Experimental News Clipping Site

Tag: accuracy

  • AWS News Blog: Get insights from multimodal content with Amazon Bedrock Data Automation, now generally available

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    Kurt Seifried
    in

    Source URL: https://aws.amazon.com/blogs/aws/get-insights-from-multimodal-content-with-amazon-bedrock-data-automation-now-generally-available/ Source: AWS News Blog Title: Get insights from multimodal content with Amazon Bedrock Data Automation, now generally available Feedly Summary: Amazon Bedrock Data Automation streamlines the extraction of valuable insights from unstructured multimodal content (documents, images, audio, and videos) by providing a simplified way to build intelligent document processing and media analysis…

  • Cloud Blog: How to calculate your AI costs on Google Cloud

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/topics/cost-management/unlock-the-true-cost-of-enterprise-ai-on-google-cloud/ Source: Cloud Blog Title: How to calculate your AI costs on Google Cloud Feedly Summary: What is the true cost of enterprise AI? As a technology leader and a steward of company resources, understanding these costs isn’t just prudent – it’s essential for sustainable AI adoption. To help, we’ll unveil a comprehensive…

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

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    Kurt Seifried
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    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: SOTA Code Retrieval with Efficient Code Embedding Models

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    Kurt Seifried
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    Source URL: https://www.qodo.ai/blog/qodo-embed-1-code-embedding-code-retreival/ Source: Hacker News Title: SOTA Code Retrieval with Efficient Code Embedding Models Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces Qodo-Embed-1, a new family of code embedding models that outperforms larger models in code retrieval tasks while maintaining a smaller footprint. It emphasizes the challenges existing models face…

  • Hacker News: Doing data labelling in-house

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    Kurt Seifried
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    Source URL: https://www.ericbutton.co/p/data-labelling Source: Hacker News Title: Doing data labelling in-house Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the development of Yeager, a state-of-the-art AI model for air traffic control audio, and the significant effort put into in-house data labeling. It emphasizes the unique challenges of working with highly specialized…

  • Simon Willison’s Weblog: Notes from my Accessibility and Gen AI podcast appearence

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Mar/2/accessibility-and-gen-ai/#atom-everything Source: Simon Willison’s Weblog Title: Notes from my Accessibility and Gen AI podcast appearence Feedly Summary: I was a guest on the most recent episode of the Accessibility + Gen AI Podcast, hosted by Eamon McErlean and Joe Devon. We had a really fun, wide-ranging conversation about a host of different topics.…

  • Simon Willison’s Weblog: Quoting Kellan Elliott-McCrea

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Mar/2/kellan-elliott-mccrea/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Kellan Elliott-McCrea Feedly Summary: Regarding the recent blog post, I think a simpler explanation is that hallucinating a non-existent library is a such an inhuman error it throws people. A human making such an error would be almost unforgivably careless. — Kellan Elliott-McCrea Tags: ai-assisted-programming, generative-ai,…

  • Simon Willison’s Weblog: Hallucinations in code are the least dangerous form of LLM mistakes

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Mar/2/hallucinations-in-code/#atom-everything Source: Simon Willison’s Weblog Title: Hallucinations in code are the least dangerous form of LLM mistakes Feedly Summary: A surprisingly common complaint I see from developers who have tried using LLMs for code is that they encountered a hallucination – usually the LLM inventing a method or even a full software library…

  • Hacker News: 3x Improvement with Infinite Retrieval: Attention Enhanced LLMs in Long-Context

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    Kurt Seifried
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    Source URL: https://arxiv.org/abs/2502.12962 Source: Hacker News Title: 3x Improvement with Infinite Retrieval: Attention Enhanced LLMs in Long-Context Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel approach called InfiniRetri, which enhances long-context processing capabilities of Large Language Models (LLMs) by utilizing their own attention mechanisms for improved retrieval accuracy. This…

  • Hacker News: The Dino, the Llama, and the Whale (Deno and Jupyter for Local AI Experiments)

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    Kurt Seifried
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    Source URL: https://deno.com/blog/the-dino-llama-and-whale Source: Hacker News Title: The Dino, the Llama, and the Whale (Deno and Jupyter for Local AI Experiments) Feedly Summary: Comments AI Summary and Description: Yes Summary: The text outlines the author’s journey in experimenting with a locally hosted large language model (LLM) using various tools such as Deno, Jupyter Notebook, and…