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咚咚锵123
咚咚锵123
·
2025-09-12
搞不懂在说什么
阿里DeepSeek时刻！开源新架构模型：推理快10倍、成本暴降90%
阿里巴巴开源Qwen3-Next-80B-A3B新架构模型，融合门控DeltaNet和门控注意力的混合架构，训练成本较Qwen3-32B暴降90%，推理效率提升10倍，在超长文本32K以上场景表现尤佳。性能上，指令微调版本媲美旗舰Qwen3-235B，思考模型超越谷歌Gemini-2.5-Flash，成为最强低能耗开源模型之一。
阿里DeepSeek时刻！开源新架构模型：推理快10倍、成本暴降90%
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咚咚锵123
咚咚锵123
·
2025-09-03
哪儿有70%？今年最低价格100元左右，现在也不到40%的涨幅
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咚咚锵123
咚咚锵123
·
2025-03-15
星舰是特斯拉的业务么？
马斯克：星舰明年年底前携带Optimus前往火星，人类登陆火星最早可能在2029年开始
3月15日，马斯克表示，星舰将于明年年底前前往火星，届时还将携带特斯拉人形机器人擎天柱（Optimus）。如果携带着擎天柱的星舰在火星上的着陆进展顺利，人类登陆火星最早可能在2029年开始，不过2031年的可能性更大。
马斯克：星舰明年年底前携带Optimus前往火星，人类登陆火星最早可能在2029年开始
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咚咚锵123
咚咚锵123
·
2025-02-28
价格17.5 ？哪儿来的这个价格？
阿里巴巴：于2月26日在纽约证券交易所购回57.46万股股份
格隆汇2月27日｜阿里巴巴(BABA.US)公告，公司于2025年2月26日在纽约证券交易所购回57.46万股股份，购回价格为每股17.5美元，最低购回价为每股17.29美元，总金额为999.79万美元。购回股份的授权决议于2024年8月22日获通过，购回股份拟用于注销。此次购回占购回授权通过当日已发行股份的1.46%。 海量资讯、精准解读，尽在新浪财经APP
阿里巴巴：于2月26日在纽约证券交易所购回57.46万股股份
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咚咚锵123
咚咚锵123
·
2025-02-26
这几年分红回购很多的
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咚咚锵123
咚咚锵123
·
2025-02-24
前天还180，今天又200了？这些分析师是不是吸毒了？
非常抱歉，此主贴已删除
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咚咚锵123
咚咚锵123
·
2023-11-22
他是华人么？
非常抱歉，此主贴已删除
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咚咚锵123
咚咚锵123
·
2023-09-11
很突然，有隐情？
非常抱歉，此主贴已删除
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08:35","market":"hk","language":"zh","title":"阿里DeepSeek时刻！开源新架构模型：推理快10倍、成本暴降90%","url":"https://stock-news.laohu8.com/highlight/detail?id=2566948680","media":"AIGC开放社区","summary":"阿里巴巴开源Qwen3-Next-80B-A3B新架构模型，融合门控DeltaNet和门控注意力的混合架构，训练成本较Qwen3-32B暴降90%，推理效率提升10倍，在超长文本32K以上场景表现尤佳。性能上，指令微调版本媲美旗舰Qwen3-235B，思考模型超越谷歌Gemini-2.5-Flash，成为最强低能耗开源模型之一。","content":"<html><head></head><body><blockquote><p>阿里巴巴开源Qwen3-Next-80B-A3B新架构模型，融合门控DeltaNet和门控注意力的混合架构，训练成本较Qwen3-32B暴降90%，推理效率提升10倍，在超长文本32K以上场景表现尤佳。性能上，指令微调版本媲美旗舰Qwen3-235B，思考模型超越谷歌Gemini-2.5-Flash，成为最强低能耗开源模型之一。</p></blockquote><p style=\"text-align: justify;\">今天凌晨2点，阿里巴巴开源了新架构模型Qwen3-Next-80B-A3B，对混合注意力机制、高稀疏性MoE、训练方法等进行了大幅度创新，迎来了自己的DeepSeek时刻。</p><p style=\"text-align: justify;\">Qwen3-Next是一个混合专家模型总参数800亿，仅激活30亿，训练成本较Qwen3-32B暴降90%，推理效率却提升10倍，尤其是在超长文本32K以上的提示场景中。</p><p style=\"text-align: justify;\">性能方面，Qwen3-Next的指令微调模型在推理与长上下文任务中，可媲美阿里的旗舰模型Qwen3-235B；思考模型则超过了谷歌最新的Gemini-2.5-Flash思考模型，成为目前最强低能耗开源模型之一。