Another critique one should make here is that even using “GDP per hours worked,” which is not the right measure, the only European countries clearly better than the US are Denmark and Norway, and one of them is sitting on a sea of oil wealth.

Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: Tech report: Tech blog:

I support open-source models distilling what commercial companies distilled for free from the entire internet. I published distillation for free in 1991 in Europe - this was copied in the US and in China (

I am pleased to announce that a memorandum of understanding has been signed between the Republic of Kazakhstan and Network School. Our new campus will become a haven for global techno-optimism, with expedited visas, streamlined redomiciliation, and active recruitment of talent.

Neoclouds: The Kimi K3 Scare Kimi K3 caused a large scare in the AI trade as this Chinese open source model matched frontier models on benchmarks. Let me unpack what's actually going on. Chinese Labs have much less GPUs than American Labs and yet are able to train "just as good" of a model. This implies that Chinese Labs have huge efficiencies that allow them to use much less GPUs in training. This is would imply less HBM, less datacenters, less cloud bills - the whole capex heavy buildout that the AI trade is predicated upon. Now here's the big hole in all this logic. MoonshotAI, the Lab that made Kimi K3, is supposedly a magnitude more efficient in training than American Labs yet their inference compute consumption is the same or less efficient! Kimi K3 cost exactly the same as GPT 5.5 and slightly less than Claude 4.8 Opus High. Some people are misunderstanding what expensive tokens mean. Yes the cost of the open source weights/topology is 0 but the amount of the compute/GPUs that you need to run the model is a metric of a efficient your inference is. Compute/GPU time is very expensive and cost of open source inference is very not free. Now, it makes absolutely zero sense that MoonshotAI Kimi is so much more efficient in training but slightly less efficient in inference. Why? Training is a the forward pass plus backward pass and inference is the forward pass. This means that training efficiency improvements lead to inference efficiency improvements. You know why MoonshotAI training and inference efficiencies are asymmetric? Because their "training efficiencies" come from distilling American models. If MoonshotAI had true training efficiencies they would also show inference efficiencies but they have no advantage in inference efficiencies! AI Capex will still continue because: 1. If American Labs stop training capex, then Chinese models will also stop improving. AI progress will have stopped. American companies have never given up just because Chinese are trying to copy them. 2. Chinese model still consume alot of compute/GPUs for inference. Inference demand will outstrip training demand anyways.

Humanoid network have just raised $300,000 in 12 hours. Builders looking for funding and distribution in Robinhood Chain Please Contact: - ai developers @celesteanglm - robotics @IntoPurpleMoon - degen @0xTP91 - moonshot product @Ryhar8103 - 龙的传人 @sal_hotpot666 - US @DonJohnsonSays - EMEA @umeirzz - Institution @hananyss