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veekaybee.github.io | ||
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blacklight.sh
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| | | | | RAG isn't about vector databases and embeddings, or any specific architecture. It's about retrieving relevant context well. | |
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weaviate.io
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| | | | | Learn what a vector database is and how it powers vector search, semantic search, and LLM RAG with embeddings, indexing (HNSW/ANN), and scalable retrieval. | |
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vickiboykis.com
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| | | | | [AI summary] The text provides an in-depth account of the development and deployment of a semantic search application called Viberary, focusing on its design, challenges, and lessons learned. The author details the entire process, from initial data exploration and model selection to production deployment using Docker, DigitalOcean, and load balancing. Key takeaways include the importance of a testable prototype, careful hyperparameter management, and the benefits of using DigitalOcean over larger cloud providers. The text also highlights the difficulty of semantic search and the need for algorithmic fine-tuning across machine learning, UI, and deployment processes. The author emphasizes reproducible environments, efficient Docker practices, and the value of ... | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr | ||
