半沐
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半沐
慢就是快，相信复利的力量 币安100%返佣邀请码: BM168
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Who is the most profitable company this summer? It’s not “who”—it’s Alibaba. The Qwen 3.8 preview has been released; its capabilities have already surpassed Opus 4.8 and GPT 5.5. When the official version comes out, it must not be able to be outclassed. As for Kimi K3, it’s already been crushing tests on overseas networks, but domestically, some idiots are playing both sides—yet Kimi’s official coding plan is sold out. And for the Moon’s Dark Side, its largest external shareholder is still Alibaba, holding 36%. That means they’re directly raking in money.
Kimi is about to go public. With Kimi K3, it directly blasts out a $1 trillion market cap—Alibaba holds 36%, directly raking in huge profits: $BABA
Binance News
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AI Trends | Mian Plans a Hong Kong IPO Within 6 Months
Mian told investors that it is preparing to go public within the next six months at the earliest, using the latest model to upend industry perceptions and capitalize on the opportunity to trigger a global tech-stock shakeup.Insiders say this Chinese AI pioneer has distributed shareholder resolutions to investors to seek support for a Hong Kong listing. Starting the notification process means the IPO could occur within the next six months.Mian is wrapping up a funding round that may value the company at over $30 billion. The company believes the timing is right: its annualized recurring revenue reached $300 million in June, up sharply from $200 million in April.The company began preparations for an IPO before releasing Kimi K3 last week. The model has 28 billion parameters, and its overall capabilities are second only to Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6. In some leading frontier benchmarks, Artificial Analysis ranks Kimi K3 above Anthropic Opus 4.8, marking a first milestone for a Chinese open-weight model to achieve this.
See translation
仅次fable5 ,能够每天进化的大模型,恐怖如斯，估计是为了千问4做准备
半沐
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关于Qwen3.8如何实现“以天为单位”的进化，结合目前的公开信息，其核心机制主要体现在高频自动化更新、架构底层的动态激活以及智能体（Agent）技能的夜间验证三个方面。具体细节如下：
1. 高频自动化的权重与链路更新
Qwen3.8打破了传统的单次大规模发布模式，建立了一套高频自动化迭代机制。团队透露，模型每天凌晨会自动更新权重，每48小时刷新一次推理链路优化，并且每周会推送全新的多模态对齐能力。这种机制让模型的升级变成了一场持续进行的“现场直播”。
2. 动态专家路由与分层记忆缓存架构
在底层架构上，Qwen3.8抛弃了传统的稀疏激活套路，采用了“动态专家路由+分层记忆缓存”的混合架构。这种设计使得2.4T参数能够动态运作，模型在运行时“不是堆出来的巨兽，而是会呼吸、能反思、懂取舍的智能体”。这种架构层面的优化为高频迭代提供了技术基础。
3. 智能体（Agent）技能的“夜间进化与验证”机制
在具体的Agent任务执行层面，Qwen系列（如Qwen3-Max）应用了“集体技能进化流程”。该机制将用户的真实交互会话转化为结构化证据，由Evolver分析模式并生成候选更新。这些候选技能不会直接上线，而是进入“夜间验证阶段”：在真实环境中同时执行旧技能和新候选技能，仅当新技能在整体任务成功率和执行稳定性上确实优于旧技能时才会被接受并部署。这保证了已部署的技能池不会随时间退化，实现了持续稳定的性能提升。
4. 预览版的持续测试与后训练
目前上线的 Qwen3.8-Max 预览版模型正在以“天”为单位持续进化。有分析指出，这种基座参数的小版本持续更新，表明模型可能正在进行密集的后训练（Post-training），为最终4.0正式版的发布做准备。
1. 高频自动化的权重与链路更新
Qwen3.8打破了传统的单次大规模发布模式，建立了一套高频自动化迭代机制。团队透露，模型每天凌晨会自动更新权重，每48小时刷新一次推理链路优化，并且每周会推送全新的多模态对齐能力。这种机制让模型的升级变成了一场持续进行的“现场直播”。
2. 动态专家路由与分层记忆缓存架构
