Experimental News Clipping Site

Tag: image analysis

  • Docker: Docker and CNCF: Partnering to Power the Future of Open Source

    —

    by

    Kurt Seifried
    in

    Source URL: https://www.docker.com/blog/docker-cncf-partnership/ Source: Docker Title: Docker and CNCF: Partnering to Power the Future of Open Source Feedly Summary: At Docker, open source is not just something we support; it’s a core part of our culture. It’s part of our DNA. From foundational projects like Docker Compose (35.5k stars, 5.4k forks) and Moby (69.8k stars,…

  • Slashdot: Apple’s Upgraded AI Models Underwhelm On Performance

    —

    by

    Kurt Seifried
    in

    Source URL: https://apple.slashdot.org/story/25/06/10/1646256/apples-upgraded-ai-models-underwhelm-on-performance?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Apple’s Upgraded AI Models Underwhelm On Performance Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the performance of Apple’s recent AI models in comparison to competitors, revealing that they lag behind those from Google, Alibaba, OpenAI, and Meta. This assessment has implications for the company’s position…

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

    —

    by

    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: Introducing AI.GENERATE_TABLE: creating structured data from gen AI models in BigQuery

    —

    by

    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/convert-ai-generated-unstructured-data-to-a-bigquery-table/ Source: Cloud Blog Title: Introducing AI.GENERATE_TABLE: creating structured data from gen AI models in BigQuery Feedly Summary: The explosion of digital content from social media, smartphones, and other sources has created a massive amount of unstructured data like images, videos, and documents. To help you analyze this data, BigQuery is connected with…

  • Simon Willison’s Weblog: qwen2.5vl in Ollama

    —

    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/May/18/qwen25vl-in-ollama/#atom-everything Source: Simon Willison’s Weblog Title: qwen2.5vl in Ollama Feedly Summary: qwen2.5vl in Ollama Ollama announced a complete overhaul of their vision support the other day. Here’s the first new model they’ve shipped since then – a packaged version of Qwen 2.5 VL which was first released on January 26th 2025. Here are…

  • Cloud Blog: Expanding BigQuery geospatial capabilities with Earth Engine raster analytics

    —

    by

    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/a-closer-look-at-earth-engine-in-bigquery/ Source: Cloud Blog Title: Expanding BigQuery geospatial capabilities with Earth Engine raster analytics Feedly Summary: At Google Cloud Next 25, we announced a major step forward in geospatial analytics: Earth Engine in BigQuery. This new capability unlocks Earth Engine raster analytics directly in BigQuery, making advanced analysis of geospatial datasets derived from…

  • Simon Willison’s Weblog: Quoting Eliot Higgins, Bellingcat

    —

    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Apr/26/elliot-higgins/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Eliot Higgins, Bellingcat Feedly Summary: We’ve been seeing if the latest versions of LLMs are any better at geolocating and chronolocating images, and they’ve improved dramatically since we last tested them in 2023. […] Before anyone worries about it taking our job, I see it more…

  • Simon Willison’s Weblog: Image segmentation using Gemini 2.5

    —

    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Apr/18/gemini-image-segmentation/ Source: Simon Willison’s Weblog Title: Image segmentation using Gemini 2.5 Feedly Summary: Max Woolf pointed out this new feature of the Gemini 2.5 series in a comment on Hacker News: One hidden note from Gemini 2.5 Flash when diving deep into the documentation: for image inputs, not only can the model be…

  • Slashdot: ChatGPT Models Are Surprisingly Good At Geoguessing

    —

    by

    Kurt Seifried
    in

    Source URL: https://yro.slashdot.org/story/25/04/17/1941258/chatgpt-models-are-surprisingly-good-at-geoguessing?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: ChatGPT Models Are Surprisingly Good At Geoguessing Feedly Summary: AI Summary and Description: Yes Summary: The text discusses a concerning trend related to the use of OpenAI’s new models, o3 and o4-mini, for deducing locations from images, raising potential privacy issues. The models’ advanced image analysis capabilities combined with…

  • Hacker News: Gemma3 – The current strongest model that fits on a single GPU

    —

    by

    Kurt Seifried
    in

    Source URL: https://ollama.com/library/gemma3 Source: Hacker News Title: Gemma3 – The current strongest model that fits on a single GPU Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the features and capabilities of the Gemma 3 models developed by Google, which are built on Gemini technology and designed for multimodal tasks. Their…