Tag: caching
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The Cloudflare Blog: Quicksilver v2: evolution of a globally distributed key-value store (Part 2)
Source URL: https://blog.cloudflare.com/quicksilver-v2-evolution-of-a-globally-distributed-key-value-store-part-2-of-2/ Source: The Cloudflare Blog Title: Quicksilver v2: evolution of a globally distributed key-value store (Part 2) Feedly Summary: This is part two of a story about how we overcame the challenges of making a complex system more scalable. AI Summary and Description: Yes Summary: The text describes the evolution of Cloudflare’s Quicksilver,…
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The Cloudflare Blog: Quicksilver v2: evolution of a globally distributed key-value store (Part 1)
Source URL: https://blog.cloudflare.com/quicksilver-v2-evolution-of-a-globally-distributed-key-value-store-part-1/ Source: The Cloudflare Blog Title: Quicksilver v2: evolution of a globally distributed key-value store (Part 1) Feedly Summary: This blog post is the first of a series, in which we share our journey in redesigning Quicksilver — Cloudflare’s distributed key-value store that serves over 3 billion keys per second globally. AI Summary…
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Cloud Blog: From news to insights: Glance leverages Google Cloud to build a Gemini-powered Content Knowledge Graph (CKG)
Source URL: https://cloud.google.com/blog/topics/customers/glance-builds-gemini-powered-knowledge-graph-with-google-cloud/ Source: Cloud Blog Title: From news to insights: Glance leverages Google Cloud to build a Gemini-powered Content Knowledge Graph (CKG) Feedly Summary: In today’s hyperconnected world, delivering personalized content at scale requires more than just aggregating information – it demands deep understanding of context, relationships, and user preferences. Glance, a leading content…
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Tomasz Tunguz: The Surprising Input-to-Output Ratio of AI Models
Source URL: https://www.tomtunguz.com/input-output-ratio/ Source: Tomasz Tunguz Title: The Surprising Input-to-Output Ratio of AI Models Feedly Summary: When you query an AI model, it gathers relevant information to generate an answer. For a while, I’ve wondered : how much information does the model need to answer a question? I thought the output would be larger, however…