Kimi K2.6 surpassed Opus 4.5 with a score of 56.3 in 4.8 months, and GLM-5.2 cleared GPT-5.2 with a score of 72.4 in 6 months.
这两个数字是判断模型层会否商品化的关键。但分子分母都由作者自选：换一组基准、换一个时代起点模型，追赶期就会变。当成方向性信号可以，当成定量结论会踩坑。
7 Matching Annotations
- Aug 2026
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newsletter.semianalysis.com newsletter.semianalysis.com
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www.apple.com www.apple.com
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This creates a vast shared memory pool across systems, allowing users to load the largest and most demanding frontier-class open-weight models available today.
用 Thunderbolt 5 加 RDMA 把多机拼成共享内存池，是在没有 NVLink 的前提下硬凑容量。注意苹果只敢报四台三倍，已见明显衰减。这条路线适合权重长期驻留的本地推理，不适合训练，也撑不起多租户服务。
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- Jun 2026
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Cards are made in three weights, and in calculatingthe sized card cabinet required, it is convenient torecollect that the capacity of the standard depth draweris 1,000 light cards, 750 medium weight, and 500 heavycards.
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- Feb 2026
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Airbnb picks Alibaba’s Qwen over ChatGPT in a win for Chinese open-source AIAirbnb ‘relies heavily’ on Alibaba’s Qwen models to power its AI customer service agent, CEO Brian Chesky says
Even US firms are using Chinese models because they are so much cheaper. They do not use them for everything, but where they can get away with lower cost options they will.
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www.chathamhouse.org www.chathamhouse.org
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Low-cost Chinese AI models forge ahead, even in the US, raising the risks of a US AI bubble Nvidia’s latest earnings report reassured some. But Chinese AI models are fast gaining a following around the world, underlining concerns over an ‘AI bubble’ centered on high-investment, high-cost US models.
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- Dec 2023
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www.canadafit.ca www.canadafit.caHelp1
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- Feb 2017
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cs231n.github.io cs231n.github.io
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SVM only cares that the difference is at least 10
The margin seems to be manually set by the creator in the loss function. In the sample code, the margin is 1-- so the incorrect class has to be scored lower than the correct class by 1.
How is this margin determined? It seems like one would have to know the magnitude of the scores beforehand.
Diving deeper, is the scoring magnitude always the same if the parameters are normalized by their average and scaled to be between 0 and 1? (or -1 and -1... not sure of the correct scaling implementation)
Coming back to the topic -- is this 'minimum margin' or delta a tune-able parameter?
What effects do we see on the model by adjusting this parameter?
What are best and worst case scenarios of playing with this parameter?
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