Models & Labs
Anthropic Releases Claude Fable 5 with Safety Measures
WIRED AIhigh confidence
Why it matters
- →Anthropic's release strategy highlights the importance of balancing AI innovation with security concerns.
- →The introduction of guardrails in Claude Fable 5 reflects a cautious approach to public AI deployment.
- →Collaboration with industry partners and the US government underscores the model's potential impact on cybersecurity.
Anthropic has introduced two new AI models, Claude Fable 5 and Claude Mythos 5, with the latter being restricted to select industry partners due to its advanced capabilities. Claude Fable 5, available to the public, includes safety measures that redirect certain queries to an older model to prevent misuse. This cautious release strategy highlights Anthropic's commitment to balancing innovation with security. The company aims to refine these safety mechanisms over time, ensuring broader access without compromising cybersecurity.
Read originalMore from WIRED 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.
