#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
🪙 32,137 #BTC ($2.18 billion) went from the wallet of the #Mt․Gox exchange that collapsed in 2014 to an unknown address — the market reacted with a drop, suggesting that payments to the exchange's creditors could begin at any moment.
⚫️The Black Swan arrived unexpectedly... wait for new comments, despite the unpleasant surprise, the situation may become an opportunity to enter the market and make money on non-negative growth.
😙 The reasons for the fall of the # bitcoin exchange rate below $61,000
The unemployment rate was 4.3%, which is higher than expected, indicating a possible recession
The Bank of Japan raised the interest rate for the first time in 17 years, which led to an outflow of investments from risky assets
Increased geopolitical tensions (fear of a major world war)
😏Continued distribution of #BTC from #Mt.Gox and #Genesis