Google DeepMind is developing an AI-powered mouse pointer that aims to transform user interaction by understanding context and intent. The initiative seeks to integrate AI seamlessly into existing workflows, allowing users to perform tasks like editing images or finding directions with simple gestures and voice commands. This innovation is part of DeepMind's broader vision to create more intuitive user interfaces, demonstrated through experimental demos powered by their Gemini AI system. The goal is to reduce the friction of switching between applications, making AI a more natural part of everyday digital interactions.
Read originalThe 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.
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.