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Tag: processing

  • Slashdot: Apple Lets Developers Tap Into Its Offline AI Models

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    Kurt Seifried
    in

    Source URL: https://apple.slashdot.org/story/25/06/09/1859253/apple-lets-developers-tap-into-its-offline-ai-models?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Apple Lets Developers Tap Into Its Offline AI Models Feedly Summary: AI Summary and Description: Yes Summary: Apple is set to introduce the Foundation Models framework, enabling developers to leverage its AI models on devices while ensuring user privacy and reducing cloud dependency. This innovation allows for personalized experiences…

  • Cloud Blog: Unlock 66% better price-performance with new M4 VMs for memory-intensive workloads

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/compute/m4-vms-are-designed-for-memory-intensive-workloads-like-sap/ Source: Cloud Blog Title: Unlock 66% better price-performance with new M4 VMs for memory-intensive workloads Feedly Summary: Today, we’re excited to announce the general availability of the memory-optimized machine series: Compute Engine M4, our most performant memory-optimized VM with under 6TB of memory.  The M4 family is designed for workloads like SAP…

  • Cloud Blog: Simplify your streaming pipelines with new Pub/Sub Single Message Transforms

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/pub-sub-single-message-transforms/ Source: Cloud Blog Title: Simplify your streaming pipelines with new Pub/Sub Single Message Transforms Feedly Summary: Today, we’re introducing Pub/Sub Single Message Transforms (SMTs) to make it easy to perform simple data transformations right within Pub/Sub itself. This comes at a time when businesses are increasingly reliant on streaming data to derive…

  • Simon Willison’s Weblog: Qwen3 Embedding

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Jun/8/qwen3-embedding/#atom-everything Source: Simon Willison’s Weblog Title: Qwen3 Embedding Feedly Summary: Qwen3 Embedding New family of embedding models from Qwen, in three sizes: 0.6B, 4B, 8B – and two categories: Text Embedding and Text Reranking. The full collection can be browsed on Hugging Face. The smallest available model is the 0.6B Q8 one, which…

  • Cloud Blog: Multimodal agents tutorial: How to use Gemini, Langchain, and LangGraph to build agents for object detection

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/build-multimodal-agents-using-gemini-langchain-and-langgraph/ Source: Cloud Blog Title: Multimodal agents tutorial: How to use Gemini, Langchain, and LangGraph to build agents for object detection Feedly Summary: Here’s a common scenario when building AI agents that might feel confusing: How can you use the latest Gemini models and an open-source framework like LangChain and LangGraph to create…

  • Cloud Blog: Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms report

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/gartner-2025-magic-quadrant-for-data-science-and-ml-platforms/ Source: Cloud Blog Title: Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms report Feedly Summary: Today, we are excited to announce that Gartner® has named Google as a Leader in the 2025 Magic Quadrant™ for Data Science and Machine Learning Platforms report (DSML).…

  • Cloud Blog: From analytics to data management: New BigQuery transactional features

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/bigquery-features-for-transactional-data-management/ Source: Cloud Blog Title: From analytics to data management: New BigQuery transactional features Feedly Summary: For years, BigQuery has been synonymous with fully managed, fast, petabyte-scale analytics. Its columnar architecture and decoupled storage and compute have made it the go-to data warehouse for deriving insights from massive datasets.  But what about the…