Models & Labs
Google Enhances Search with AI-Powered Features
Google AI Bloghigh confidence
Why it matters
- →Google's integration of AI into Search represents a significant advancement in search technology.
- →The introduction of Search agents could redefine how users interact with and rely on search engines for task management.
- →These updates enhance the personalization and interactivity of Search, potentially setting a new standard for search engines.
Google has announced a major upgrade to its Search platform, integrating advanced AI features to enhance user interaction. The new Gemini 3.5 Flash model will power AI Mode, offering improved performance for search queries. A redesigned Search box now provides AI-driven suggestions and supports multimodal inputs, making it easier for users to find information. Additionally, Google is introducing Search agents that can autonomously manage tasks and provide real-time updates. These updates aim to make Search more intuitive and personalized, enhancing the overall user experience.
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llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
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llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.