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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 #65047 · 04/10/2026, 04:10 PM

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🚀 Whop Introduces Treasury Yield Product Following Tether Investment Whop has launched its Treasury yield product on March 25, following a significant investment from Tether in February, which valued the company at $1.6 billion. According to NS3.AI, the product was introduced after Tether's $200 million investment. Steven Schwartz noted that 3% of users engaged with the beta version within a week, despite the absence of a marketing campaign. The product channels funds through a Veda vault on Plasma into Aave lending markets, offering an annual percentage yield (APY) of up to 6%. The investment from Tether will enable Whop to integrate on-platform USDT wallets and payment options. #Whop#Tether#TreasuryYield#Investment#Crypto#APY#Aave#USDT#Fintech#Blockchain#AAVE

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