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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 #371 · 03/28/2025, 03:19 AM

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🚀 Exciting updates: UXLINK One account growth has exploded from 160K to 500,000+ in under 10 days! 💥 💰 Over 6,000,000 $UXLINK deposited as OAOG protocol’s universal gas 📈 Daily transactions: 10,000+ | Total transactions: ~1,000,000 This is what global community power looks like! 🌍✨ Thank you for trusting us to redefine #Web3. Chain & account abstraction are revolutionizing user experiences—smoother, safer, and simpler for everyone. As the world’s largest #Web3 social platform & infrastructure, we’re building the SOCIAL FOUNDATION for mass adoption. 💪The future is here—let’s shape it together! 🎉#UXLINK#MassAdoption#Web3Infrastructure#AccountAbstraction

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EdgeMarket.AI 📣

@edgemarketai · Post #8002 · 02/23/2026, 02:09 PM

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By February 23, the story will already be written. EdgeMarket explores how decentralised oracles, collective intelligence, and AI signals converge to forecast global outcomes — transparently, without manipulation. Prediction is infrastructure. Truth is a system. #EdgeMarket#DecentralisedOracles#CollectiveIntelligence#AIInference#OlympicData#FutureOfPrediction#Web3Infrastructure

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