跳到正文
10月21日周三
  1. Sundar Pichai42

    打开声音!

    引用Google DeepMind@GoogleDeepMind

    Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.

  2. Rohan Paul54

    Coco Labs 的 AI 设计模型 Vyb 按图层逐个元素构建设计,文字保持可编辑文本、图形保持矢量、照片保持像素,并从网站自动提取 logo、字体和配色。在 145 款 Google 字体上按像素重叠 IoU 打分,Vyb 达到 90.3%,对比中 最强图像模型为 76.0%,该测试的测量上限为 94.4%。由于每层保留原生格式,导出 Figma 时结构完整保留。

    引用Anusha Ramesh@anurahk

    I launched the first image and video generation models at Google, and kept asking why all AI designs look the same. AI has brought us the WORST era of design. It made content creation effortless, but it also made 90% of it look painfully generic. My co-founder and I spent 2 years building in stealth to change that. Meet Vyb, the first AI design model that understands your taste like a designer would. RT + comment your website URL and we’ll make your first design.

  3. Arena.ai60

    Arena 宣布 Meta 的 Muse Wonder Full (Max) 在 Agent Arena 排名第 7,净提升 +11.3%,基于超 6.5K 真实智能体会话,将 Meta 推至实验室第 4 位。

    引用Arena.ai@arena

    Exciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs! This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts). Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google). Across categories, Muse Wonder Full (Max) landed: - #4 in Data & Analytics and in Consumer Product - #5 in Brand & Marketing - #6 in Content Creation Tools - #7 in Gaming and in Simulations - #8 in Reference-Based Design Congrats to the @AIatMeta team and @alexandr_wang on this release!

  4. Rohan Paul54

    SoMark 团队发布开源文档解析模型 OXR(1.3B 参数),将 PDF 和文档图像转为含布局坐标的 Markdown 或 JSON 的 Agent-Friendly 数据。

    引用SoMark@somarkai

    Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟

  5. Mark Chen54

    OpenAI 宣布收购 Orchestra Bio,一个帮助生物医药团队协调药物开发复杂流程的平台。OpenAI 首席研究官 Mark Chen 欢迎 Orchestra Bio 团队和 @ashokaraj_ 加入。双方将基于 Rosalind 生命科学研究模型和工具整合运营,帮助新疗法更快抵达患者。

    引用OpenAI@OpenAI

    We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development. Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner. Welcome @ashokaraj_ and team! https://openai.com/index/openai-to-acquire-orchestra-bio/

  6. Rohan Paul48

    HeyGen 的 Professional Voice Clone 用 30 分钟到 2 小时的录音音频训练,保留呼吸、停顿、笑声、口音和元音发音,官方称生成的声音无人能分辨真假。推文强调这一数据量区间是关键:短参考片段无法体现长句前的换气节奏或语速加快时的口音特征,而这些细微的不规则正是听者判断声音是否属于真人的依据。

    引用HeyGen@HeyGen

    Professional Voice Clone on HeyGen sounds exactly like you. Upload 30 min to 2 hrs of audio and get a voice no one can tell apart from your own. Every breath, pause, laugh, and accent, preserved. Comment "PVC" for a free month. First 100, web subscriptions only, ends 11/19.

  7. ViggleAI46

    一个模型即可生成 3D 角色、动作和场景。 JST-2 现已在我们的 API 上线。 今天就开始构建:https://viggle.ai/developers

    引用ViggleAI@ViggleAI

    Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

  8. Rohan Paul69

    Anthropic 在科学博客发布 Claude 设计蝎子神经毒素蛋白结合剂的研究,Rohan Paul 转述称 Claude 在 Claude Science 环境中作为智能体自主完成整个蛋白设计流程:选择毒素攻击位点、驱动 12 个蛋白设计程序、自设评分阈值。

    引用Anthropic@AnthropicAI

    New on our Science Blog: designing protein binders against scorpion neurotoxins. Scorpions sting about 1.2m people every year, and antivenoms are often costly or in short supply. Claude has made an early step toward finding new treatments. https://www.anthropic.com/research/claude-scorpion-toxin-binder-design

  9. Elon Musk49

    “电力不足”的说法过于简化了。要以极快速度上线大规模算力,每个环节都很难。 如果有两倍的时间,这会相对容易,但并没有。

    引用Rihard Jarc@RihardJarc

    Former $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA): 1. According to the expert, open-source models are closing the gap with frontier models faster than expected, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration. 2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept. 3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock. 4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them. 5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow. 6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all. found on @AlphaSenseInc

  10. Rohan Paul52

    Shadeform 发布 Flex VMs 和 Shadeform Spot,把闲置的裸金属 GPU 服务器转化为可租用的云算力,数小时内即可上架。作者认为关键在于无需构建 API,只需一个 IP 和 SSH 密钥即可完成接入,这消除了闲置算力难以触达买家的集成障碍。

