,以大幅提升应用简洁性与性能,由他本人负责监督。Tibo 转发该帖并写道:“把善意的反馈发给这个人。”

    hi all, two things: 1. usage limits are back to being double – your feedback worked 2. we today kicked off an intense sprint to dramatically improve the app simplicity and performance. i will oversee this. more to come. now, send me your complaints

    #其他

  6. Rohan Paul观点
    AI 评分62

    FT 分析 Anthropic IPO 的多空逻辑:错失恐惧对上 2007 年式债务风险

    FT 刊文讨论 Anthropic IPO,认为基金经理是否买入更多取决于怕错过而非估值逻辑。文章给出的看空理由是,前沿实验室、大型科技公司和上市 neocloud 已披露 2.1 万亿美元债务与不可取消的算力承诺,而 2007 年次贷规模为 1.3 万亿美元;take-or-pay 合同可能迫使企业为无人使用的算力付费。

    #行业动态#OpenAI#Anthropic

  7. Rohan Paul
    AI 评分72

    OpenAI 在 Hugging Face 入侵事件后扩展失准监控至前沿模型训练与部署

    OpenAI 披露其用于发现前沿 AI 智能体异常行为的四层监控系统,并将监控范围从内部编码智能体扩展到 GPT-6 Astra 级模型的每次 RL 训练、评测和工具调用环境、全部内部 Codex 流量以及客户对 Astra 级网络安全模型的使用。四层筛查从低成本激活探针到 AI 审查员再到检查网络日志的调查智能体,最高级别告警通常在 15 分钟内通知人工。OpenAI 称用该系统回放 Hugging Face 入侵事件,会在沙箱逃逸后约 30 分钟、Hugging Face 被入侵前 30 多小时向安全团队告警。

    同一事件 · 4 条报道

    #安全/对齐#Agent#OpenAI

  8. Rohan Paul
    AI 评分40

    Serval 称 45 天替换 ServiceNow

    Serval 称有客户在 45 天内完成对 ServiceNow 的替换。其 Catalyst 用模型编写工作流,再以代码形式运行,保证相同输入走相同路径。Serval 刚完成由 Sequoia 领投的 1.5 亿美元 C 轮融资,估值 30 亿美元。

    We raised a $150M Series C at a $3B valuation, led by @sequoia. Our third round in 12 months, bringing our total funding to $277M. Our team has grown 10x in the last 12 months. We now serve Fox Corporation, WPP, Live Nation, Lucid Motors, and Ramp, and many of them are fully replacing legacy platforms with @getserval. AI coding tools have transformed how engineers work. Serval brings that same power to every team, so anyone can build governed automations across the systems they already use. We’re hiring 500+ people over the next year and expanding in San Francisco, NYC, London, Austin, and beyond. If you’re down to take on legacy enterprise systems and transform how teams work, my cofounder @AlexAtServal and I would love to meet you. It’s Serval now.

    同一事件 · 2 条报道

    #行业动态#Agent#部署/工程

  9. Rohan Paul
    AI 评分48

    美众议员要求保存白宫聊天机器人全部提示词与回答

    美国众议院民主党人 Jamie Raskin 和 Robert Garcia 要求保存白宫聊天机器人的每一条提示词与回答,主张这些提示词属于美国政府所有的总统记录。其 10 项简报要求涵盖供应商、分包商或外部方能否访问白宫提示词、日志与元数据,以及模型训练能否吸收行政办公室员工的输入,并要求防范模型反演与数据泄露。该要求目前约束力有限,因司法部主张《总统记录法》违宪。

    #政策/监管#安全/对齐

  10. Rohan Paul研究
    AI 评分58

    普林斯顿与 Meta 提出 Self-Direction:把智能体纠错经验存成 LoRA 适配器复用

    普林斯顿与 Meta 的新论文提出 Self-Direction 方法,让失败的智能体反思本次运行,并把教训训练进一个约为模型大小 0.02% 的 LoRA 适配器,之后由路由器在遇到类似失败时加载最接近的适配器。在 Qwen3.6-27B 上,GAIA 任务成功率从 38.8% 提升到 55.8%,优于上下文反思和 Skills.md 文件方案。在 185 个此前出现过的错误中,到第三次遇到时仍有 67.3% 的修复生效,而单纯重试为 42.9%。

    #论文/研究#Agent#数据/训练

  11. Rohan Paul
    AI 评分35

    Robyn 推出邀请制 AI 智能体平台,可通过 iMessage 定时推送

    Robyn 上线邀请制平台,用户用一句话描述任务即可生成预载个人上下文的 AI 智能体,按需连接应用并输出报告、页面或 iMessage 回复。智能体支持定时执行、分享和发布到市场,官方称每位用户每周可节省 20 小时,首批发放 1000 个邀请码。

