#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
EVAA: Introducing Loop APY for LP Pool Interface
#Loop#EVAA
EVAA introduces a new Loop APY feature in its LP Pool Interface, enabling users to deposit LP tokens from StormTrade or DeDust as collateral, borrow TON or USDT, and utilize a liquidity looping strategy to potentially enhance annual returns. This strategy combines third-party yields, EVAA rates, and compounding effects.
Source: link
@tonlines
For operatori
Umuman olganda kod yozayotganingizda bir xil hisoblash jarayonini qayta-qayta yozish qimmatli vaqtingizni o'g'irlab sizni bezor qilishi mumkin, masalan siz “Salom, Dunyo!” jumlasini 100 marta yozishingiz zarur bo’lib qoldi.Siz uni qayta qayta yozib chiqgan bo’larmidingiz, yo’q albatta.
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👨🏫 Mentor: Suxrob Xayitmurodov
#csharp#for#loop#starter
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