ElevenLabs 通过 3 亿美元 tender 估值翻倍至 220 亿美元
ElevenLabs 宣布通过 3 亿美元的 tender offer 让员工套现部分已归属股权,估值 220 亿美元,较今年 2 月融资 5 亿美元时的 110 亿美元估值翻倍。
ElevenLabs 宣布通过 3 亿美元的 tender offer 让员工套现部分已归属股权,估值 220 亿美元,较今年 2 月融资 5 亿美元时的 110 亿美元估值翻倍。
OpenAI 在 Dev Day 上由 CEO Sam Altman 透露了新的 Decisions API,功能类似 TypeSafe AI 本月发布的决策模型 Jev,让 Luna 模型在给定选项中快速低成本地输出选择。
Google(Alphabet)发布新模型 Gemini 4 Argon,主打防御性网络安全,称其可自主发现、验证并修复关键软件漏洞,目前仅通过 Fairwind 安全计划向部分网络安全合作伙伴开放。该模型也用于编码、调试和代码库迁移等日常工程工作,并称在多项基准上显著领先 GPT-6 Astra 与 Anthropic 的 Fable 和 Opus。
由前 Google 和 SpaceX 产品经理 Rama Afullo 联合创办的 Satlyt 完成 800 万美元种子轮融资,由 Non Sibi Ventures 领投,为多公司卫星提供运行 AI 模型的软件,对标 VMware 和 Snowflake 的平台模式。
听力科技初创公司 Legato 周四宣布,其 AI 助听眼镜 Legato Frames 正式开售,起售价 999 美元,面向轻至中度听力损失成年人。该眼镜将专利助听技术集成于镜腿,AI 系统可区分人声与背景噪音,双扬声器系统在耳旁数英寸处降低 99% 漏音,重 34 克,续航 10-12 小时,支持处方镜片和蓝牙串流。
AI 创业公司 Photon 获 450 万美元种子轮融资,由 Gradient 和 A* 联合领投,Vercel、HongShan 等参投,用于开发帮助开发者在 iMessage 和 WhatsApp 等消息渠道上构建智能体的产品。


推荐理由:原文披露了贷款规模、股权转换条款和利益冲突提示,读者可以看清 Broadcom 既是供应商又是债主的交易结构。
推荐理由:原文记录了AI伪造声音与仿冒账号结合的诈骗全过程和追回结果,读者可以据此了解这类组合骗术的作案路径。
I'm 30 and essentially starting my life again from 0 Lets build together.
社交俱乐部 The Den 在新店开业之际,用 ChatGPT Work 将拨款申请的准备时间从 3 天压缩到 2 小时,酒牌材料从 4 天压缩到 3 小时,每周因此省出 10-15 小时用于业务增长。
Albertsons 正使用 ChatGPT Enterprise 和 OpenAI API 帮助团队加快工作速度,并让数百万顾客的购物体验更轻松。
OpenAI 发文探讨先进 AI 对突破性创意背后日常工作的价值,认为执行环节可能决定下一阶段经济的形态与进步速度。文章未给出具体模型、参数或评测数据,核心观点是 AI 在常规执行类任务上的作用可能比创意本身更具影响力。
研究通过将嵌入模型从 T5 扩展到 T5Gemma-1 再到 T5Gemma-2,发现更强的嵌入模型能显著提升扩散语言模型的生成性能,但 T5Gemma-2 的嵌入过于判别性,导致连续扩散采样失败。
AutoGUIWorld 是一个数据生成框架,结合图像生成器的视觉先验与规划器的任务知识,无需部署或运行软件环境即可合成 GUI 交互轨迹。它从操作系统上下文、视觉外观和界面状态的结构化规格中采样初始场景,生成 79,266 条覆盖 Ubuntu、Windows、macOS 和 Chrome 的空间标注步级训练样本。
针对多智能体工作流训练只优化生成器、其余智能体固定的问题,研究者提出 FloWright,通过分层、结构感知的奖励范式让一个角色自我进化、两个及以上角色协同进化,无需额外模型、标签或执行。
研究者提出分层连续扩散语言模型(HC-DLM),将离散 token 生成与连续隐变量轨迹耦合在单一去噪过程中,训练目标由 token 似然的变分下界推导而来。该模型以隐变量作为唯一持久生成状态,每步从中读出 token 并反馈作为下一步隐变量更新的脚手架。在 Sudoku、Countdown 和 LM1B 上,同等模型规模下 HC-DLM 在谜题准确率和生成困惑度上均优于离散与连续扩散基线。
所有付费 ChatGPT 账号将于明日 10am PST 迎来全球重置。作者为 GPT-6.1 Sol 初期体验致歉,称上线前两天遭遇大规模负载峰值导致速度偏慢,现已恢复到预期速度。
Signal65 报告测试显示,高通 18 核骁龙 X2 Elite(X2E-88-100)在 CPU、AI 和多数续航项目中领先英特尔酷睿 Ultra X7 358H。
准确 😂 (注:原文仅两个词,无实质信息,无法生成符合规则的 10-15 字标题。)
LangChain 在 Open SWE 智能体 Harness 内构建模型路由器,按任务分类选择最低成本合适模型,将编码线程中位数成本降低 64%,质量变化可忽略不计。
https://x.com/i/article/2105830283580989440
