Models & Labs
Google Unveils Gemini Omni and Gemini 3.5 Models
Google AI Bloghigh confidence
Why it matters
- →Gemini Omni revolutionizes video editing by allowing natural language interactions.
- →Gemini 3.5 Flash enhances agentic task performance, crucial for complex workflows.
- →Integration across Google platforms brings advanced AI capabilities to everyday users.
Google has introduced its latest AI models, Gemini Omni and Gemini 3.5, at Google I/O 2026. Gemini Omni enables users to create and edit videos through conversational commands, offering a new way to interact with multimedia content. Gemini 3.5 Flash focuses on agentic tasks and coding, providing advanced capabilities for complex workflows. These models are being integrated across Google's platforms, including Search and the Gemini app, to enhance user experiences with personalized AI agents and interactive tools. This development underscores Google's commitment to advancing AI technology in practical applications.
Read originalMore in Models & Labs
Models & Labsmodels
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
Models & Labsmodels
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.