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/3e1195a7f92abd4c7b2f8aecce07bc5a\" tg-width=\"865\" tg-height=\"316\"/></p><p style=\"text-align: justify;\">在线体验：https://chat.qwen.ai/</p><p style=\"text-align: justify;\">开源地址：https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d</p><p style=\"text-align: justify;\">https://modelscope.cn/collections/Qwen3-Next-c314f23bd0264a</p><p style=\"text-align: justify;\">阿里API：https://www.alibabacloud.com/help/en/model-studio/models#c5414da58bjgj</p><p style=\"text-align: justify;\">网友对阿里新模型的架构非常赞赏，表示，半年前我才刚跟联合创始人聊过类似这样的架构！当时好像把它叫做 “动态权重注意力” 之类的，具体名字记不太清了。这设计真的太出色了！</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/a2c0dde2edca64e3ff02338d600871b8\" tg-width=\"865\" tg-height=\"252\"/></p><p style=\"text-align: justify;\">昨天我测试了好几款模型：思维模式下的 ChatGPT-5、Claude-4，还有专家模式下的 Grok-4。刚刚又测了Qwen3 Next。在所有这些模型里，只有你们这款模型第一次尝试就给了我正确答案。真的太出色了！</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/01c463b2636d8b94e644c8dcc5543634\" tg-width=\"865\" tg-height=\"230\"/></p><p style=\"text-align: justify;\">未来以来，这个模型击败了谷歌的Gemini-2.5-Flash。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/82eb25aba26a0f3f56c713edaedf46c0\" tg-width=\"865\" tg-height=\"206\"/></p><p style=\"text-align: justify;\">在这里看到 DeltaNet的应用，真的有点让人惊喜！我很好奇，如果换成模型架构发现的AlphaGo 时刻这篇论文中提出的模型架构，这款模型的性能会发生怎样的变化？</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/8716669e82ece67859edf9e0e0b60168\" tg-width=\"865\" tg-height=\"239\"/></p><p style=\"text-align: justify;\">800 亿参数、超高稀疏性再加上多token预测，这配置太惊艳了！要是你的 GPU 有足够显存，用它跑起来速度绝对飞快。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/6b48dd54ac6114016d90c344e4463f80\" tg-width=\"865\" tg-height=\"176\"/></p><p style=\"text-align: justify;\">基本上老外对阿里的创新模型非常满意，赞美超多。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/297670e7bad94b6a4eff5411fb13eef2\" tg-width=\"865\" tg-height=\"996\"/></p><p style=\"text-align: justify;\">Qwen3-Next架构简单介绍</p><p style=\"text-align: justify;\">阿里认为上下文长度扩展与总参数扩展是大模型未来发展的两大核心趋势，为在长上下文和大参数场景下进一步提升训练与推理效率，他们设计了全新的模型架构Qwen3-Next。</p><p style=\"text-align: justify;\">相较于Qwen3的MoE结构，Qwen3-Next进行了多项关键改进，包括混合注意力机制、高稀疏性MoE结构、利于训练稳定性的优化手段，以及可实现更快推理的多token预测机制。</p><p style=\"text-align: justify;\">在核心特性方面，Qwen3-Next采用门控DeltaNet+门控注意力的混合创新架构。线性注意力虽能打破标准注意力的二次复杂度，更适合长上下文处理，但仅用线性注意力或标准注意力均有局限。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/caf800847584cbb841385701513546a8\" tg-width=\"865\" tg-height=\"669\"/></p><p style=\"text-align: justify;\">线性注意力速度快但召回能力弱，标准注意力推理时成本高、速度慢。经系统实验验证，门控DeltaNet的上下文学习能力优于滑动窗口注意力、Mamba2等常用方法，将其与标准注意力按3:1比例，75%层用门控DeltaNet，25%层保留标准注意力结合，模型性能持续超越单一架构，实现性能与效率的双重提升。