Models & Labs
Microsoft Unveils AI Models and Agentic Hardware at Build 2026
The Rundown AIhigh confidence
Why it matters
- →Microsoft's new AI models and agentic hardware mark a shift towards independence from OpenAI.
- →The Majorana 2 quantum chip could accelerate the timeline for practical quantum computing.
- →Microsoft's developments position its platforms as central to the future of agentic AI.
At Build 2026, Microsoft unveiled a suite of AI advancements, including seven new in-house models and its first always-on agent, Microsoft Scout. Built on OpenClaw, Scout operates within Teams to automate tasks like scheduling. The company also showcased the Majorana 2 quantum chip, which boasts a 1,000x reliability improvement, hinting at faster quantum computing progress. These moves signal Microsoft's intent to lead in AI innovation, positioning its platforms as key players in the agentic AI era.
Read originalMore from The Rundown AI
More 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.