
Google has introduced three new models to its Gemini lineup, focusing on speed and cybersecurity, but the anticipated 3.5 Pro model remains unreleased. The new models include the 3.6 Flash, which offers efficiency improvements but still falls short compared to rivals like Grok 4.5 and GPT-5.6 Luna. Google's delay in releasing the 3.5 Pro model raises questions about its competitive position in the AI market. Meanwhile, the company is advancing with its Gemini 4 pre-training, described as its most ambitious yet.
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© The Rundown AIClaude AI has achieved a remarkable feat by solving the Jacobian conjecture, a mathematical enigma that has confounded experts since 1939. This was accomplished through a succinct one-line formula shared by Anthropic's Levent Alpöge on social media, making it easy for the mathematical community to verify. Previous attempts to solve this problem have failed, making this development particularly noteworthy. The ability of AI to tackle such complex challenges suggests a future where AI could similarly transform fields like medicine and engineering. This breakthrough is a testament to the evolving capabilities of AI in advancing scientific research and problem-solving.
© The Rundown AIAnthropic has resolved the uncertainty surrounding its Fable 5 model by deciding to keep it available to subscribers. This decision comes after a period of shifting deadlines and user frustration, with the model now accessible under Max and Team Premium plans but with reduced usage caps. Lower-tier plans will receive a one-time $100 credit before transitioning to a pay-per-use model. The move is a response to competitive pressures from OpenAI's GPT-5.6 Sol and Moonshot's Kimi K3, both of which are expanding their offerings. While this provides some clarity for users, it also points to the ongoing challenges Anthropic faces in balancing demand and access to its AI models.
The latest b10083 release of llama.cpp continues its trend of broadening platform compatibility, making it a versatile choice for developers across different systems. Notably, this update includes support for Ubuntu with ROCm 7.2, enhancing performance for AMD GPU users. Windows users benefit from updated CUDA support, with DLLs for both CUDA 12.4 and 13.3, ensuring compatibility with the latest NVIDIA technologies. While no groundbreaking new features are introduced, the release solidifies llama.cpp's position as a flexible inference runtime across diverse hardware setups.
The latest b10085 release of llama.cpp addresses a key issue with the Qwen3-VL vision model's position embedding interpolation. By aligning the interpolation method with the transformers reference, the update ensures more accurate grounding coordinates, particularly for larger and non-square images. This change is crucial for developers working with image processing tasks, as it reduces discrepancies in image scaling. While the update doesn't introduce new models, it enhances the precision of existing functionalities, making llama.cpp a more reliable tool for AI developers.
The latest b10089 release of llama.cpp significantly enhances CUDA support by adding k-quant and i-quant capabilities to the GET_ROWS function. This update allows for more efficient device-side embedding lookups, reducing the need to fallback to the host and improving performance for single-device graphs. By factoring out super-block dequantizers into shared device functions, the release ensures that all quantized GGML types can now take the direct device path. This marks a notable improvement in CUDA's handling of quantized data, making it more robust and efficient.