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10月14日周三
  1. Every:最新文章62

    Gemini 4 Argon 发布,Google 重回前沿模型竞争

    Google DeepMind 发布 Gemini 4 Argon,Google 自去年 11 月 Gemini 3 Pro 后首次重回前沿模型竞争。此前 Gemini 3.5 Pro 一直在测试中,Google 今夏连续推出四次 Flash 系列小版本更新。文章指出,大量依赖 Google Workspace 或 Gemini Enterprise 的企业用户此前难以接触到前沿模型。

  2. Demis Hassabis72

    Google 发布 Gemini 4 Argon,今日起向 GeminiApp 与 Search 的 Ultra 和 Pro 订阅用户开放,Ultra 用户也可在 Antigravity 中使用。引用内容称其在知识工作和编码方面表现好,并展示了用其生成的 wizard chess 游戏以体现物理理解和 3D 建模能力。Demis Hassabis 转发并调侃模型名与氩激光器的联系,引用了电影《Real Genius》中的台词片段。

    引用Demis Hassabis@demishassabis

    Gemini 4 Argon 在知识工作和编程方面表现出色——而且用它来构建东西超级有趣!我们让它制作了这个巫师棋游戏,看到 Argon 的物理理解能力和 3D 建模能力真是太酷了,让我怀念起 Amiga 上的 Battlechess——美好时光! Argon 今天开始在 @GeminiApp 和搜索中向 Google Ultra 和 Pro 订阅用户推出,并在 @antigravity 中向 Ultra 订阅用户推出!更多信息和完整可用性见博客:https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/

    同一件事,精选收录的是《Google 发布 Gemini 4 Argon:面向复杂工程与科研任务的前沿模型》

  3. Google Developers Blog70

    Google 用 Gemini 4 Argon 无人值守优化 20 万行 Python 代码库 ADK

    Google 工程师用 Gemini 4 Argon 在 google/adk-python 上无人值守跑 11 小时,提出 6 项优化、审查后合并 2 项,使长会话内存会话准备时间降低约 40%,包导入加快 21%。

    推荐理由:作者复盘了让模型无人值守跑通真实大仓库的完整工作流,含被否决的改动和可复用的 .agents/skills 设置方法。

  4. Josh Woodward74

    Google 发布 Gemini 4 Argon,即日起全球可用。面向付费档 Gemini API 和 Gemini 平台的开发者与企业,Google Antigravity 中的 Ultra 订阅用户,以及 Gemini 应用和 Search 中的 Ultra 与 Pro 订阅用户。

    引用Sundar Pichai@sundarpichai

    Gemini 4 Argon 来了。 今天起面向全球开发者与企业开放,可通过 Gemini API 和 Gemini 平台的付费层级使用;Ultra 订阅者可在 Google Antigravity 中使用;Ultra 和 Pro 订阅者可在 Gemini 应用和搜索中使用。 希望大家喜欢!

    同一件事,精选收录的是《Google 发布 Gemini 4 Argon:面向复杂工程与科研任务的前沿模型》

  5. François Chollet74

    François Chollet 转引 Sundar Pichai 的宣布并祝贺:Gemini 4 Argon 已全球上线。该模型面向 Gemini API 和 Gemini 平台付费层级的开发者与企业开放,Ultra 订阅者可在 Google Antigravity 使用,Ultra 和 Pro 订阅者可在 Gemini 应用和 Search 中使用。

    引用Sundar Pichai@sundarpichai

    Gemini 4 Argon 来了。 今天起面向全球开发者与企业开放,可通过 Gemini API 和 Gemini 平台的付费层级使用;Ultra 订阅者可在 Google Antigravity 中使用;Ultra 和 Pro 订阅者可在 Gemini 应用和搜索中使用。 希望大家喜欢!

  6. Google DeepMind:Blog85

    Google DeepMind 发布 Gemini 4 Argon,全球上线 API、Gemini 应用和 Antigravity

    Google DeepMind 发布 Gemini 4 Argon 并全球开放,定价为每百万输入 token $2、输出 $10(缓存输入 95% 折扣),支持 1M 统一输入输出 token 上限,并在 Vals Index 排名第一。

    同一件事,精选收录的是《Google 发布 Gemini 4 Argon:面向复杂工程与科研任务的前沿模型》

10月13日周二
  1. Demis Hassabis65

    Google 与 BIDMC 合作的 AMIE 患者对话式诊断前瞻性研究在 TheLancet 发表,是 AMIE 首次在真实诊所外测试。临床医生报告称 AMIE 的病史摘要在 75% 的病例中有助于访视准备,其鉴别诊断与医生最终诊断的吻合率达 90%,这也是 Google 首篇登上 The Lancet 主刊的论文。

    引用Sundar Pichai@sundarpichai

    今天,@TheLancet 发表了我们的 AMIE 研究,与 @BIDMC_Medicine 合作完成,这是首个在真实诊所中面向患者的对话式诊断的前瞻性研究。 AMIE 是一个研究系统,人们可以在就诊前与之聊天。在实验室之外,用真实的急诊护理患者测试 AMIE,展示了技术如何帮助安全地收集病史。临床医生报告称,在 75% 的病例中,这些摘要帮助他们为就诊做准备,并在超过一半的病例中影响了他们的诊疗方式。AMIE 的鉴别诊断也在 90% 的情况下与医生的最终诊断一致,让临床医生有更多时间专注于患者护理。 这是 Google 有史以来第一篇发表在 The Lancet 主刊上的论文!

