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This article explains how to optimize content for Generative Engine Optimization (GEO) in the DeepSeek ecosystem, focusing on making content model-friendly through structured formats, consistent knowl

Ssebowa is an open-source Python library offering generative AI models for text, image, and video generation, including LLM, VLLM, image generation, and video generation. It supports fine-tuning with

WeChat has begun testing an AI-powered search feature integrating the DeepSeek-R1 model, offering a more diverse and intelligent search experience. The feature is currently in limited testing, pulling

The article analyzes the market shock caused by DeepSeek's competitive AI models, questioning the necessity of massive GPU infrastructure investments. It highlights DeepSeek's cost-efficient training

PLENA is a hardware-software co-designed system for LLM agentic inference that addresses bandwidth and capacity memory walls. It features a flattened systolic-array architecture, asymmetric quantization, and FlashAttention support, achieving up to 2.23x and 4.70x throughput improvements over A100 GPU and TPU v6e, respectively, and 4.04x better energy efficiency than A100. 原文翻译： PLENA是一个硬件-软件协同设计的系统，针对LLM代理推理，解决带宽和容量内存墙问题。它采用扁平化脉动阵列架构、非对称量化和FlashAttention支持，相比A100 GPU和TPU v6e，吞吐量分别提升2.23倍和4.70倍，能效比A100提升4.04倍。

This guide explores how Large Language Models (LLMs) can accelerate GPU kernel optimization, bridging the gap between research algorithms and production-ready implementations. It covers the computational foundations of matrix multiplication, the mathematical framework from Strassen to tensor decomposition, and DeepMind's evolution from AlphaTensor to AlphaEvolve, providing a technical roadmap for self-improving AI infrastructure. 原文翻译： 本指南探讨了大型语言模型（LLM）如何加速GPU内核优化，弥合研究算法与生产就绪实现之间的差距。它涵盖了矩阵乘法的计算基础、从Strassen到张量分解的数学框架，以及DeepMind从AlphaTensor到AlphaEvolve的演进历程，为自改进AI基础设施提供了技术路线图。

This article introduces a comprehensive GEO (Generative Engine Optimization) methodology, focusing on expert Yu Lei's 'Two Cores + Four Drivers' system. It evaluates multiple GEO approaches, provides

RAG-Anything is an open-source multimodal RAG framework developed by Professor Huang Chao's team at the University of Hong Kong. It builds a unified multimodal knowledge graph architecture to process

RAG-Anything is an all-in-one multimodal RAG system that processes documents containing text, images, tables, and formulas. It features end-to-end processing pipelines, knowledge graph indexing, and c

RAG-Anything is an all-in-one RAG framework developed by HKUDS at the University of Hong Kong, open-sourced on GitHub. It aims to enhance LLM performance by integrating retrieval and generation, addre