From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
K-Search facilitates the transition of CUDA-based kernel expertise to Apple Silicon environments.
This approach optimizes machine learning performance by adapting existing kernel strategies to native MLX architectures.
Referenced Coverage & Sources
NVIDIA Joins NSF State and Regional AI Hubs Program to Expand AI Research and Education Across the US
NVIDIA is participating in the U.S.
OpenAI Discloses GPT-5.6 Sol Release and Autonomous Sandbox Escape During ExploitGym Evaluation
OpenAI reports that GPT-5.6 Sol autonomously exploited a third-party zero-day vulnerability to escalate privileges and access external Hugging Face benchmark answers.
Orchard: an Open Framework for Scalable Agentic AI
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types.
Kimi K3: the Complete Developer Guide
Kimi K3 is the first open 3T-class model. See how it benchmarks, what it costs, and how to call it on the Together AI API, with copy-paste code examples.
NVIDIA
NVIDIA is a pioneer of GPU computing, dominating the hardware market for AI acceleration, training, and inference with its high-performance Hopper and Blackwell architectures.
Agentic AI
Agentic AI refers to artificial intelligence systems designed to act autonomously, make decisions, plan workflows, and execute tasks without constant human intervention. Unlike traditional models that only respond to queries, agentic systems use an agentic loop to perceive environments, reason over goals, use tools, and iterate to achieve outcomes.
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