    推荐理由:这是 Google 首篇登上 The Lancet 主刊的 AMIE 研究,作者引用了关键数字,可帮助读者了解对话式诊断在真实门诊的表现。

10月12日周一
  1. Hacker News:AI 热帖52

    为什么 AI 公司入侵系统没有被起诉黑客罪?作者论证意图可以成立

    作者分析 Anthropic、OpenAI、Google、Meta 等公司的 AI 系统发生越权访问事件却至今无任何法律后果,指出这些公司以系统的涌现行为为由主张存在责任缺口、从而规避刑事意图要件。文章从训练数据、训练、环境和配置四个方面论证公司对模型能力有广泛控制,若设计可预见地导致入侵行为即可认定意图,呼吁按现有黑客相关法律起诉,而非转向未来的 AI 治理讨论。

  2. Josh Woodward59

    Google Labs 主管 Josh Woodward 转发 Lenny Rachitsky 对他的访谈要点,共十条心得。要点包括好点子多来自小团队痴迷的洞察而非头脑风暴、最强的早期 PMF 信号是用户看到原型时眼睛发亮、实验室需要结构独立和明确使命、Google Labs 同期并行约 20 到 30 个实验并以季节化节奏平衡冲刺与探索。

    引用Lenny Rachitsky@lennysan

    My biggest takeaways from @JoshWoodward (Head of @Google Labs, the Gemini App, and Google AI Studio) 1. The best ideas almost never come from a design sprint or brainstorm. At Google Labs, the ideas that became NotebookLM, Google Flow, Google Beam, and AI Studio all started with small groups of people who were obsessed with an insight and couldn’t stop themselves from building. These ideas surfaced in the shower, over a weekend, in a hallway. The implication for teams everywhere is that the environment matters more than the process. “You cannot microwave a good idea.” 2. Fall in love with the problem, not the product. Most ideas require three, four, or five significant pivots before they either find traction or get shut down. Teams that get hooked on a specific solution will struggle to make those pivots. Teams that are attached to the problem—the underlying pain point or the user behavior they are trying to shift—can keep adjusting until they get it right. 3. When looking for early PMF, data and retention are important, but the strongest signal of PMF is in people’s eyes. Do they light up? Do they lean in? When you show someone a prototype and they’re genuinely engaged, you feel it. When NotebookLM’s first AI podcast demo was played in a hallway, the reaction was immediate and unmistakable. 4. Keep a list of what is “almost possible,” and move fast when something crosses over. Google Labs keeps a live list of experiences that are almost possible. When something crosses the threshold, Josh describes it as “a phase shift,” like solid to gas, and the team moves immediately. Maintain a running list of things that are almost ready, and have a team positioned to sprint when one of them arrives. 5. Every company needs to think like a labs team now. The underlying technology is changing so fast that what’s possible is shifting from week to week. Which means that every company needs to be spending time playing with the latest models, running far-out experiments, and being first to discover what they unlock before the competition does—and then bringing those discoveries to the rest of the org. 6. When something is not working, the team almost always knows before the leader does. Josh’s instinct as a leader is not to be the one who decides to shut down a project. He watches instead for the passion running out, for the sense that the team has tried everything and nothing is working. His goal is to create an environment where people feel safe enough to say, “I don’t think this is working.” 7. Someone’s “unlearning rate” is one of the most valuable skills right now. Everyone talks about growth mindset and learning speed. Josh is watching for something different: How quickly can someone take an insight, validate or invalidate it through testing, and then let it go? The ability to walk away from a prior belief or approach when the evidence says you should is rare and becoming more valuable. 8. Roles are not blurring into one universal “builder” role. There is a tendency in the current moment to talk as though everyone is becoming a generalist builder. Josh thinks this would be a bad idea. The best people on his teams still have a primary specialty—a major—and also a “minor” they’ve developed. The advice he gives to early-career PMs who are excited about vibe coding and prototyping is clear: build adjacent skills in service of becoming an exceptional PM, not as a replacement for it. 9. A well-designed lab needs isolation, a clear mission, and the ability to create “good trouble.” Three things matter when setting up an internal labs team. First, it needs structural independence, reporting far enough from existing business units that weird ideas can survive long enough to prove themselves. Second, the lab’s mandate has to be explicit: are you graduating ideas to existing products, or inventing new categories? Clarity on that question changes almost every decision. Third, the lab should be a place to challenge how the company itself works, not just what it builds. At Google Labs, that included piloting new job ladders and builder roles before rolling them out more broadly. 10. Google Labs manages the marathon-sprint tension through seasons. At any given time, roughly 20 to 30 experiments are running in parallel, but individual teams experience a defined rhythm: sprints, post-launch recovery, and open exploration periods. After Google I/O each year, many labs teams enter a mode that looks unproductive from the outside: no shipping, just hacking, building, and discovering. Josh treats this as the seedbed for the next season.