</p><p style=\"text-align: justify;\">标准注意力层还进行了多项增强，如采用此前研究中的输出门控机制以减少注意力低秩问题、将每个注意力头的维度从128提升至256、仅对前25%位置维度应用旋转位置编码以改善长序列外推能力。</p><p style=\"text-align: justify;\">稀疏性设计上，Qwen3-Next采用超高稀疏性MoE结构，800亿总参数在每步推理中仅激活约30亿，占比3.7%。实验表明，在全局负载均衡的前提下，固定激活专家数量并增加专家总参数，能稳步降低训练损失。与Qwen3的MoE相比，Qwen3-Next将总专家数扩展至512个，结合10个路由专家+1个共享专家的设计，在不影响性能的同时最大化资源利用率。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/77da51d760e41d13e613c24772e61e65\" tg-width=\"865\" tg-height=\"922\"/></p><p style=\"text-align: justify;\">训练稳定性优化方面，注意力输出门控机制有效解决了注意力Sink、大规模激活等问题，保障模型数值稳定性；针对Qwen3中QK-Norm存在的部分层归一化权重异常增大问题，Qwen3-Next采用零中心RMSNorm，并对归一化权重施加权重衰减以防止无界增长；初始化时对MoE路由器参数进行归一化，确保训练初期每个专家都能被无偏选择，减少随机初始化带来的噪声。这些设计提升了小规模实验的可靠性，保障大规模训练平稳进行。</p><p style=\"text-align: justify;\">多token预测机制也是Qwen3-Next的亮点，其原生引入的多token预测（MTP）机制，不仅为投机解码提供高接受率的MTP模块，还能提升模型整体性能，同时针对MTP的多步推理性能进行优化，通过保持训练与推理一致性的多步训练，进一步提高实际场景中投机解码的接受率。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/5d434a1876d13581c15d4658a7125a82\" tg-width=\"865\" tg-height=\"519\"/></p><p style=\"text-align: justify;\">预训练阶段，Qwen3-Next展现出卓越的效率。其训练数据来自Qwen3的36T token预训练语料中均匀采样的15T token子集，GPU时长不足Qwen3-30-3B的80%，计算成本仅为Qwen3-32B的9.3%，却能实现更优性能。推理速度上，填充阶段4K上下文长度时吞吐量接近Qwen3-32B的7倍，32K以上时超10倍；</p><p style=\"text-align: justify;\">解码阶段4K上下文长度时吞吐量接近Qwen3-32B的4倍，32K以上时仍保持超10倍的速度优势。性能表现上，Qwen3-Next-80B-A3B-Base仅激活Qwen3-32B-Base非嵌入参数的1/10，却在多数基准测试中性能更优，且显著超过Qwen3-30B-A3B。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/5c247e4f3ea45e8d086a2a4d3ee5e149\" tg-width=\"1024\" tg-height=\"632\"/></p><p style=\"text-align: justify;\">后训练阶段的性能同样亮眼。指令模型Qwen3-Next-80B-A3B-Instruct大幅超越Qwen3-30B-A3B-Instruct-2507和Qwen3-32B-Non-thinking，性能接近旗舰模型Qwen3-235B-A22B-Instruct-2507；在RULER基准测试中，该模型在各长度下均优于注意力层更多的Qwen3-30B-A3B-Instruct-2507，且在256K上下文内击败总层数更多的Qwen3-235B-A22B-Instruct-2507，印证了混合架构在长上下文任务中的优势。</p><p style=\"text-align: justify;\">推理模型Qwen3-Next-80B-A3B-Thinking性能超过Qwen3-30B-A3B-Thinking-2507、Qwen3-32B-Thinking等更高成本模型，多个基准测试击败Gemini-2.5-Flash-Thinking，关键指标接近Qwen3-235B-A22B-Thinking-2507。</p></body></html>","source":"lsy1680749794970","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>阿里DeepSeek时刻！开源新架构模型：推理快10倍、成本暴降90%</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; 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margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n阿里DeepSeek时刻！