Excited to announce Volantis's $88M Series A. We are solving Al's memory bottleneck by using optics, enabling chips with huge amounts of fast & cheap memory. By boosting both the memory bandwidth and capacity per chip by orders of magnitude, we enable ultra-fast inference (up to 10,000 tps/user) for large models (>10T) - with low $/tok to boot. Initially, this will enable insanely fast agents - think coding agents that finish in minutes or even seconds instead of hours. More excitingly, optics is a fundamentally scalable way to increase memory systems. Not 2X/year, but by orders of magnitude across new generations. This will enable a structurally new Al industry, including restarting scaling laws, holding entire repos in context windows & more. Our team has pioneered many core semiconductor technologies: the 1st CoWoS product, early HBM, the 1st silicon photonics CPO systems, the 1st high volume tunable VCSELs, the 1st processors to directly communicate using light & more. We’ve already sent data >10× farther than equally tiny electrical wires inside a chip package. Our next iteration is already taped out and targets world-record bandwidth density over relevant distances, read more: https://volantissemi.ai/news-insights/our-88m-series-a-demolishing-the-memory-wall-with-photonics-post
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
Rulin Shao 等人在 arXiv:2609.37725 提出 Context Language Models(CLMs),把上下文当作文件、由模型自由更新,从而原生管理自身上下文,并可自然扩展到多智能体共享文件式上下文。
Latent Space 播客访谈 MIT 博士生 Alex Zhang,围绕其 Recursive Language Models(RLM)研究展开。
Whatever you do just get rid of that synthetic nasty sound in your music. Suno isnt going anywhere if it remains at the current quality... it needs to sound polished, mastered, not like its playing through the cheapest speakers you could buy at circuit city before they went out of business.
推荐理由:原文给出三大新模型组合在 Coding Agent Index 的得分与单任务成本对比,读者可以据此在性能和价格之间做选型权衡。
我要为 @aidotengineer nyc 换掉我的标语 提前飞过去参加 NY Comic Con。11 天后见!
it took not even 30 seconds after leaving my hotel in nyc to see something that i almost never saw in 3 months in sf: i saw a hot girl
OpenRouter 比较了 LangChain、LangGraph、CrewAI、OpenAI Agents SDK、Claude Agent SDK、Microsoft Agent Framework 和 Google ADK 如何定义工具 Schema 并在不同提供商的 wire format 之间做翻译。
推荐理由:原文逐一拆解六大框架的工具调用格式翻译位置,并给出在 API 层统一格式的可行做法,便于开发者选型前对齐自己的技术栈。
OpenRouter 发文将多模型编排拆为三层:工作流编排(LangGraph、CrewAI 负责)、模型路由和提供商路由(OpenRouter 负责)。
OpenRouter 发布教程,介绍让便宜模型先答常规支持问题、再按需升级到更强模型的路由方法。文中比较静态规则、分类器分诊和答案检查三种模式,给出带 models 回退数组的可复制请求示例,并说明请求错误由 OpenRouter 的 fallback 重试,而答案是否合规需在应用侧检查,最后列出升级率、每工单成本和全轮延迟等应测量的指标。
OpenAI 发布 GPT-6 Astra Ultrafast,运行在 NVIDIA Blackwell GPU 上,现已在 OpenAI API 及符合条件的 ChatGPT Work 和 Codex 用户中可用。
推荐理由:原文给出 Ultrafast 模式相对 Astra Standard 的加速幅度和适用场景,开发者可据此评估 coding agent 工作流的收益。
天呐 (引用推文:Linux 内核存在"多个漏洞" https://lwn.net/Articles/1097401/)
"Several vulnerabilities" in the Linux kernel https://lwn.net/Articles/1097401/
突发:Elon Musk 与战争部长 Pete Hegseth 一同参加了 Project Meridian 的首次会议。🇺🇸