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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 #64803 · 04/10/2026, 12:45 AM

🚀 Galaxy Digital Reports Annual Loss Amid Digital Asset Challenges Galaxy Digital has released its annual report, revealing a net loss of approximately $241 million for the year. According to Odaily, the loss was primarily due to unrealized losses in digital assets and investment positions. The adjusted figures indicate that the company achieved an EBITDA of around $216 million, with its core operations showing profitability under non-GAAP measures. The digital asset business, which includes trading, lending, asset management, and staking services, reported an adjusted gross profit of about $505 million. Following the release of the financial report, the company's stock price increased by over 11%. #GalaxyDigital#AnnualLoss#DigitalAssets#EBITDA#NetLoss#UnrealizedLosses#InvestmentPositions#DigitalAssetBusiness#Trading#Lending#AssetManagement#StakingServices#AdjustedGrossProfit#StockPriceIncrease