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Source channel @githubtrending · Post #14826 · Jun 12

#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

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DOFH - DevOps from hell

@dofh_ru · Post #3570 · 02/05/2025, 05:54 PM

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Here we go again! SEV-SNP is vulnerable, again. New AMD SEV-SNP vulnerability: https://github.com/google/security-research/security/advisories/GHSA-4xq7-4mgh-gp6w Exploit: https://github.com/google/security-research/tree/master/pocs/cpus/entrysign Reports about two recent vulnerabilities in SEV-SNP memory encryption and isolation mechanism, on CPU pipeline, cache and branch prediction level: https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3019.html https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3010.html AMD reported that previous approaches to Spectre class attacks will work to fix new vulnerabilities: https://www.amd.com/content/dam/amd/en/documents/epyc-technical-docs/tuning-guides/software-techniques-for-managing-speculation.pdf #cVM #TEE #SEV #SNP #SEV_SNP #AMD

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