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Tutorial#kintone📝 BlogAnalyzed: Dec 24, 2025 19:42

Accessing Multiple kintone Environments with Claude Desktop

Published:Dec 22, 2025 14:34
1 min read
Zenn Claude

Analysis

This article discusses how to use Claude Desktop to access multiple kintone environments, addressing the limitation of the official kintone local MCP server which, by default, only allows configuration for one environment's authentication information. This is particularly useful for users who work with multiple kintone domains for business or personal learning. The article highlights the inconvenience of having to provide instructions for each environment separately and proposes Claude Desktop as a solution. It's a practical guide for kintone users looking to streamline their workflow when dealing with multiple instances of the platform, leveraging the capabilities of generative AI tools compatible with the MCP server.
Reference

“kintone's official local MCP server has been announced.”

PermalinkZenn Claude
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:06

FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptation

Published:Dec 10, 2025 13:06
1 min read
ArXiv

Analysis

This article introduces FROMAT, a novel approach for transferring material appearance across multiple views using few-shot learning and self-attention mechanisms. The research likely focuses on improving the realism and efficiency of material transfer in computer graphics and related fields. The use of 'few-shot' suggests an emphasis on learning from limited data, which is a key area of research in AI.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#MLLM🔬 ResearchAnalyzed: Jan 10, 2026 13:49

    ESMC: MLLM-Driven Embedding Selection for Explainable Clustering

    Published:Nov 30, 2025 04:36
    1 min read
    ArXiv

    Analysis

    This ArXiv paper explores the use of Multilingual Large Language Models (MLLMs) for improving the explainability of multiple clustering tasks. The approach, ESMC, focuses on selecting embeddings to enhance understanding of cluster formation.
    Reference

    “ESMC leverages MLLMs for embedding selection.”

    PermalinkArXiv