在底层架构上，Qwen3.8抛弃了传统的稀疏激活套路，采用了“动态专家路由+分层记忆缓存”的混合架构。这种设计使得2.4T参数能够动态运作，模型在运行时“不是堆出来的巨兽，而是会呼吸、能反思、懂取舍的智能体”。这种架构层面的优化为高频迭代提供了技术基础。
3. 智能体（Agent）技能的“夜间进化与验证”机制
在具体的Agent任务执行层面，Qwen系列（如Qwen3-Max）应用了“集体技能进化流程”。该机制将用户的真实交互会话转化为结构化证据，由Evolver分析模式并生成候选更新。这些候选技能不会直接上线，而是进入“夜间验证阶段”：在真实环境中同时执行旧技能和新候选技能，仅当新技能在整体任务成功率和执行稳定性上确实优于旧技能时才会被接受并部署。这保证了已部署的技能池不会随时间退化，实现了持续稳定的性能提升。
4. 预览版的持续测试与后训练
目前上线的 Qwen3.8-Max 预览版模型正在以“天”为单位持续进化。有分析指出，这种基座参数的小版本持续更新，表明模型可能正在进行密集的后训练（Post-training），为最终4.0正式版的发布做准备。
See translation
关于Qwen3.8如何实现“以天为单位”的进化，结合目前的公开信息，其核心机制主要体现在高频自动化更新、架构底层的动态激活以及智能体（Agent）技能的夜间验证三个方面。具体细节如下：
1. 高频自动化的权重与链路更新
Qwen3.8打破了传统的单次大规模发布模式，建立了一套高频自动化迭代机制。团队透露，模型每天凌晨会自动更新权重，每48小时刷新一次推理链路优化，并且每周会推送全新的多模态对齐能力。这种机制让模型的升级变成了一场持续进行的“现场直播”。
2. 动态专家路由与分层记忆缓存架构
在底层架构上，Qwen3.8抛弃了传统的稀疏激活套路，采用了“动态专家路由+分层记忆缓存”的混合架构。这种设计使得2.4T参数能够动态运作，模型在运行时“不是堆出来的巨兽，而是会呼吸、能反思、懂取舍的智能体”。这种架构层面的优化为高频迭代提供了技术基础。
3. 智能体（Agent）技能的“夜间进化与验证”机制
在具体的Agent任务执行层面，Qwen系列（如Qwen3-Max）应用了“集体技能进化流程”。该机制将用户的真实交互会话转化为结构化证据，由Evolver分析模式并生成候选更新。这些候选技能不会直接上线，而是进入“夜间验证阶段”：在真实环境中同时执行旧技能和新候选技能，仅当新技能在整体任务成功率和执行稳定性上确实优于旧技能时才会被接受并部署。这保证了已部署的技能池不会随时间退化，实现了持续稳定的性能提升。
4. 预览版的持续测试与后训练
目前上线的 Qwen3.8-Max 预览版模型正在以“天”为单位持续进化。有分析指出，这种基座参数的小版本持续更新，表明模型可能正在进行密集的后训练（Post-training），为最终4.0正式版的发布做准备。
1. 高频自动化的权重与链路更新
Qwen3.8打破了传统的单次大规模发布模式，建立了一套高频自动化迭代机制。团队透露，模型每天凌晨会自动更新权重，每48小时刷新一次推理链路优化，并且每周会推送全新的多模态对齐能力。这种机制让模型的升级变成了一场持续进行的“现场直播”。
2. 动态专家路由与分层记忆缓存架构
在底层架构上，Qwen3.8抛弃了传统的稀疏激活套路，采用了“动态专家路由+分层记忆缓存”的混合架构。这种设计使得2.4T参数能够动态运作，模型在运行时“不是堆出来的巨兽，而是会呼吸、能反思、懂取舍的智能体”。这种架构层面的优化为高频迭代提供了技术基础。
3. 智能体（Agent）技能的“夜间进化与验证”机制
在具体的Agent任务执行层面，Qwen系列（如Qwen3-Max）应用了“集体技能进化流程”。该机制将用户的真实交互会话转化为结构化证据，由Evolver分析模式并生成候选更新。这些候选技能不会直接上线，而是进入“夜间验证阶段”：在真实环境中同时执行旧技能和新候选技能，仅当新技能在整体任务成功率和执行稳定性上确实优于旧技能时才会被接受并部署。这保证了已部署的技能池不会随时间退化，实现了持续稳定的性能提升。
4. 预览版的持续测试与后训练
目前上线的 Qwen3.8-Max 预览版模型正在以“天”为单位持续进化。有分析指出，这种基座参数的小版本持续更新，表明模型可能正在进行密集的后训练（Post-training），为最终4.0正式版的发布做准备。
See translation
周一会不会直接封板啊
MarsBit News
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Alibaba Qianwen Qwen3.8 Large Model to Be Released and Open-Sourced
Mars Finance News, (Science and Technology Innovation Board Daily) learned that Alibaba Qianwen Qwen3.8 large model is about to be released and open-sourced, with up to 2.4T parameters. Currently, the Qianwen 3.8-Max preview version has already been showcased on Alibaba’s Token plan, Qoder, and QoderWork. (Reporter Huang Xinyi, Science and Technology Innovation Board Daily)
See translation
国产ai要杀疯了，阿里巴巴牛逼
MarsBit News
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Alibaba Qianwen Qwen3.8 Large Model to Be Released and Open-Sourced
Mars Finance News, (Science and Technology Innovation Board Daily) learned that Alibaba Qianwen Qwen3.8 large model is about to be released and open-sourced, with up to 2.4T parameters. Currently, the Qianwen 3.8-Max preview version has already been showcased on Alibaba’s Token plan, Qoder, and QoderWork. (Reporter Huang Xinyi, Science and Technology Innovation Board Daily)
Alibaba is set to launch Qianwen 3.8, and its capabilities are only second to Claude Fable 5. Alibaba is going to step up its efforts.
