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Ever since ChatGPT was released, I’ve been thinking about the business logic and competitive dynamics of large language models. I’ve struggled to find a good framework for it. A few days ago, DeepSeek V4 came out. I wrote a long post critiquing some of V4’s design decisions. And suddenly, three years of unresolved business questions clicked into place.
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DeepSeek V4’s technical report had just been released, and I opened it immediately. The amount and breadth of information in it, and the technical depth, are vast.
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I think the best writing style for a paper must be extremely simple. So simple that reviewers reject it for not being innovative enough. Only then can the paper be considered fully revised.
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Small GPU, huge model. The GPU-poor are about to rise up!
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I have been on Zhihu for a few years, but this is the first time I am actually promoting my own paper.
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The problem with biomimetics is that the way carbon-based organisms implement something is not necessarily the optimal design for silicon-based organisms.
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If you ask me about the timelines for AGI, artificial general intelligence, and ASI, artificial superintelligence, I would say:
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While reading the DeepSeek R1 paper during the day, all I could think about was R1-Zero.
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I generally call artificial intelligence pseudo-science, pseudo-math, and pseudo-engineering: a trinity.
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I often make one kind of mistake: when an idea lacks mathematical beauty, I subconsciously kill it off.
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Hard to say; the more I look at post-training, the more unnatural it feels.
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Over the past couple of days I went to UT Austin to visit a friend, and I sat in on a few physics classes with him.
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Math seems to have been counterintuitive all along.
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One day, an academic heavyweight wrote a paper titled Tomatoes Are All Eggs Need, which for the first time proposed the method of making tomato fried eggs.
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I once dreamed of a paper like this.