开源新架构模型：推理快10倍、成本暴降90%\n</h2>\n\n<h4 class=\"meta\">\n\n\n2025-09-12 08:35 北京时间 <a href=https://mp.weixin.qq.com/s/H2-7AsvsO8HAJzPXBw5Qbg><strong>AIGC开放社区</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>阿里巴巴开源Qwen3-Next-80B-A3B新架构模型，融合门控DeltaNet和门控注意力的混合架构，训练成本较Qwen3-32B暴降90%，推理效率提升10倍，在超长文本32K以上场景表现尤佳。性能上，指令微调版本媲美旗舰Qwen3-235B，思考模型超越谷歌Gemini-2.5-Flash，成为最强低能耗开源模型之一。今天凌晨2点，阿里巴巴开源了新架构模型Qwen3-Next-80B-...</p>\n\n<a href=\"https://mp.weixin.qq.com/s/H2-7AsvsO8HAJzPXBw5Qbg\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/66a54173e70f4b676e346f3e3ec2f8ea","relate_stocks":{"BABA":"阿里巴巴","09988":"阿里巴巴-W"},"source_url":"https://mp.weixin.qq.com/s/H2-7AsvsO8HAJzPXBw5Qbg","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2566948680","content_text":"阿里巴巴开源Qwen3-Next-80B-A3B新架构模型，融合门控DeltaNet和门控注意力的混合架构，训练成本较Qwen3-32B暴降90%，推理效率提升10倍，在超长文本32K以上场景表现尤佳。性能上，指令微调版本媲美旗舰Qwen3-235B，思考模型超越谷歌Gemini-2.5-Flash，成为最强低能耗开源模型之一。今天凌晨2点，阿里巴巴开源了新架构模型Qwen3-Next-80B-A3B，对混合注意力机制、高稀疏性MoE、训练方法等进行了大幅度创新，迎来了自己的DeepSeek时刻。Qwen3-Next是一个混合专家模型总参数800亿，仅激活30亿，训练成本较Qwen3-32B暴降90%，推理效率却提升10倍，尤其是在超长文本32K以上的提示场景中。性能方面，Qwen3-Next的指令微调模型在推理与长上下文任务中，可媲美阿里的旗舰模型Qwen3-235B；思考模型则超过了谷歌最新的Gemini-2.5-Flash思考模型，成为目前最强低能耗开源模型之一。在线体验：https://chat.qwen.ai/开源地址：https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9dhttps://modelscope.cn/collections/Qwen3-Next-c314f23bd0264a阿里API：https://www.alibabacloud.com/help/en/model-studio/models#c5414da58bjgj网友对阿里新模型的架构非常赞赏，表示，半年前我才刚跟联合创始人聊过类似这样的架构！当时好像把它叫做 “动态权重注意力” 之类的，具体名字记不太清了。这设计真的太出色了！昨天我测试了好几款模型：思维模式下的 ChatGPT-5、Claude-4，还有专家模式下的 Grok-4。刚刚又测了Qwen3 Next。在所有这些模型里，只有你们这款模型第一次尝试就给了我正确答案。真的太出色了！未来以来，这个模型击败了谷歌的Gemini-2.5-Flash。在这里看到 DeltaNet的应用，真的有点让人惊喜！我很好奇，如果换成模型架构发现的AlphaGo 时刻这篇论文中提出的模型架构，这款模型的性能会发生怎样的变化？800 亿参数、超高稀疏性再加上多token预测，这配置太惊艳了！要是你的 GPU 有足够显存，用它跑起来速度绝对飞快。基本上老外对阿里的创新模型非常满意，赞美超多。Qwen3-Next架构简单介绍阿里认为上下文长度扩展与总参数扩展是大模型未来发展的两大核心趋势，为在长上下文和大参数场景下进一步提升训练与推理效率，他们设计了全新的模型架构Qwen3-Next。相较于Qwen3的MoE结构，Qwen3-Next进行了多项关键改进，包括混合注意力机制、高稀疏性MoE结构、利于训练稳定性的优化手段，以及可实现更快推理的多token预测机制。在核心特性方面，Qwen3-Next采用门控DeltaNet+门控注意力的混合创新架构。线性注意力虽能打破标准注意力的二次复杂度，更适合长上下文处理，但仅用线性注意力或标准注意力均有局限。线性注意力速度快但召回能力弱，标准注意力推理时成本高、速度慢。经系统实验验证，门控DeltaNet的上下文学习能力优于滑动窗口注意力、Mamba2等常用方法，将其与标准注意力按3:1比例，75%层用门控DeltaNet，25%层保留标准注意力结合，模型性能持续超越单一架构，实现性能与效率的双重提升。标准注意力层还进行了多项增强，如采用此前研究中的输出门控机制以减少注意力低秩问题、将每个注意力头的维度从128提升至256、仅对前25%位置维度应用旋转位置编码以改善长序列外推能力。稀疏性设计上，Qwen3-Next采用超高稀疏性MoE结构，800亿总参数在每步推理中仅激活约30亿，占比3.7%。实验表明，在全局负载均衡的前提下，固定激活专家数量并增加专家总参数，能稳步降低训练损失。与Qwen3的MoE相比，Qwen3-Next将总专家数扩展至512个，结合10个路由专家+1个共享专家的设计，在不影响性能的同时最大化资源利用率。训练稳定性优化方面，注意力输出门控机制有效解决了注意力Sink、大规模激活等问题，保障模型数值稳定性；针对Qwen3中QK-Norm存在的部分层归一化权重异常增大问题，Qwen3-Next采用零中心RMSNorm，并对归一化权重施加权重衰减以防止无界增长；初始化时对MoE路由器参数进行归一化，确保训练初期每个专家都能被无偏选择，减少随机初始化带来的噪声。