
Anthropic has launched Fable 5.1, a new version of its advanced AI model, which is now available on cloud platforms and through the Anthropic API. This release focuses on reducing token costs and easing restrictions, making it more accessible to users. A key feature is the zero data retention policy, allowing clients to run models on their own infrastructure without data outflows. The model has set new records in performance benchmarks and includes novel scientific findings. Anthropic assures that user data remains secure and has not been accessed without permission.
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© TechCrunch AIAfterQuery, an AI training-data startup, has rapidly ascended to a $3.2 billion valuation, marking it as Y Combinator's quickest unicorn. This leap comes just five months after its $30 million Series A, showcasing a dramatic increase in market value. The company specializes in training AI models to replicate professional-level decision-making, attracting major clients like Nvidia and Motif Technologies. This swift rise in valuation reflects the growing interest in AI solutions capable of performing complex tasks with expert precision.
© TechCrunch AIOpenAI is preparing to release its Astra model, which it claims is the first large language model to meet a critical cybersecurity threshold. Astra has demonstrated the ability to find and exploit unknown security flaws autonomously, raising both excitement and concern. OpenAI is taking precautions by limiting access to its advanced capabilities and implementing new safety measures to prevent misuse. While Astra scored perfectly on ExploitBench, its real-world impact remains to be seen as OpenAI plans further evaluations and safety disclosures upon its public release.
© TechCrunch AIGoogle is stepping into the creative design arena with Google Pics, an AI-driven tool integrated into its Workspace suite. Unlike traditional design platforms like Canva, Google Pics leverages AI to generate images based on user prompts, rather than relying on pre-made templates or designs. This tool is powered by Google's Nano Banana image-generation model and offers features like object isolation, text modification, and collaborative editing. Initially available in Google Docs and Slides, it aims to streamline everyday design tasks for business and premium AI subscribers, marking a shift towards AI-assisted creativity in professional settings.
The b10739 release of llama.cpp brings targeted performance improvements for Apple's M2 Max, with fa-vec tuning specifically designed for its 30 GPU cores. This update aims to boost efficiency in AI processing tasks, making the most of Apple's latest hardware capabilities. While the KleidiAI feature for Apple Silicon remains disabled, the release continues to support a wide array of systems, including macOS, Linux, and Windows. The inclusion of ROCm 7.14 and CUDA 12 and 13 DLLs further extends its reach. This update marks a significant enhancement in llama.cpp's ability to adapt to different hardware environments, offering developers improved performance and flexibility.
The b10741 release of llama.cpp brings a key improvement in the model loading process by adjusting the order of parameter loading, specifically loading hparams.n_layer_nextn before n_layer() calls. This change aims to streamline initialization and eliminate redundant operations, enhancing efficiency. While no new model architectures are introduced, the update supports a wide range of hardware configurations, including macOS, Linux, and Windows systems. With support for ROCm 7.14 and CUDA 13, developers can expect a more robust runtime environment. This release continues llama.cpp's focus on refining its operations, making it a more efficient tool for developers working with diverse hardware setups.
The latest b10742 release of llama.cpp continues its trend of broadening platform compatibility, now including support for a wide array of systems such as Ubuntu with Vulkan and ROCm 7.14, as well as Windows with CUDA 13. This update doesn't introduce new models but focuses on enhancing the runtime environment across diverse hardware configurations. By enabling Vulkan and ROCm support, llama.cpp is making strides in offering more flexible deployment options for developers. This release demonstrates llama.cpp's commitment to being a versatile inference runtime, catering to both AMD and NVIDIA users.