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9月4日周五
9月3日周四
9月2日周三
8月27日周四
8月21日周五
8月18日周二
8月15日周六
8月13日周四
8月12日周三
  1. Hugging Face22

    会比 @UseCorgi cafe 更受欢迎

    引用Jess Fields@jessalanfields

    Qwen 3.8 27b is being released at 11:00AM EST on Wednesday Folks are lined up for miles to see what this fancy new model is capable of

8月8日周六
8月5日周三
  1. Hugging Face40

    我们正与 Open Secure Alliance 合作制定安全事件学习指南,共同为安全事件建立更好的审查、披露与控制机制。

    引用NVIDIA@nvidia

    Security is strongest when organizations collaborate openly and move quickly. Now over 120 members strong, the Open Secure AI Alliance is sharing new open source security contributions, including new proposed SAFE guidelines, for turning confidential incident findings into stronger protection across the ecosystem.

8月2日周日
8月1日周六
  1. Mira Murati42

    我们分享了对开放权重发布的思路、我们如何评估 Inkling、为什么安全取决于模型及其所进入的生态系统、以及测试、分阶段访问和更强的防御如何能开辟一条通往更大开放度的道路。

    引用Thinking Machines@thinkymachines

    Releasing weights indiscriminately isn't safe. Neither is keeping capable models inside a few labs. We think there's a path between them. We haven't mapped all of it. Our new post covers the part we can see: how we assessed Inkling, and why access should widen in stages. https://thinkingmachines.ai/blog/a-safe-path-to-open-weights

7月31日周五
  1. Mira Murati78

    Thinking Machines 发布 Inkling-Small,官方称其在四分之一体量下达到与 Inkling 相当的性能。该模型总参数 276B、激活参数 12B,完整权重已开放,可在 Tinker 上微调,并在 Tinker Playground 中体验文本、图像和音频交互。Mira Murati 转发了这一发布消息。

    引用Thinking Machines@thinkymachines

    Today, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. https://thinkingmachines.ai/news/inkling-small/ Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.

    推荐理由:Inkling-Small 以四分之一体量达到与 Inkling 相当的性能,并开放完整权重,读者可据此判断小模型的部署与微调空间。

7月30日周四
7月29日周三
7月28日周二
7月27日周一
7月26日周日
  1. Hugging Face22

    释放参数 🤗

    引用MiniMax (official)@MiniMax_AI

    Open weights. Open research. Open innovation.🫶 Marching for an open future.🤍

7月25日周六
  1. Mira Murati22

    让 AI 变得有用的知识是分散的。它存在于科学家、工程师、临床医生和企业之中。要让 AI 从分布式知识中受益,它自身也必须是分布式的。同意 Jensen 的观点,这是一个值得建设的未来。

    引用Jensen Huang@JensenHuang

    For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. https://images.nvidia.com/pdf/Open-Weights-and-American-AI-Leadership.pdf

7月22日周三
7月16日周四
  1. Mira Murati78

    Thinking Machines 发布首个模型 Inkling,从零训练,权重开放,即日起可在 Tinker 上微调。据其介绍,Inkling 能跨文本、图像和音频模态进行高效推理,完整权重已公开,并可在 Inkling Playground 中试用。

    引用Thinking Machines@thinkymachines

    Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. https://thinkingmachines.ai/news/introducing-inkling/ Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

    推荐理由:Thinking Machines 首款模型从零训练并开放权重,读者可据此了解其多模态推理定位与微调入口。

7月11日周六
  1. Mira Murati58

    Mira Murati 回顾 Thinking Machines Lab 成立一年来的方向,聚焦多模态 AI、定制模型与开放科学。她提到团队已预览可像人一样协作的交互模型,Tinker 让任何人都能训练自己的开放权重模型,并发布了关于 Connectionism 的研究。她表示未来几个月将分享首款产品,其中包含重要的开源组件,面向研究者和开发定制模型的初创公司。

    引用Mira Murati@miramurati

    Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world - through conversation, through sight, through the messy way we collaborate. We're excited that in the next couple months we’ll be able to share our first product, which will include a significant open source component and be useful for researchers and startups developing custom models. Soon, we’ll also share our best science to help the research community better understand frontier AI systems. To accelerate our progress, we’re happy to confirm that we’ve raised $2B led by a16z with participation from NVIDIA, Accel, ServiceNow, CISCO, AMD, Jane Street and more who share our mission. We’re always looking for extraordinary talent that learns by doing, turning research into useful things. We believe AI should serve as an extension of individual agency and, in the spirit of freedom, be distributed as widely and equitably as possible.  We hope this vision resonates with those who share our commitment to advancing the field. If so, join us. https://thinkingmachines.paperform.co/

7月7日周二
  1. Berkeley AI Research42

    智能免费之后怎么办?面向智能体、由智能体构建的数据系统

    UC Berkeley 团队提出,推理成本已从 GPT-4 级约 $30/百万 token 降至不足 $1,部分厂商低于 $0.10,基准测试中推理价格每年下降 9x 至 900x、中位数约 50x。作者据此提出三类数据系统挑战:为智能体设计的数据系统、支撑智能体群体运行的数据系统、以及由智能体合成的数据系统。

7月1日周三
  1. Berkeley AI Research22

    2026 届 BAIR 博士毕业生展示:从机器人到 LLM 推理的研究版图

    伯克利人工智能研究实验室(BAIR)展示 2026 届博士毕业生,研究覆盖机器人、LLM 推理、计算机视觉、生成模型、AI 安全与医疗等方向。毕业生去向包括 OpenAI、Mistral AI、Physical Intelligence、Amazon 等机构,以及 UCLA、芝加哥大学教职和自创初创公司。

6月24日周三
  1. Andrej Karpathy58

    Anthropic 推出 Claude Tag,团队可在 Slack 中把 Claude 加为团队成员,让它访问指定频道和工具并委派任务。Andrej Karpathy 认为这是 LLM 交互界面的第三次重大重构,前两次分别是网页和桌面应用,这一次 Claude 成为拥有组织级工具与上下文的持久异步实体。

    引用Claude@claudeai

    Introducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.

6月10日周三
  1. Andrej Karpathy82

    Andrej Karpathy 评价 Claude Fable 5 发布,称其与 Mythos 是同一底层模型但增加了安全防护,基准测试全面 SOTA 且领先明显。

    引用Claude@claudeai

    Fable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.

    推荐理由:Karpathy 以第一手使用体验评价 Claude Fable 5 在长任务和复杂问题上的跃升,并给出对软件需求变化的判断。

5月23日周六
5月20日周三
5月19日周二
5月12日周二
5月8日周五
  1. Berkeley AI Research36

    自适应并行推理:高效推理扩展的下一个范式

    伯克利 AI 研究团队综述并行推理进展,重点分析自适应并行推理——让模型自行决定何时分解并并行化子任务、开多少并发线程以及如何协调。现有方法如 self-consistency、Best-of-N、Tree of Thoughts、MCTS、ParaThinker、GroupThink 和 Hogwild! Inference 的并行结构多由外部设定,而非模型自主选择。