#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
🚨 BREAKING: $117M in assets stolen from @Balancer in the last 2 hours after a major hack!!!
🔹 Assets stolen are across multiple chains: #Ethereum, #Base, #Optimism, #Sonic, #Polygon, #Berachain – mainly in Liquid Staking Tokens (LSTs) of $ETH.
Top 5 stolen assets:
• 7,838 $WETH (~$29.1M)
• 6,841 $OSETH (~$26.8M)
• 4,459 $WSTETH (~$20.1M)
• 2,405 $SFRXETH (~$10M)
• 2,038 $RSETH (~$8.67M)
🔹 The hacker is acting quickly: Converting LSTs into $ETH in real-time!
🔹 Big move: Whale account 0x009, dormant for 3 YEARS, just resurfaced after the exploit and withdrew $7.38M worth of assets from #Balancer!
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Follow @spotonchain for more updates about the hack!
https://x.com/spotonchain/status/1985289043383300351