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看完今天上午云栖大会千问交出的整套底牌， 我感觉最锋利的地方全落在了账本上： 研发端让模型零干预自主迭代 33 轮扛住研发重活， 成本端直接把缓存命中输入打穿到了每百万 Tokens 一毛钱。 咱们把这两串数字掰开看， 在模型研发端，Qwen3.8-Max 在没有任何人工干预的情况下自主跑完 33 轮闭环演进，调用上万次工业工具把总线模块面积缩减了 42%，Artificial Analysis 质量指数推到了 45。 面对从未见过的阿里新款芯片，它自己进场完成算子调优与底层框架适配，竟然把单实例推理吞吐拉升了 96%，这说明算法团队日夜洗数据调参的阶段正在过去，大模型正在自己接管研发流水线。 而在落地成本端，提前开源架构的 Qwen3.8-Flash 通过注意力机制等底层重构，把训练成本直接砍掉近九成，配上每百万 Tokens 一毛钱的缓存单价，让大规模并发调用有了真实的利润空间。 Pinterest CEO 在公开场合提到，换用千问后综合调用成本压缩至同类商业模型的 8%。 更耐人寻味的是技术迭代的节奏，新架构 Qwen4 已经在训，后续版本规划直奔 5 到 10 万亿参数，配合全球突破 30 亿次的开源下载量，感觉千问交出来的已经不是几个孤立的模型版本了，有点把 AI 从手工作坊时代推进到了自运转的工业工厂时代的意味。 并且阿里的这种工程化能力已经开始向多模态交付延伸了，视频模型 Wan3.0 在主流评测中排名前列，下代模型定档 11 月，刚开源的 Qwen-Image-2.1 凭借 7B 的轻量尺寸，把原生透明图层与局部编辑做进了同一个模型，支持圈选修改、图层替换与多图保真参考，以往需要跨几个专业软件来回倒腾的设计链路，现在能在同一个模型内被压成秒级响应。 从算法团队教模型理解世界，到模型反向进入研发体系、全行业的计算成本被大幅摊薄。 开源生态把图纸与开采机完整交出来之后，竞争的重心彻底转向了谁能用更低成本把真实业务跑通。 这套成本架构落地之后，大家最想先重构手头的哪块业务？我自己是打算把后台知识库检索和长文本记忆全量切过去，粗算一年能省出好几台服务器的实打实开销。

看完今天上午云栖大会千问交出的整套底牌， 我感觉最锋利的地方全落在了账本上： 研发端让模型零干预自主迭代 33 轮扛住研发重活， 成本端直接把缓存命中输入打穿到了每百万 Tokens 一毛钱。 咱们把这两串数字掰开看， 在模型研发端，Qwen3.8-Max 在没有任何人工干预的情况下自主跑完 33 轮闭环演进，调用上万次工业工具把总线模块面积缩减了 42%，Artificial Analysis 质量指数推到了 45。 面对从未见过的阿里新款芯片，它自己进场完成算子调优与底层框架适配，竟然把单实例推理吞吐拉升了 96%，这说明算法团队日夜洗数据调参的阶段正在过去，大模型正在自己接管研发流水线。 而在落地成本端，提前开源架构的 Qwen3.8-Flash 通过注意力机制等底层重构，把训练成本直接砍掉近九成，配上每百万 Tokens 一毛钱的缓存单价，让大规模并发调用有了真实的利润空间。 Pinterest CEO 在公开场合提到，换用千问后综合调用成本压缩至同类商业模型的 8%。 更耐人寻味的是技术迭代的节奏，新架构 Qwen4 已经在训，后续版本规划直奔 5 到 10 万亿参数，配合全球突破 30 亿次的开源下载量，感觉千问交出来的已经不是几个孤立的模型版本了，有点把 AI 从手工作坊时代推进到了自运转的工业工厂时代的意味。 并且阿里的这种工程化能力已经开始向多模态交付延伸了，视频模型 Wan3.0 在主流评测中排名前列，下代模型定档 11 月，刚开源的 Qwen-Image-2.1 凭借 7B 的轻量尺寸，把原生透明图层与局部编辑做进了同一个模型，支持圈选修改、图层替换与多图保真参考，以往需要跨几个专业软件来回倒腾的设计链路，现在能在同一个模型内被压成秒级响应。 从算法团队教模型理解世界，到模型反向进入研发体系、全行业的计算成本被大幅摊薄。 开源生态把图纸与开采机完整交出来之后，竞争的重心彻底转向了谁能用更低成本把真实业务跑通。 这套成本架构落地之后，大家最想先重构手头的哪块业务？我自己是打算把后台知识库检索和长文本记忆全量切过去，粗算一年能省出好几台服务器的实打实开销。

