Experimental News Clipping Site

Tag: Chunking

  • Tomasz Tunguz: How AI Tools Differ from Human Tools

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    Kurt Seifried
    in

    Source URL: https://www.tomtunguz.com/tools-evolution/ Source: Tomasz Tunguz Title: How AI Tools Differ from Human Tools Feedly Summary: Now that we’ve compressed nearly all human knowledge into large language models, the next frontier is tool calling. Chaining together different AI tools enables automation. The shift from thinking to doing represents the real breakthrough in AI utility. I’ve…

  • Cloud Blog: How to enable real time semantic search and RAG applications with Dataflow ML

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/topics/developers-practitioners/create-and-retrieve-embeddings-with-a-few-lines-of-dataflow-ml-code/ Source: Cloud Blog Title: How to enable real time semantic search and RAG applications with Dataflow ML Feedly Summary: Embeddings are a cornerstone of modern semantic search and Retrieval Augmented Generation (RAG) applications. In short, they enable applications to understand and interact with information on a deeper, conceptual level. In this post,…

  • Cloud Blog: Introducing BigQuery ObjectRef: Supercharge your multimodal data and AI processing

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/data-analytics/new-objectref-data-type-brings-unstructured-data-into-bigquery/ Source: Cloud Blog Title: Introducing BigQuery ObjectRef: Supercharge your multimodal data and AI processing Feedly Summary: Traditional data warehouses simply can’t keep up with today’s analytics workloads. That’s because today, most data that’s generated is both unstructured and multimodal (documents, audio files, images, and videos). With the complexity of cleaning and transforming…

  • Hacker News: Chunking Attacks on File Backup Services Using Content-Defined Chunking [pdf]

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    Kurt Seifried
    in

    Source URL: https://www.daemonology.net/blog/chunking-attacks.pdf Source: Hacker News Title: Chunking Attacks on File Backup Services Using Content-Defined Chunking [pdf] Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text details various parameter-extraction attacks on file backup services utilizing content-defined chunking (CDC) techniques. The authors explore vulnerabilities associated with the use of user-specific secret parameters in CDC…

  • Cloud Blog: Use Gemini 2.0 to speed up document extraction and lower costs

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/use-gemini-2-0-to-speed-up-data-processing/ Source: Cloud Blog Title: Use Gemini 2.0 to speed up document extraction and lower costs Feedly Summary: A few weeks ago, Google DeepMind released Gemini 2.0 for everyone, including Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, and Gemini 2.0 Pro (Experimental). All models support up to at least 1 million input tokens, which…

  • Hacker News: Yek: Serialize your code repo (or part of it) to feed into any LLM

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    Kurt Seifried
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    Source URL: https://github.com/bodo-run/yek Source: Hacker News Title: Yek: Serialize your code repo (or part of it) to feed into any LLM Feedly Summary: Comments AI Summary and Description: Yes **Short Summary with Insight:** The text presents a Rust-based tool called “yek” that automates the process of reading, chunking, and serializing text files within a repository…

  • Cloud Blog: Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/introducing-vertex-ai-rag-engine/ Source: Cloud Blog Title: Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence Feedly Summary: Closing the gap between impressive model demos and real-world performance is crucial for successfully deploying generative AI for enterprise. Despite the incredible capabilities of generative AI for enterprise, this perceived gap may be…

  • 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…

  • Hacker News: Roaming RAG – Make the Model Find the Answers

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    system automation
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    Source URL: http://arcturus-labs.com/blog/2024/11/21/roaming-rag–make-_the-model_-find-the-answers/ Source: Hacker News Title: Roaming RAG – Make the Model Find the Answers Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text presents a novel approach called “Roaming RAG,” which simplifies the retrieval-augmented generation (RAG) model by allowing a large language model (LLM) to directly navigate well-structured documents without the…

  • Cloud Blog: Vertex AI grounding: More reliable models, fewer hallucinations

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/how-vertex-ai-grounding-helps-build-more-reliable-models/ Source: Cloud Blog Title: Vertex AI grounding: More reliable models, fewer hallucinations Feedly Summary: At the Gemini for Work event in September, we showcased how generative AI is transforming the way enterprises work. Across all the customer innovation we saw at the event, one thing was clear – if last year was…