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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.

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llama.cpp releases version b11868: enhanced Hexagon NPU multi-core Flash Attention support, with cross-platform compilation and build capabilities.

llama.cpp v2.45.0 (tag b11868) has been officially released, with the core update being the introduction of a KV-head splitting mechanism in the Hexagon backend to optimize multi-core HMX Flash Attention performance and unify the FA splitting control flag. Binary packages are simultaneously available for all major platforms, including macOS, Linux, Windows, Android, iOS, and openEuler, supporting multiple backends such as CUDA 12/13, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon (Adreno + Hexagon).

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llama.cpp releases version b11867: server-side entry point refactored, with full support for multiple platforms and heterogeneous acceleration backends.

llama.cpp has officially released version b11867 on GitHub. Key changes include a refactored server entry point (PR #31113) and the addition of the server_role mechanism. Additionally, more than 40 precompiled binaries are now available, covering macOS, Linux, Windows, Android, iOS, and openEuler, with support for diverse hardware backends such as CPU, CUDA (12.4/12.8/13.4), Vulkan, ROCm 10.0, OpenVINO, SYCL (FP16/FP32), and Snapdragon (Adreno + Hexagon).

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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.

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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.

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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.

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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.

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