Experimental News Clipping Site

Tag: retrieval

  • Cloud Blog: How Mr. Cooper assembled a team of AI agents to handle complex mortgage questions

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/topics/financial-services/assembling-a-team-of-ai-agents-to-handle-complex-mortgage-questions-at-mr-cooper/ Source: Cloud Blog Title: How Mr. Cooper assembled a team of AI agents to handle complex mortgage questions Feedly Summary: In today’s world where instant responses and seamless experiences are the norm, industries like mortgage servicing face tough challenges. When navigating a maze of regulations, piles of financial documents, and the high…

  • Docker: Build and Distribute AI Agents and Workflows with cagent

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    Kurt Seifried
    in

    Source URL: https://www.docker.com/blog/cagent-build-and-distribute-ai-agents-and-workflows/ Source: Docker Title: Build and Distribute AI Agents and Workflows with cagent Feedly Summary: cagent is a new open-source project from Docker that makes it simple to build, run, and share AI agents, without writing a single line of code. Instead of writing code and wrangling Python versions and dependencies when creating…

  • Cloud Blog: Gemini and OSS text embeddings are now in BigQuery ML

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/use-gemini-and-open-source-text-embedding-models-in-bigquery/ Source: Cloud Blog Title: Gemini and OSS text embeddings are now in BigQuery ML Feedly Summary: High-quality text embeddings are the engine for modern AI applications like semantic search, classification, and retrieval-augmented generation (RAG). But when it comes to picking a model to generate these embeddings, we know one size doesn’t fit…

  • Simon Willison’s Weblog: Quoting James Luan

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    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Sep/8/james-luan/ Source: Simon Willison’s Weblog Title: Quoting James Luan Feedly Summary: I recently spoke with the CTO of a popular AI note-taking app who told me something surprising: they spend twice as much on vector search as they do on OpenAI API calls. Think about that for a second. Running the retrieval layer…

  • Simon Willison’s Weblog: Is the LLM response wrong, or have you just failed to iterate it?

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    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Sep/7/is-the-llm-response-wrong-or-have-you-just-failed-to-iterate-it/#atom-everything Source: Simon Willison’s Weblog Title: Is the LLM response wrong, or have you just failed to iterate it? Feedly Summary: Is the LLM response wrong, or have you just failed to iterate it? More from Mike Caulfield (see also the SIFT method). He starts with a fantastic example of Google’s AI mode…

  • Simon Willison’s Weblog: Quoting Jason Liu

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    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Sep/6/jason-liu/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Jason Liu Feedly Summary: I am once again shocked at how much better image retrieval performance you can get if you embed highly opinionated summaries of an image, a summary that came out of a visual language model, than using CLIP embeddings themselves. If you tell…

  • Schneier on Security: Indirect Prompt Injection Attacks Against LLM Assistants

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    Kurt Seifried
    in

    Source URL: https://www.schneier.com/blog/archives/2025/09/indirect-prompt-injection-attacks-against-llm-assistants.html Source: Schneier on Security Title: Indirect Prompt Injection Attacks Against LLM Assistants Feedly Summary: Really good research on practical attacks against LLM agents. “Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous” Abstract: The growing integration of LLMs into applications has introduced new security risks,…