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Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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UXLINK

@uxlink_community · Post #470 · 05/10/2025, 05:27 AM

最近の日本Web3界隈で目立つのが、UXLINKの存在感。🇯🇵🔍 CNPとの提携を皮切りに、日本ローカルの強力なIPとの協業が加速⚡ オンチェーン/オフチェーン両方でのコミュニティ展開に加え、 Web2企業とのクロスパートナー戦略も水面下で進行中🤝 “ユーザー起点のWeb3ソーシャル”という文脈で、 UXLINKは今、日本で一番面白い動きをしているかもしれない。🚀 #UXLINK#Web3JP#CNP#ソーシャルレイヤー#CommunityDriven One of the most quietly significant players gaining traction in Japan’s Web3 scene 🇯🇵👀#UXLINK Following its recent collaboration with CNP—a top domestic IP—UXLINK is making inroads across both native Web3 communities and mainstream Web2 circles 🤝 IRL activations, on-chain social dynamics, and a clear long-term strategy signal a serious Japan play 🎯 If you're tracking the rise of social infrastructure in Asia’s Web3 movement, this is one to watch. 📡 #UXLINK#Web3Japan#CommunityLayer#CNP#Web3Social