
NestAI, an AI lab founded by Peter Sarlin, has launched its first AI models designed specifically for military applications. The initiative aims to reduce Europe's dependence on foreign technology providers, especially in light of recent US export restrictions. NestAI's models focus on drone autonomy and battlefield orchestration, providing adaptable solutions for military operations. The company is collaborating with AMD and Finland's LUMI AI factory to enhance its computational capabilities. This development underscores the importance of European control over foundational military technologies.
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© SiftedNeko Health has successfully raised $700 million in Series C funding, marking a pivotal moment for the company. With 100,000 members and profitability at the clinic level, this funding round is a testament to the company's robust growth and operational success. The investment is set to drive further expansion and innovation in their healthcare services, potentially reshaping the landscape of healthcare delivery. Neko Health's strategic position in the health tech industry is strengthened by this substantial financial backing, which reflects strong investor belief in its business model and future trajectory.
© SiftedMeticulous, a London-based startup, has secured $15 million in Series A funding to advance its AI-driven code testing platform. Founded by ex-Palantir and Dropbox engineers, the company aims to streamline the process of verifying AI-generated code by simulating user flows and identifying edge cases. This approach allows developers to quickly understand the impact of code changes, potentially accelerating the deployment process. With plans to expand into backend testing and grow its team, Meticulous is positioning itself as a key player in the AI code verification space.
© SiftedSkalar, a Munich-based AI startup specializing in tax and accounting, has secured €12 million in pre-seed and seed funding. This investment marks a new chapter for founder Felix Haas, who previously sold a startup to Klarna for €110 million. The funds will be directed towards developing AI-driven solutions that aim to simplify complex tax processes. This move signifies a growing trend in the financial sector towards leveraging AI for more efficient operations. With this backing, Skalar is set to innovate in the tax industry, potentially changing how businesses manage their tax obligations through automation and optimization.
The b10002 release of llama.cpp enhances its functionality by adding new functions to check the contiguity of inner tensor dimensions, which is crucial for developers dealing with complex data structures. This update significantly broadens the range of supported platforms, including macOS, Linux, Windows, and openEuler. Noteworthy improvements include the integration of ROCm 7.2 for Ubuntu and CUDA 13 for Windows, which cater to specific hardware needs. Although some configurations like KleidiAI on Apple Silicon remain disabled, the release marks a step forward in creating a more adaptable AI development environment. Developers can now optimize performance across a wider array of hardware setups, making the tool more versatile and efficient.
The b10004 release of llama.cpp significantly upgrades its Vulkan and CPU backends by fully integrating f16 SET_ROWS, bringing it on par with f32 capabilities. This update includes comprehensive backend tests and addresses Intel platform issues by implementing DenormPreserve 16. While no new models are introduced, the release broadens compatibility across platforms like macOS, Linux, Windows, and Android, enhancing its utility for developers. With ROCm 7.2 and CUDA 12 and 13 support, llama.cpp continues to evolve as a versatile inference runtime, accommodating a wide range of hardware configurations.
The latest llama.cpp release, version b10010, enhances server capabilities with new Cross-Origin Resource Sharing (CORS) options, allowing developers to fine-tune web application interactions. This update introduces a unique 'localhost' setting and includes rigorous testing to ensure stability. While the release doesn't feature new model architectures, it significantly broadens compatibility across macOS, Linux, Windows, and openEuler platforms. By focusing on server-side improvements, llama.cpp becomes more adaptable for developers integrating AI into web services, offering greater flexibility in deployment environments.