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Source channel @githubtrending · Post #14826 · Jun 12

#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data. https://github.com/FareedKhan-dev/all-rag-techniques

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Crypto M - Crypto News

@CryptoM · Post #65082 · 04/10/2026, 08:38 PM

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🚀 Scroll Users Face Excessive Transaction Fees Due to Multiplier Increases Scroll users incurred over $50,000 in additional transaction fees following six manual multiplier increases that elevated Layer 1 data charges to 1,280 times the original baseline. According to NS3.AI, L2BEAT reported that approximately 139,000 transactions were impacted over a span of roughly four days, with the baseline cost estimated at around $280. On April 9, the team reduced both multipliers by 160 times. Etherfi Cash bots contributed approximately $35,000 of the excess fees during etherfi's migration to Optimism. #Scroll#TransactionFees#MultiplierIncrease#Layer1#L2BEAT#NS3AI#Etherfi#Optimism#Crypto#Blockchain#SCR

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