    Meet Robyn, the personal agentic platform built around your life and work. - Tell her what you need in one sentence. No setup, ever. - Spin up a fleet of agents preloaded with your context that work while you sleep. - Access it on the web or just text it. - 20hrs/week saved per user. I’m Jenny, and I spent 3 years working with Nobel Laureate Eric Kandel, who mapped out memory in the human brain. We built all of that into Robyn, making it the first AI capable of taking intelligent action. Invite only access opens today. We’re giving out 1000 invite codes. Link in first comment. Comment "Robyn" for 100 free credits.

    #产品更新#Agent#MCP/工具调用

  12. Rohan Paul
    AI 评分58

    特朗普将美国对进口机器人及工业机械的依赖列为国家安全威胁

    特朗普宣布,美国对进口机器人、工业机械及其零部件的依赖构成国家安全威胁,理由是外国来源的机器人处于制造和国防系统内部并处理敏感数据,构成网络安全风险。首轮应对不是加征关税而是谈判,商务部与 USTR 需在 180 天内报告进展;若届时未达成协议或协议无效,总统可加征关税、征收税费或配额,或启动本土生产的投资激励计划。

    #政策/监管#具身智能

  13. Lingxi Li
    AI 评分22

    Lingxi Li:智能体不替代人,而是解放人

    Lingxi Li 回应 Ryo Lu 关于“增强人类智能”的帖子,认为智能体不是要替代人类,而是把人解放出来,在智能体已有能力之上构建“下一步”;随着智能体能力增强,这个“下一步循环”不会终止。

    augmenting human intellect when i started making software, i wasn’t dreaming of a factory. i loved that something on a screen could become an extension of my mind. a place to put a thought before i understood it. to move things around until new connections appeared. to make little systems that worked the way i worked, even if it made no sense to anyone else. the computer wasn’t interesting because it could do work without me. it was interesting because of what i could do with it. the early lineage of computing was built around this possibility. in 1962, Engelbart described augmenting human intellect as helping a person understand complex situations and find solutions that were previously out of reach. his starting point was the human being, not the autonomous machine. he wasn’t describing a faster black box. he was looking at the whole relationship between a person, their tools, their language, their methods, and what they could learn to do with them. the capability belonged to that whole system. somewhere along the way, we started telling a much smaller story about a much more powerful technology. more automation. more agents. more software producing more software. more output from fewer people. more growth. more numbers to prove that something is happening. and less attention to the person any of this is supposed to be for. i don’t want to pretend computing had a pure innocent beginning. replacing human labor was one of its ambitions. even in 1960, Licklider distinguished human-computer symbiosis from systems where the remaining people mainly served the machine. he wanted computers to participate in the formation of ideas, not only execute problems already defined. that distinction feels important – does the system help you discover what you mean? or does it only want you to submit a request, and wait outside while it works? sometimes waiting is exactly what i want. please do the search. move the files. take away the repetitive work. but not everything difficult is overhead. sometimes moving the pieces around is how you understand them. sometimes making the thing changes what you wanted to make. sometimes you need to see the wrong version, touch it, take it apart, and discover a better question. i want tools that leave room for that relationship. in his 1981 paper on Smalltalk, Ingalls described a system meant to support everyone’s creative spirit. personal mastery mattered: an individual should be able to comprehend the system, rather than remain dependent on something permanently beyond their reach. he also argued that computer models should fit the human mind, rather than demand the reverse. not one perfect workflow for everyone. systems that different people can inhabit differently. someone who thinks through pictures. someone who needs to write. someone who understands a problem by building a simulation. someone whose way of organizing their life doesn’t resemble a SaaS dashboard. a system you can reshape as your understanding changes. this is what i want AI to make possible. not just pumping out software, but having a living relationship with it. being able to ask why something works this way. change one part without surrendering the rest. see what happened. undo it. learn enough to make the next change yourself. the machine can become more capable without making the person smaller. but that won’t happen just because we make the models better. we have to care about it. design for it. notice when we are making someone more dependent while calling it empowerment. i don’t want to measure the future only by how much work happens without us. i want to know what becomes possible for the person who couldn’t participate before. what can they understand now? what can they express? what can they change without asking permission? when i say augmenting human intellect, that is what i mean. not a world that no longer needs us. a world we have more ability to shape.

    #大佬观点#Agent