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10月21日周三
  1. 钛媒体58

    智能体手机加速爆发:换机困境、端侧AI与下一代入口之争

    硅谷101梳理了智能体手机在2026年加速落地的三股推动力:换机周期拉长、端侧AI算力成熟与端云协同架构成型。文章以努比亚NaviX Ultra搭载豆包手机助手和骁龙旗舰平台为例,实测了长按AI键跨App操作、全局读屏与记忆能力,并对比苹果、谷歌、OpenAI在系统级Agent与超级入口上的不同路线。

  2. 钛媒体38

    数字造星时代:流水线式AI艺人如何变现,方桃子一条广告报价25万元

    2026年AI艺人行业已跑出完整盈利链条,AI原生艺人方桃子凭《被裁掉的女孩》单平台播放破两亿、抖音粉丝55.8万,60秒以上广告报价25.8万元。芒果TV于10月12日官宣程渡等五位原生AI艺人出道并上线《了不起啊朋友》,耀客传媒则早在3月签约秦凌岳、林汐颜。但方桃子美瞳广告因"AI无真实眼睛"翻车下架,林汐颜账号五月起停更,观众为何为AI脸买单仍无答案。

  3. 钛媒体18

    短剧正在诞生一种新的职业剧:专业知识成了主角的“金手指”

    短剧领域正出现一种新的职业剧形态,以《急诊摸鱼王》《冒姓琅琊》为代表,把专业知识和专业能力变成主角的“金手指”,而非依赖财富、权力反转。医疗短剧《急诊摸鱼王》呈现急诊分诊、创伤初步评估等专业细节,评论区有医学专业人士用术语参与讨论。这类创作强调专业打底,让观众在快节奏叙事中了解另一个行业。

  4. 雷锋网35

    一次抓取背后:Token经济学如何计量算力、模型与语料

    截至2026年6月,我国日均词元调用量已突破500万亿,Token正成为连接模型服务、推理算力与数据供给的计量接口。DeepSeek自8月起探索峰谷时段定价,9月10日调整Flash系列价格,闲时每百万词元缓存命中输入价0.02元。无问芯穹跨集群异构推理架构在特定测试中首词元等待时间降低51.5%、单词元成本降低37.5%。

  5. 钛媒体58

    这个双11,大模型攻入电商腹地

    听筒Tech 观察今年双11,豆包、千问等大模型直接进入电商场景:豆包设“双十一问豆包”导航栏并发放消费券,千问办公在双11前夕上线面向商家的电商版 Agent,淘宝站内 AI 助手接入千问。文章认为,消费者从“搜什么买什么”转向“买什么问 AI”,大模型正把搜索、推荐、比价、下单串成对话闭环,但 AI 推荐中的软性商业推广仍处监管灰色地带。

  6. 钛媒体25

    对话经济学家史晋川:从2026诺贝尔经济学奖看如何让消费者"花钱"

    2026年诺贝尔经济学奖授予罗伯特·霍尔与迈克尔·伍德福德,表彰其为宏观经济稳定化奠定微观基础。浙大教授史晋川在对话中表示,提振消费不能只靠刺激传统需求,中国居民消费结构正从"住行"转向服务型消费,需通过改革放开养老、游戏等服务领域,让产业满足需求结构转变。

  7. 钛媒体58

    国产AI日韩剧走红,头部爆款《漂亮的恶意》变现数百万元

    Tech星球报道国产AI韩剧、日剧、美剧在抖音、小红书走红,制作团队均为国内创作者。AI韩剧《漂亮的恶意》上线6集播放量破1.8亿,抖音账号涨粉170多万,变现数百万元,单集制作成本约1.5万至2万元。2026年上半年抖音新上线AI短剧约22.19万部,播放破亿约1055部,破亿率约0.47%。

  8. Latent Space44

    SF Compute CEO Evan Conrad 谈可编程数据中心与 GPU 算力交易市场

    SF Compute 正把 GPU 算力做成可交易市场,并自建云运营能力实现"物理交割",让买家拿到可用算力而非现金结算。CEO Evan Conrad 称 NVIDIA 因缺乏中立性无法自建这类交易所,而 SF Compute 通过匹配不同约束条件的订单在技术快速迭代期维持交易。他还提到可动态切分 InfiniBand 网络等让基础设施可编程的工程细节。

10月20日周二
  1. Ars Technica · AI35

    AI 游戏克隆令独立开发者不敢展示作品

    AI 游戏克隆正让独立开发者不敢公开作品,一款被推迟的 Steam Next Fest 试玩版成为这一问题加剧的信号。开发者担心自己的创意被 AI 快速复制,从而在展示阶段就失去竞争优势。

10月19日周一
  1. 极客公园30

    端侧 AI 商业化难在哪?麒麟软件、后摩智能等五位一线操盘手讲透了

    后摩智能 10 月 14 日在北京举办生态合作伙伴大会,圆桌环节五位来自操作系统、应用、数据工程、整机集成和芯片领域的从业者讨论端侧 AI 如何成为一门生意。麒麟软件姚翎称涉密场景下本地安全没有商量余地,罗格数据刘学鹏认为企业要把 AI 数字员工招进内部而非放在云端。后摩智能陈仲铭表示正通过 M 系列做大算力和先进封装做大带宽,突破算力、带宽、功耗的「不可能三角」。

10月13日周二
  1. MIT Technology Review · AI40

    开放模型利与弊:Anthropic 称 GLM-5.3 构成重大网络攻击风险,开放与闭源之争何去何从

    Anthropic 发布报告称,Z.ai 于 8 月发布的开放权重模型 GLM-5.3 可被任何人自由下载和修改,构成重大网络攻击风险。开放模型支持者认为其可本地运行、成本更低、便于学术研究并防止权力集中,而 Artificial Analysis LLM 排行榜前八名目前仍均为闭源模型。

  2. Emad30

    过去几周游戏 x AI 能力的阶跃式提升是史上最大的 未来一年将发生巨大变化 当然,最好的游戏仍将由充满热情的团队打造

    引用Tim Sweeney@TimSweeneyEpic

    We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn't to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.

10月12日周一
  1. Andrej Karpathy60

    Andrej Karpathy 重提 4 月的一条推文,认为公众对 LLM 能力的认知差距正在扩大,已从两群人各说各话变成一条陡峭的漏斗。他估算约 6B 人几乎没接触过 LLM,1-2B 人只用免费版聊天产品,约 20M 人在数学与编程中专业使用前沿模型,而约 5,000 人能在内部接触前沿系统。

    引用Andrej Karpathy@karpathy

    Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

8月15日周六