
Anthropic's Fable 5 has faced backlash due to its stringent safety constraints. Users have expressed dissatisfaction, arguing that these restrictions hinder the model's full potential and flexibility. The constraints are part of Anthropic's efforts to ensure safe AI deployment, but they have sparked debate over the balance between safety and usability.
Read original
© Matt WolfeLocal AI models are becoming viable alternatives to cloud-based models for everyday tasks, offering privacy and independence from major AI labs.
© Matt WolfeAnthropic has launched Fable 5, a new AI model with advanced capabilities.
© Matt WolfeUsers report that Fable 5 consumes a large number of tokens, raising concerns.
Claude Code's latest update introduces the Claude Fable 5, a Mythos-class model now safe for general use. This model surpasses previous offerings in capability, marking a significant step forward for developers using Claude Code. Additionally, the update resolves an issue with session transcripts not saving when launched from certain environments. This release enhances both the power and reliability of the Claude Code platform, offering developers a more robust toolset for their projects.
The latest b9590 release of llama.cpp addresses a critical issue where the LFM2 template handler was ignoring the json_schema from response_format, focusing solely on tool-calling grammar. This update ensures more robust handling of JSON schemas, which is crucial for developers relying on precise data formatting. The release also includes a variety of platform-specific builds, though some features like KleidiAI on macOS and SYCL on Windows remain disabled. This update is a step forward in refining the tool's functionality, particularly for those working with complex data structures.
The b9591 release of llama.cpp brings notable improvements to Multi-Task Processing (MTP) by removing padding and optimizing data handling. The update refines the ggml_gated_delta_net function, which now only requires the initial recurrent state and uses a snapshot count as an operational parameter, enhancing processing efficiency. These changes are implemented across all backends, addressing previous review comments and fixing CI build errors. With support for diverse hardware configurations, including macOS Apple Silicon, ROCm 7.2 on Ubuntu, and CUDA 12 and 13 on Windows, this release is a significant step forward for developers seeking improved performance and reliability.