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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 #64877 · 04/10/2026, 06:14 AM

🚀 Starknet Opens Voting on v0.14.2 Upgrade Proposal Starknet has announced that voting is now open for STRK holders on the v0.14.2 upgrade proposal. According to Foresight News, the voting period will end on April 16. The proposed upgrade includes the introduction of SNIP-36, SNIP-37, and SNIP-13, and aims to enable protocol-level S-Two proof verification, significantly reducing costs for privacy use cases and zk-thread applications. Additionally, the upgrade seeks to adjust the storage economic model to more accurately reflect network resource usage while maintaining cost advantages for developers. The StarkGate token contract will also be upgraded to optimize ERC-20 event indexing, verification, and token handling. Other internal upgrades are planned to prepare for the decentralized verification phase described in SNIP-33, including improvements in P2P, weighted proposer sampling, and the concept of virtual proposers. #Starknet#STRK#v0.14.2 #SNIP36#SNIP37#SNIP13#STwoProof#PrivacyUseCases#zkThread#StorageEconomicModel#StarkGate#ERC20#P2P#DecentralizedVerification#SNIP33#VirtualProposers