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Data centers cluster in Ulanqab: Direct supply from the West Inner Mongolia power grid and Inner Mongolia's power surplus
The delivered electricity price for data centers in Ulanqab is about 0.358 yuan/kWh, 0.442 yuan lower than Beijing. A single AI accelerator card saves about 2,700 yuan per year, and a 10,000-card cluster saves about 27 million yuan per year. The article argues that the low electricity price comes from the West Inner Mongolia power grid, as a provincial-level independent grid, allowing dedicated-line direct supply between data centers and power plants and exempting transmission and distribution tariffs, rather than from wind and solar resources themselves. Combined with Inner Mongolia's surplus capacity of more than 100 GW of wind power and about 130 GW of coal power as of the end of 2025, the region has already built or planned 89 data centers, with confirmed computing capacity of 15 GW. This institutional cost advantage is difficult for other computing hubs to replicate.
The delivered electricity price for data centers in Ulanqab is about 0.358 yuan/kWh, 0.442 yuan lower than Beijing. A single AI accelerator card saves about 2,700 yuan per year, and a 10,000-card cluster saves about 27 million yuan per year. The article argues that the low electricity price comes from the West Inner Mongolia power grid, as a provincial-level independent grid, allowing dedicated-line direct supply between data centers and power plants and exempting transmission and distribution t
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TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God
InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.
InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suit
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Latest Open Source Model Roundup, Issue 24: Motif-3, GLM-5.3, Hy4-preview, and Open Source License Changes
The Interconnects Issue 24 open source model roundup notes that in 2026 Western vendors shifted to Apache 2.0, while leading Chinese vendors instead tightened their licenses: Zhipu's GLM-5.3 changed from MIT to a custom license, requiring model service providers with annual revenue exceeding $10 billion to first pass a Z.AI security review, and Kimi K3 and MiniMax M3 also attached commercial agreements or revenue thresholds. The models included in this issue are the MIT-licensed Motif-3, Xiaohongshu dots-studio's dots3-note-prev, Qwen3.8-Flash-Next (125B-A6B, including 51B n-gram embeddings, using GDN and Qwen Sparse Attention), GLM-5.3-Flash, which is being trialed first on OpenRouter and OpenCode under the name Ox-Alpha, Tencent's Hy4-preview, as well as NVIDIA Nemotron-3.5-Lightning-30B-A3B, Ant Group's Ling-3.0-flash, and Qwen3.8-2.4T-A95B.
The Interconnects Issue 24 open source model roundup notes that in 2026 Western vendors shifted to Apache 2.0, while leading Chinese vendors instead tightened their licenses: Zhipu's GLM-5.3 changed from MIT to a custom license, requiring model service providers with annual revenue exceeding $10 billion to first pass a Z.AI security review, and Kimi K3 and MiniMax M3 also attached commercial agreements or revenue thresholds. The models included in this issue are the MIT-licensed Motif-3, Xiaohon
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