Kerim Kaya (kerimkaya)

We witnessed one of the largest on-chain transaction ever - 10 million vector storage and search queries in just 24 hours. Entire Wikipedia as Smart Contract Public RAG model, Embeddings Lives On-Chain.

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With all the LLM stack, it is easier than ever to build dynamic interest-based feeds, and super accurate semantic search. It's time to build next-generation search and feeds on Farcaster with the increase in content. What would you like to see included in Farcaster feeds and searches?

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Do you think LLMs have a deep understanding of Ethereum? The data says the accuracy rate of even the top LLMs is super low. We’re creating a monumental Crypto Corpus for any LLM to keep up with Ethereum daily. ( Will be available on Hugging Face) Ping me if you want to contribute

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Last week at #EthCC , we introduced HollowDB, a game changer for privacy-preserving & decentralized LLMCache, ID management, and session storage. - Lightning-Fast Speeds - Permanent Storage at the lowest cost - Custom ZK Circuits - Multiple Clients Read: hollowdb.super.site

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Great work! Where do you store embeddings, and what vdb you’re using for RAG?

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Would love to help with anything ( working on language models since 2015)

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