It’s all Alibaba’s investment—what a mess, didn’t expect that, did you? Pull it out and scare you to death.
链研社lianyanshe
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Alibaba has also been lucky recently: Zhongxin has a floating profit of 130 billion yuan. Today, Kimi K3 is making the charts, and Alibaba holds 36% of the shares. Part of the investment also involves participating in it in the form of providing compute power services.
If Kimi were listed in Hong Kong, with limited shares available for trading, Alibaba, Meituan, Tencent, and Xiaohongshu have all invested. Its market cap might exceed Zhipu. Then the money Alibaba earns would be more than what it earned on Changxin—just from these two, it could make 20% of Alibaba’s market value.
Source: @lianyanshe on X
#Crypto #Web3
If Kimi were listed in Hong Kong, with limited shares available for trading, Alibaba, Meituan, Tencent, and Xiaohongshu have all invested. Its market cap might exceed Zhipu. Then the money Alibaba earns would be more than what it earned on Changxin—just from these two, it could make 20% of Alibaba’s market value.
Source: @lianyanshe on X
#Crypto #Web3
kimi.k3 will be fully open sourced on July 27. Yang Zhilin is awesome, Alibaba is awesome $BABA
Yang Zhilin: Many geniuses call me a genius
半沐
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Achievements of Yang Zhilin, Founder of The Dark Side of the Moon
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
Genius is only a matter of seeing its threshold
半沐
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Achievements of Yang Zhilin, Founder of The Dark Side of the Moon
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
Achievements of Yang Zhilin, Founder of The Dark Side of the Moon
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
1. Academic achievements: laying the foundational bedrock for global long-context large language models (the highest-value long-term asset)
1) Transformer-XL (2018, co-first author)
- Core breakthrough: addressed the fatal limitations of the original Transformer—its fixed context window and inability to capture long-range text dependencies—by introducing a segment-level recurrent memory mechanism.
- Industry significance: one of the technical sources behind today’s long-context models. Many long-text optimization approaches in Claude, Kimi, and the GPT series borrow heavily from this idea. Without this work, models with one million-token long context would be next to impossible to even discuss. It also serves as a theoretical foundation for Kimi’s later focus on ultra-long documents.
2) XLNet (2019, first author, co-released with Google Brain)
- Core breakthrough: tackled information bias in BERT-style masked pretraining by adopting permutation language modeling; it surpassed BERT across 20 mainstream NLP benchmarks.
- Historical standing: widely recognized in the industry as one of the most important advances in pretraining models after BERT. Selected for a NeurIPS Oral presentation; the paper has been cited tens of thousands of times, making it one of the most influential foundational NLP works by a Chinese scholar.
3) Other academic contributions
- Collaborated with Turing Award winner Bengio to build the HotpotQA multi-turn reasoning question-answering dataset;
- Completed a PhD in just 4 years (regular program is 6 years), studied under Salakhutdinov, the former AI lead at Apple; interned at Google Brain and Meta FAIR, contributing to early R&D for Gemini and Bard;
- Among NLP papers by scholars under 35 in China, total citations have long remained in the top tier.
半沐
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#kimi k3#
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
A group of people still haven’t figured out the value of Kimi K3. Its frontend capabilities surpass Fable 5—now it’s number one in the world. Its backend capabilities are only behind Fable 5. GPT 5.6.Sol runs at the highest configuration, and the subscription price is only 5%. Currently, OpenAI is valued at $1.2 trillion. If Moonshot’s Dark Side IPOs, it could directly approach $1 trillion. Why? Because its founder is Yang Zhilin, a top-tier talent whom AI leaders around the world all regard as a benchmark—first author of Transformer-XL, first author of XLNet—someone even more impressive than Sam Altman or Dario; an outstanding figure.
And the largest shareholder of Moonshot’s Dark Side is Alibaba, holding 36% of the shares.
And the largest shareholder of Moonshot’s Dark Side is Alibaba, holding 36% of the shares.
半沐
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#kimi k3#
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
#kimi k3#
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
Super awesome Kimi k3 from the Dark Side of the Moon, and his largest external shareholder is Alibaba, holding 36%. If we estimate the value of Moon's Dark Side based on OpenAI’s valuation, the value of the shares Alibaba currently holds is $500 billion—two valuations for Alibaba right now
$BABA
$BABA
A group of people still don’t understand why the Dark Side of the Moon is so awesome—what does it have to do with Alibaba? Because Alibaba holds 36% of the Dark Side of the Moon’s shares, participates in every funding round, and if it goes public it will directly break through $10,000. And that price directly creates an investment income of $360 billion for Alibaba.
Alibaba’s greatest investment isn’t investing in ChangXin Memory, but investing in the Dark Side of the Moon, acquiring 36% of the shares—Kimi K3 directly smashes through
Fable 5 is still just cheap cardboard. Kimi K3 directly built it for you—J-12. This really just blows right through everything.
半沐
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The strongest model in the world this year, Kimi K3, is out—it's awesome!
The strongest model in the world this year, Kimi K3, is out—it's awesome!
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