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Tag: Google model

  • Simon Willison’s Weblog: CompileBench: Can AI Compile 22-year-old Code?

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Sep/22/compilebench/ Source: Simon Willison’s Weblog Title: CompileBench: Can AI Compile 22-year-old Code? Feedly Summary: CompileBench: Can AI Compile 22-year-old Code? Interesting new LLM benchmark from Piotr Grabowski and Piotr Migdał: how well can different models handle compilation challenges such as cross-compiling gucr for ARM64 architecture? This is one of my favorite applications of…

  • Cloud Blog: 229 things we announced at Google Cloud Next 25 – a recap

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/topics/google-cloud-next/google-cloud-next-2025-wrap-up/ Source: Cloud Blog Title: 229 things we announced at Google Cloud Next 25 – a recap Feedly Summary: Google Cloud Next 25 took place this week and we’re all still buzzing! It was a jam-packed week in Las Vegas complete with interactive experiences, including more than 10 keynotes and spotlights, 700 sessions,…

  • Cloud Blog: Networking support for AI workloads

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/networking/cross-cloud-network-solutions-support-for-ai-workloads/ Source: Cloud Blog Title: Networking support for AI workloads Feedly Summary: At Google Cloud, we strive to make it easy to deploy AI models onto our infrastructure. In this blog we explore how the Cross-Cloud Network solution supports your AI workloads. Managed and Unmanaged AI options Google Cloud provides both managed (Vertex…

  • Cloud Blog: Optimizing RAG retrieval: Test, tune, succeed

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/optimizing-rag-retrieval/ Source: Cloud Blog Title: Optimizing RAG retrieval: Test, tune, succeed Feedly Summary: Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications. But RAG can be…

  • Cloud Blog: Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/learn-how-to-handle-429-resource-exhaustion-errors-in-your-llms/ Source: Cloud Blog Title: Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors Feedly Summary: Large language models (LLMs) give developers immense power and scalability, but managing resource consumption is key to delivering a smooth user experience. LLMs demand significant computational resources, which means it’s essential to…

  • Cloud Blog: Announcing Public Preview of Vertex AI Prompt Optimizer

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/announcing-vertex-ai-prompt-optimizer/ Source: Cloud Blog Title: Announcing Public Preview of Vertex AI Prompt Optimizer Feedly Summary: Prompt design and engineering stands out as one of the most approachable methods to drive meaningful output from a Large Language Model (LLM). ​​However, prompting large language models can feel like navigating a complex maze. You must experiment…