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Tag: tokens

  • Slashdot: Over 3,200 Cursor Users Infected by Malicious Credential-Stealing npm Packages

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    by

    Kurt Seifried
    in

    Source URL: https://developers.slashdot.org/story/25/05/11/2222257/over-3200-cursor-users-infected-by-malicious-credential-stealing-npm-packages?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Over 3,200 Cursor Users Infected by Malicious Credential-Stealing npm Packages Feedly Summary: AI Summary and Description: Yes Summary: The text highlights a recent cybersecurity threat involving malicious npm (Node Package Manager) packages that target the AI-powered code-editing tool Cursor on macOS. The packages are designed to steal user credentials…

  • Cloud Blog: From LLMs to image generation: Accelerate inference workloads with AI Hypercomputer

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/compute/ai-hypercomputer-inference-updates-for-google-cloud-tpu-and-gpu/ Source: Cloud Blog Title: From LLMs to image generation: Accelerate inference workloads with AI Hypercomputer Feedly Summary: From retail to gaming, from code generation to customer care, an increasing number of organizations are running LLM-based applications, with 78% of organizations in development or production today. As the number of generative AI applications…

  • Simon Willison’s Weblog: Gemini 2.5 Models now support implicit caching

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/May/9/gemini-implicit-caching/#atom-everything Source: Simon Willison’s Weblog Title: Gemini 2.5 Models now support implicit caching Feedly Summary: Gemini 2.5 Models now support implicit caching I just spotted a cacheTokensDetails key in the token usage JSON while running a long chain of prompts against Gemini 2.5 Flash – despite not configuring caching myself: {“cachedContentTokenCount": 200658, "promptTokensDetails":…

  • Cloud Blog: New column-granularity indexing in BigQuery offers a leap in query performance

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    by

    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/data-analytics/search-indexes-with-column-granularity-in-bigquery/ Source: Cloud Blog Title: New column-granularity indexing in BigQuery offers a leap in query performance Feedly Summary: BigQuery delivers optimized search/lookup query performance by efficiently pruning irrelevant files. However, in some cases, additional column information is required for search indexes to further optimize query performance. To help, we recently announced indexing with…

  • Simon Willison’s Weblog: Feed a video to a vision LLM as a sequence of JPEG frames on the CLI (also LLM 0.25)

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    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/May/5/llm-video-frames/#atom-everything Source: Simon Willison’s Weblog Title: Feed a video to a vision LLM as a sequence of JPEG frames on the CLI (also LLM 0.25) Feedly Summary: The new llm-video-frames plugin can turn a video file into a sequence of JPEG frames and feed them directly into a long context vision LLM such…

  • Cloud Blog: Announcing new Vertex AI Prediction Dedicated Endpoints

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    by

    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/reliable-ai-with-vertex-ai-prediction-dedicated-endpoints/ Source: Cloud Blog Title: Announcing new Vertex AI Prediction Dedicated Endpoints Feedly Summary: For AI developers building cutting-edge applications with large model sizes, a reliable foundation is non-negotiable. You need your AI to perform consistently, delivering results without hiccups, even under pressure. This means having dedicated resources that won’t get bogged down…

  • Simon Willison’s Weblog: Dummy’s Guide to Modern LLM Sampling

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    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/May/4/llm-sampling/#atom-everything Source: Simon Willison’s Weblog Title: Dummy’s Guide to Modern LLM Sampling Feedly Summary: Dummy’s Guide to Modern LLM Sampling This is an extremely useful, detailed set of explanations by @AlpinDale covering the various different sampling strategies used by modern LLMs. LLMs return a set of next-token probabilities for every token in their…