这些设计提升了小规模实验的可靠性，保障大规模训练平稳进行。多token预测机制也是Qwen3-Next的亮点，其原生引入的多token预测（MTP）机制，不仅为投机解码提供高接受率的MTP模块，还能提升模型整体性能，同时针对MTP的多步推理性能进行优化，通过保持训练与推理一致性的多步训练，进一步提高实际场景中投机解码的接受率。预训练阶段，Qwen3-Next展现出卓越的效率。其训练数据来自Qwen3的36T token预训练语料中均匀采样的15T token子集，GPU时长不足Qwen3-30-3B的80%，计算成本仅为Qwen3-32B的9.3%，却能实现更优性能。推理速度上，填充阶段4K上下文长度时吞吐量接近Qwen3-32B的7倍，32K以上时超10倍；解码阶段4K上下文长度时吞吐量接近Qwen3-32B的4倍，32K以上时仍保持超10倍的速度优势。性能表现上，Qwen3-Next-80B-A3B-Base仅激活Qwen3-32B-Base非嵌入参数的1/10，却在多数基准测试中性能更优，且显著超过Qwen3-30B-A3B。后训练阶段的性能同样亮眼。指令模型Qwen3-Next-80B-A3B-Instruct大幅超越Qwen3-30B-A3B-Instruct-2507和Qwen3-32B-Non-thinking，性能接近旗舰模型Qwen3-235B-A22B-Instruct-2507；在RULER基准测试中，该模型在各长度下均优于注意力层更多的Qwen3-30B-A3B-Instruct-2507，且在256K上下文内击败总层数更多的Qwen3-235B-A22B-Instruct-2507，印证了混合架构在长上下文任务中的优势。推理模型Qwen3-Next-80B-A3B-Thinking性能超过Qwen3-30B-A3B-Thinking-2507、Qwen3-32B-Thinking等更高成本模型，多个基准测试击败Gemini-2.5-Flash-Thinking，关键指标接近Qwen3-235B-A22B-Thinking-2507。","news_type":1,"symbols_score_info":{"BABA":1,"09988":1.1}},"isVote":1,"tweetType":1,"viewCount":1166,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":474427730354232,"gmtCreate":1756850819481,"gmtModify":1756850821467,"author":{"id":"4101710028431850","authorId":"4101710028431850","name":"咚咚锵123","avatar":"https://static.laohu8.com/default-avatar.jpg","crmLevel":7,"crmLevelSwitch":0,"followedFlag":false,"idStr":"4101710028431850","authorIdStr":"4101710028431850"},"themes":[],"title":"","htmlText":"哪儿有70%？今年最低价格100元左右，现在也不到40%的涨幅","listText":"哪儿有70%？今年最低价格100元左右，现在也不到40%的涨幅","text":"哪儿有70%？今年最低价格100元左右，现在也不到40%的涨幅","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/474427730354232","isVote":1,"tweetType":1,"viewCount":1356,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":413656137875984,"gmtCreate":1742019603769,"gmtModify":1742019605783,"author":{"id":"4101710028431850","authorId":"4101710028431850","name":"咚咚锵123","avatar":"https://static.laohu8.com/default-avatar.jpg","crmLevel":7,"crmLevelSwitch":0,"followedFlag":false,"idStr":"4101710028431850","authorIdStr":"4101710028431850"},"themes":[],"title":"","htmlText":"星舰是特斯拉的业务么？","listText":"星舰是特斯拉的业务么？","text":"星舰是特斯拉的业务么？","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/413656137875984","repostId":"1125787882","repostType":2,"repost":{"id":"1125787882","kind":"news","weMediaInfo":{"introduction":"为用户提供金融资讯、行情、数据，旨在帮助投资者理解世界，做投资决策。","home_visible":1,"media_name":"老虎资讯综合","id":"102","head_image":"https://static.tigerbbs.com/8274c5b9d4c2852bfb1c4d6ce16c68ba"},"pubTimestamp":1742016969,"share":"https://www.laohu8.com/m/news/1125787882?lang=zh_CN&edition=full","pubTime":"2025-03-15 13:36","market":"us","language":"zh","title":"马斯克：星舰明年年底前携带Optimus前往火星，人类登陆火星最早可能在2029年开始","url":"https://stock-news.laohu8.com/highlight/detail?id=1125787882","media":"老虎资讯综合","summary":"3月15日，马斯克表示，星舰将于明年年底前前往火星，届时还将携带特斯拉人形机器人擎天柱（Optimus）。