看完今天上午云栖大会千问交出的整套底牌， 我感觉最锋利的地方全落在了账本上： 研发端让模型零干预自主迭代 33 轮扛住研发重活， 成本端直接把缓存命中输入打穿到了每百万 Tokens 一毛钱。 咱们把这两串数字掰开看， 在模型研发端，Qwen3.8-Max 在没有任何人工干预的情况下自主跑完 33 轮闭环演进，调用上万次工业工具把总线模块面积缩减了 42%，Artificial Analysis 质量指数推到了 45。 面对从未见过的阿里新款芯片，它自己进场完成算子调优与底层框架适配，竟然把单实例推理吞吐拉升了 96%，这说明算法团队日夜洗数据调参的阶段正在过去，大模型正在自己接管研发流水线。 而在落地成本端，提前开源架构的 Qwen3.8-Flash 通过注意力机制等底层重构，把训练成本直接砍掉近九成，配上每百万 Tokens 一毛钱的缓存单价，让大规模并发调用有了真实的利润空间。 Pinterest CEO 在公开场合提到，换用千问后综合调用成本压缩至同类商业模型的 8%。 更耐人寻味的是技术迭代的节奏，新架构 Qwen4 已经在训，后续版本规划直奔 5 到 10 万亿参数，配合全球突破 30 亿次的开源下载量，感觉千问交出来的已经不是几个孤立的模型版本了，有点把 AI 从手工作坊时代推进到了自运转的工业工厂时代的意味。 并且阿里的这种工程化能力已经开始向多模态交付延伸了，视频模型 Wan3.0 在主流评测中排名前列，下代模型定档 11 月，刚开源的 Qwen-Image-2.1 凭借 7B 的轻量尺寸，把原生透明图层与局部编辑做进了同一个模型，支持圈选修改、图层替换与多图保真参考，以往需要跨几个专业软件来回倒腾的设计链路，现在能在同一个模型内被压成秒级响应。 从算法团队教模型理解世界，到模型反向进入研发体系、全行业的计算成本被大幅摊薄。 开源生态把图纸与开采机完整交出来之后，竞争的重心彻底转向了谁能用更低成本把真实业务跑通。 这套成本架构落地之后，大家最想先重构手头的哪块业务？我自己是打算把后台知识库检索和长文本记忆全量切过去，粗算一年能省出好几台服务器的实打实开销。

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): https://t.co/jni2tWazAH ]

Jev 正在疯狂工作（视频未加速 https://t.co/bppKIVqI0Q

Grok 4.7 is here. It's a notable improvement over Grok 4.6 at the same price and speed. https://t.co/H3OTBbXyvO

Hy Image3.5 preview is live. 🚀 Professional-grade image generation, +30% win rate in human eval vs Hy Image3.0 Both Text to image & Image to image available. Up to 2K. Better Consistency. API: https://t.co/5BvR2SdI6Q Priced for everyone. $0.024 per image on Tencent Cloud API. We only charge for what we generate — your reference images are free. Two weeks free (Only in OnSolo and Miora): Miora https://t.co/UxwBScI64O OnSolo https://t.co/iXnNNg6U9k Try it and tell us where it breaks.

🚨Qwen4家族首次曝光！！ 刚刚，在2026年云栖大会的开幕式上，新任@Alibaba_Qwen LLM负责人刘大一恒官宣了即将到来的Qwen4家族！ 包含Qwen4-Max Qwen4-Flash&Qwen4-Plus 还有Qwen4-27B！！！ 未来Qwen会训5-10T的模型

Next test on Spark 1: 32 synthetic invoices, with purchase orders and payment records. GPT-6 Astra coordinates the MiniCPM5-2B workers. They sort out matches, short payments, duplicate references and price disputes, then write the results into a case ledger. All 32 verified in 67.8 seconds. Eight in each category, with 232 executed tool calls. The video is real time. This demo doesn’t move money.