    引用Shadeform@shadeformai

    Some of the most in-demand GPUs on Earth are sitting idle right now, and the teams that paid for them are just eating those costs. Today, we’re launching Flex VMs + Shadeform Spot to fix that👇

  11. Aravind Srinivas58

    Perplexity CEO Aravind Srinivas 转引 Engadget 报道称,Perplexity AI 工作流现在可以在 Mac 上完全本地运行,要求配备至少 36GB 内存。

    引用Engadget@engadget

    A Mac with at least 36GB of RAM can now run Perplexity AI workflows locally. https://www.engadget.com/2291754/perplexity-makes-it-easier-to-run-ai-workflows-entirely-on-your-mac/?utm_term=Autofeed&utm_campaign=Echobox-Engadget&utm_medium=Social-Distribution&utm_source=Twitter#Echobox=1792511300

  12. Arena.ai57

    Arena 更新 Agent Arena Pareto 前沿,OpenAI 的 GPT-6 Luna 和 GPT-6.1 Sol 在不同推理档位下占据 11 个前沿位置中的 6 个,加上 GPT-6 Astra (Max) 共 7 席。

    引用Arena.ai@arena

    Update to the Agent Arena Pareto frontier: GPT-6 Luna (Max) has landed on the Agent Arena Pareto frontier with +1.59% net improvement at a median cost of just $0.05 per task! Luna costs 29% less than DeepSeek-V4.1-Flash and 76% less than Hy4 preview, while trailing their net-improvement scores by just 2.27 and 2.51 percentage points respectively. Compared to other GPT models on the Pareto frontier, GPT-6 Luna (Max) costs 94% less than GPT-6 Sol (Max), and 98% less than GPT-6 Astra (Max). Congrats to the @OpenAI team on this cost-efficient release!

  13. fal68

    fal 发布与 Black Forest Labs 合作后训练的视频模型 FLUX 3 Max,在第三方独立人类偏好评测中获 1139 Elo,与 Seedance 2.5 并列图生视频质量第一梯队。该模型约 12 秒生成一段 10 秒 720/768p 视频,未来三周降价 75%,后续还将继续后训练改进。

    引用fal@fal

    隆重推出 FLUX 3 Max,这是 fal Research 与 @bfl_ai 合作推出的最新后训练视频模型。 在独立第三方基准测试中,FLUX 3 Max 在图生视频质量上并列第一,可在约 12 秒内生成一段 10 秒、720/768p 的视频。 在接下来的三周内,FLUX 3 Max 享受 75% 折扣,使其成为同等质量水平下最实惠的前沿视频模型。

    推荐理由:原文给出第三方 Elo 分数和生成速度数据,读者可据此判断该模型在图生视频梯队中的位置。

  14. Rohan Paul51

    General Compute 宣布将 @positron_ai 作为下一个合作伙伴进行集群规模部署,硬件于 2027 年 Q1 上线,目标是让开源模型获得更快的推理。作者 Rohan Paul 解释其做法是 prefill 在 GPU 上运行、逐 token 生成交给 Positron 的内存优先硬件,对客户呈现为一个端点和一套 SLA;Positron 的芯片专注内存速度且不依赖 HBM。

    引用General Compute@general_compute

    Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner. We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands. Hardware goes live Q1 2027. Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership. LFG!

  15. Elon Musk52

    Elon Musk 转发 Grok @Bot 更新演示视频,介绍其大规模功能更新。新功能包括新聊天卡片、更快速度、草稿编辑、Email 别名、X DM、@bot 标签、Connectors、语音通话、通知、邀请与 Android Assistant,以及访问列表、未读、routines 和登录等。

    引用matt palmer@mattyp

    What's new in Grok Bot 00:00 New chat cards 00:31 Speed 00:53 Draft edits 01:08 Email aliases 01:33 X DMs 01:55 Tag @bot 02:31 Connectors 02:48 Voice calls 03:27 Notifications 03:56 Invites and Android Assistant 04:19 Access lists, unread, routines, sign-in This team ships!

  16. Aravind Srinivas65

    Perplexity CEO Aravind Srinivas 宣布,Perplexity Computer 现可在 Mac 上完全本地运行,无需云端推理,也不用 WiFi。模型和智能体都在本机运行,可操作 Mac 原生应用和本地文件,敏感数据不必发送到云端,现已在 Perplexity Mac 应用中可用。

    引用Perplexity@perplexity_ai

    Perplexity Computer 现在可以在你的 Mac 上完全本地运行。 与你的原生 Mac 应用和本地文件协同工作,运行任务时无需将敏感数据发送到云端。模型和智能体都在你的 Mac 上运行。 现已在 Perplexity Mac 应用中提供。

    推荐理由:Perplexity CEO 亲自宣布,Perplexity Computer 可完全本地运行,敏感数据无需上云,对关注隐私和离线使用的人有参考价值。