如果携带着擎天柱的星舰在火星上的着陆进展顺利，人类登陆火星最早可能在2029年开始，不过2031年的可能性更大。","content":"<html><head></head><body><p>3月15日，马斯克表示，星舰将于明年年底前前往火星，届时还将携带特斯拉人形机器人擎天柱（Optimus）。如果携带着擎天柱的星舰在火星上的着陆进展顺利，人类登陆火星最早可能在2029年开始，不过2031年的可能性更大。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/6eaca8670721a7a0613b78876e70ff34\" tg-width=\"756\" tg-height=\"721\"/></p></body></html>","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>马斯克：星舰明年年底前携带Optimus前往火星，人类登陆火星最早可能在2029年开始</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n马斯克：星舰明年年底前携带Optimus前往火星，人类登陆火星最早可能在2029年开始\n</h2>\n\n<h4 class=\"meta\">\n\n\n<a class=\"head\" href=\"https://laohu8.com/wemedia/102\">\n\n\n<div class=\"h-thumb\" style=\"background-image:url(https://static.tigerbbs.com/8274c5b9d4c2852bfb1c4d6ce16c68ba);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">老虎资讯综合 </p>\n<p class=\"h-time\">2025-03-15 13:36</p>\n</div>\n\n</a>\n\n\n</h4>\n\n</header>\n<article>\n<html><head></head><body><p>3月15日，马斯克表示，星舰将于明年年底前前往火星，届时还将携带特斯拉人形机器人擎天柱（Optimus）。如果携带着擎天柱的星舰在火星上的着陆进展顺利，人类登陆火星最早可能在2029年开始，不过2031年的可能性更大。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/6eaca8670721a7a0613b78876e70ff34\" tg-width=\"756\" tg-height=\"721\"/></p></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/b098b6fd8448068c49f55fe658ecad1d","relate_stocks":{"TSLA":"特斯拉"},"source_url":"","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"1125787882","content_text":"3月15日，马斯克表示，星舰将于明年年底前前往火星，届时还将携带特斯拉人形机器人擎天柱（Optimus）。如果携带着擎天柱的星舰在火星上的着陆进展顺利，人类登陆火星最早可能在2029年开始，不过2031年的可能性更大。","news_type":1,"symbols_score_info":{"TSLA":1.1}},"isVote":1,"tweetType":1,"viewCount":1138,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":408265422393856,"gmtCreate":1740695852868,"gmtModify":1740695854822,"author":{"id":"4101710028431850","authorId":"4101710028431850","name":"咚咚锵123","avatar":"https://static.laohu8.com/default-avatar.jpg","crmLevel":7,"crmLevelSwitch":0,"followedFlag":false,"idStr":"4101710028431850","authorIdStr":"4101710028431850"},"themes":[],"title":"","htmlText":"价格17.5 ？哪儿来的这个价格？","listText":"价格17.5 ？哪儿来的这个价格？","text":"价格17.5 ？