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The compilation rules for four indices, including the Shanghai Composite Index, will be adjusted starting today; the State-owned Assets Supervision and Administration Commission has outlined plans to deepen state-owned enterprise reform; S&P’s new regulations will affect $375 billion in funds.

Starting October 26, 2026, the SSE Composite Index, the SSE A‑Share Index, the SSE Free‑Float Index, and the STAR 50 Index will officially implement their revised compilation methodologies, introducing a new cap of 15% on the weight of any single constituent. On October 23, the State-owned Assets Supervision and Administration Commission of the State Council emphasized the need to steadily advance the implementation of the latest round of state‑owned asset and state‑owned enterprise reform tasks. On the same day (October 25), S&P Dow Jones Indices announced adjustments to its growth/value stock classification criteria; the new rules will take effect on December 21, impacting approximately $375 billion in passive investment funds.

Why it matters：The three policy and market‑rule changes all exhibit significant timeliness and systemic impacts: the index‑weighting red line directly affects the stability of A‑share core benchmarks; the SASAC’s statement reflects the central government’s urgent plan to tackle tough challenges in state‑owned enterprise reform; and S&P’s rule overhaul reshapes the logic of value investing, with implications amounting to hundreds of billions of dollars—making it a key variable in the global capital markets. All information is drawn from an authoritative, comprehensive report published by TMTPost on October 25, 2026, with clearly verifiable timelines, entities, measures, and impact figures.

Intel has received an exemption from the U.S. FCC’s ban on foreign-made robots, in order to ensure automated logistics at its chip fabs.

On October 22, 2026, the U.S. Federal Communications Commission (FCC) granted Intel a one-year exemption, allowing it to continue using certain foreign‑made automated guided vehicles (AGVs) at its U.S. semiconductor fabs, as the original ban’s vague wording could inadvertently affect critical semiconductor manufacturing equipment.

Kazakhstan is accelerating the development of its AI computing infrastructure, and Firebird has obtained U.S. export approval to deploy 45,000 NVIDIA Grace Blackwell GPUs.

Kazakhstan’s AI ecosystem is transitioning from the planning phase to large-scale deployment. Firebird has obtained U.S. export licenses to build an AI factory in Kazakhstan, with the first phase deploying 45,000 NVIDIA Grace Blackwell GPUs. Kazakhtelecom has already brought online more than 1,000 H200/B300 GPUs and plans to add over 2,000 Vera Rubin and other GPUs by 2025. Multiple stakeholders are collaborating to advance computing power supply, developer access, and the local implementation of AI applications.

Amazon SageMaker launches the Training Session API: users can write custom training loops for large models without managing GPUs.

Amazon SageMaker has released the Training Sessions API, enabling users to implement custom large-model training workflows—such as SFT, RLHF, GRPO, and KTO—using pure Python scripts. GPU resource scheduling, fault tolerance, distributed execution, and checkpoint management are all handled by the service.

NVIDIA drives the transformation of Europe’s retail sector: Open models and physical intelligence are being implemented in e-commerce, warehousing, and virtual try-on.

European retailers are leveraging NVIDIA-accelerated open large models and physics‑AI technologies to achieve large‑scale deployment across scenarios such as digital product discovery, intelligent warehouse simulation, virtual try‑on, and accelerated R&D. Companies including Shopify, Zooplus, L’Oréal, and the Otto Group have used tools like the Nemotron series of models, Omniverse digital twins, and ALCHEMI molecular simulations to significantly reduce inference costs, improve search accuracy, shorten R&D cycles, and validate improvements in conversion rates and sales growth.

The judge dismissed Trump’s absurd claim that “the EPA can arbitrarily dismiss pollution lawsuits against data centers.”

A U.S. federal judge ruled that the Trump administration lacked the authority to instruct the Environmental Protection Agency (EPA) to unilaterally dismiss an air pollution lawsuit against SpaceX AI over its operation of an unauthorized gas turbine in Memphis; the lawsuit filed by the NAACP will proceed.

VocabX: Reduces the inference cost of domain-specific small language models by 77% through cost-weighted vocabulary specialization.

Dharma-AI has introduced the VocabX method, which reconfigures the vocabulary of language models for specific tasks—such as automated scoring of Portuguese essays—by optimizing for cost rather than word frequency. Without modifying the model architecture, parameters, or training pipeline, empirical results show a 77% reduction in inference costs, while the evaluation score (bench score) improved from 0.703 to 0.810.

i14 releases the Laplace 3B linear attention model: inference with fixed-state memory, implemented on AWS Trainium2 and native PyTorch.

i14 has launched the Laplace 3B model with 3.028 billion parameters, which replaces Softmax attention with Gated Linear Attention (GLA). During inference, its per-sequence recursive state remains constant at 29.5 MB, independent of context length. The model was trained and validated on a single AWS trn2.48xlarge instance (equipped with 16 Trainium 2 chips), achieving a training throughput of 120,000 tokens per second and an inference throughput of 11,544 output tokens per second (with 1,280 concurrent sequences). All performance metrics are based on internal measurements conducted by i14 and the AWS GenAIIC team.

SF Compute builds a programmable data center: leveraging physical delivery to power the GPU computing power trading market.

SF Compute was founded by Evan Conrad and is dedicated to building a neutral, programmable GPU computing‑power trading infrastructure that supports physical delivery. Its model rejects the abstraction of FLOPS as a commodity, emphasizing hardware heterogeneity, actual delivery, and real‑time constraints—such as interrupt tolerance, InfiniBand topology, and firmware versions. The platform has already facilitated tens of billions of dollars in transaction volume and introduced low‑level capabilities like dynamic InfiniBand subnet partitioning.

SF Compute builds a programmable data center: leveraging physical delivery to drive the marketization of GPU computing power trading.

SF Compute was founded by Evan Conrad and focuses on enabling programmable, physically settled trading of GPU computing power. It does not operate its own data centers; instead, it leverages full-stack technology to collaborate with third-party facilities in co-building a “sovereign compute cloud,” emphasizing market neutrality and infrastructure controllability. The company has already facilitated hundreds of millions of dollars in transaction volume and implemented key capabilities such as dynamic InfiniBand partitioning.