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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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Crypto M - Crypto News

@CryptoM · Post #65294 · 04/12/2026, 03:15 PM

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🚀 TRON Network Implements Proposal 106 to Enhance Compatibility TRON Network has officially passed Proposal 106 as of April 10 at 20:00 UTC+8. According to ChainCatcher, the mainnet has adjusted the behavior and execution cost of the SELFDESTRUCT instruction. Contracts will only be permanently removed from the blockchain if the instruction is called within the same transaction as the contract's creation. Otherwise, executing the instruction will merely transfer the assets within the contract to a specified address, without deleting the contract itself. Additionally, the energy consumption for SELFDESTRUCT has been changed from 0 to 5000. This adjustment aligns TRON's mechanisms more closely with Ethereum, enhancing the compatibility of the TRON Virtual Machine (TVM). It aims to provide more stable foundational support for multi-chain development and ecosystem adaptation. #TRON#Proposal106#Blockchain#Ethereum#TVM#SELFDESTRUCT#Mainnet#MultiChain#Ecosystem#Crypto#ETH#TRX

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