哪儿来的这个价格？","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/408265422393856","repostId":"2514547872","repostType":2,"repost":{"id":"2514547872","kind":"news","pubTimestamp":1740652620,"share":"https://www.laohu8.com/m/news/2514547872?lang=zh_CN&edition=full","pubTime":"2025-02-27 18:37","market":"other","language":"zh","title":"阿里巴巴：于2月26日在纽约证券交易所购回57.46万股股份","url":"https://stock-news.laohu8.com/highlight/detail?id=2514547872","media":"港股那点事","summary":"格隆汇2月27日｜阿里巴巴(BABA.US)公告，公司于2025年2月26日在纽约证券交易所购回57.46万股股份，购回价格为每股17.5美元，最低购回价为每股17.29美元，总金额为999.79万美元。购回股份的授权决议于2024年8月22日获通过，购回股份拟用于注销。此次购回占购回授权通过当日已发行股份的1.46%。 \n\n\n\n海量资讯、精准解读，尽在新浪财经APP","content":"<html><body><div>\n<p cms-style=\"font-L\"><font cms-hidden=\"true\" cms-style=\"font-L\"></font> 格隆汇2月27日｜<a href=\"https://laohu8.com/S/BABA\">阿里巴巴</a>(BABA.US)公告，公司于2025年2月26日在纽约证券交易所购回57.46万股股份，购回价格为每股17.5美元，最低购回价为每股17.29美元，总金额为999.79万美元。购回股份的授权决议于2024年8月22日获通过，购回股份拟用于注销。此次购回占购回授权通过当日已发行股份的1.46%。 </p>\n<div></div>\n<div>\n<div><img src=\"\"/></div>\n<div>海量资讯、精准解读，尽在新浪财经APP</div>\n</div>\n</div></body></html>","source":"sina","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>阿里巴巴：于2月26日在纽约证券交易所购回57.46万股股份</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n阿里巴巴：于2月26日在纽约证券交易所购回57.46万股股份\n</h2>\n\n<h4 class=\"meta\">\n\n\n2025-02-27 18:37 北京时间 <a href=https://finance.sina.com.cn/stock/bxjj/2025-02-27/doc-inemxrkf6343616.shtml><strong>港股那点事</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>格隆汇2月27日｜阿里巴巴(BABA.US)公告，公司于2025年2月26日在纽约证券交易所购回57.46万股股份，购回价格为每股17.5美元，最低购回价为每股17.29美元，总金额为999.79万美元。购回股份的授权决议于2024年8月22日获通过，购回股份拟用于注销。此次购回占购回授权通过当日已发行股份的1.46%。 \n\n\n\n海量资讯、精准解读，尽在新浪财经APP</p>\n\n<a href=\"https://finance.sina.com.cn/stock/bxjj/2025-02-27/doc-inemxrkf6343616.shtml\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"BK4524":"宅经济概念","LU1880383366.USD":"东方汇理中国股票基金 A2 (C)","LU1152091754.HKD":"UBS (LUX) EQUITY FUND - CHINA OPPORTUNITY (USD) \"PM\" (HKD) INC","BK4554":"元宇宙及AR概念","BK4581":"高盛持仓","LU1051768304.USD":"贝莱德新兴市场股票收益A6","BK4543":"AI","BK4527":"明星科技股","LU1823568750.SGD":"Fidelity Global Technology A-ACC SGD","BK4531":"中概回港概念","LU1008478684.HKD":"UBS (LUX) EQUITY FUND - CHINA OPPORTUNITY \"P\" (HKD) ACC","LU1688375341.USD":"贝莱德中国灵活股票基金","LU0072913022.USD":"UBS (LUX) EQUITY FUND - GREATER CHINA \"P\" (USD) ACC","LU0797268264.HKD":"ALLIANZ TOTAL RETURN ASIAN EQUITY \"AT\" (HKD) ACC","BK4526":"热门中概股","LU0348816934.USD":"ALLIANZ TOTAL RETURN ASIAN EQUITY \"AT\" (USD)","LU1515016050.SGD":"Blackrock Emerging Markets Equity Income A6 SGD-H","BK4585":"ETF&股票定投概念","BK4534":"瑞士信贷持仓","LU0880133367.SGD":"UBS (LUX) 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