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NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities

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NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nemotron-Cascade 2 is the second open-weight LLM to achieve Gold Medal-level performance in the 2025 […]

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AI 에이전트와 에이전틱 AI 활용 가이드

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에이전틱 AI는 자동화와 의사결정을 위한 차세대 키워드로 주목받으며 큰 기대를 모으고 있습니다. 하지만 이러한 열기 이면에는 다소 조심스러운 전망이 자리 잡고 있습니다. 가트너(Gartner)는 비용 급증, 불분명한 비즈니스 가치, 부적절한 리스크 관리 등의 이유로 에이전틱 AI 프로젝트의 40% 이상이 2027년 말까지 중단될 것이라고 경고합니다. *이 글은 SAS 글로벌 공공 부문 전략 […] The post AI 에이전트와

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Google Colab Now Has an Open-Source MCP (Model Context Protocol) Server: Use Colab Runtimes with GPUs from Any Local AI Agent

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Google has officially released the Colab MCP Server, an implementation of the Model Context Protocol (MCP) that enables AI agents to interact directly with the Google Colab environment. This integration moves beyond simple code generation by providing agents with programmatic access to create, modify, and execute Python code within cloud-hosted Jupyter notebooks. This represents a

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Meet Mamba-3: A New State Space Model Frontier with 2x Smaller States and Enhanced MIMO Decoding Hardware Efficiency

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The scaling of inference-time compute has become a primary driver for Large Language Model (LLM) performance, shifting architectural focus toward inference efficiency alongside model quality. While Transformer-based architectures remain the standard, their quadratic computational complexity and linear memory requirements create significant deployment bottlenecks. A team of researchers from Carnegie Mellon University (CMU), Princeton University, Together

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NVIDIA AI Open-Sources ‘OpenShell’: A Secure Runtime Environment for Autonomous AI Agents

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The deployment of autonomous AI agents—systems capable of using tools and executing code—presents a unique security challenge. While standard LLM applications are restricted to text-based interactions, autonomous agents require access to shell environments, file systems, and network endpoints to perform tasks. This increased capability introduces significant risks, as a model’s ‘black box’ nature can lead

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Zhipu AI Introduces GLM-OCR: A 0.9B Multimodal OCR Model for Document Parsing and Key Information Extraction (KIE)

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Why Document OCR Still Remains a Hard Engineering Problem? What does it take to make OCR useful for real documents instead of clean demo images? And can a compact multimodal model handle parsing, tables, formulas, and structured extraction without turning inference into a resource bonfire? That is the problem targeted by GLM-OCR, introduced by researchers

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Stanford Researchers Release OpenJarvis: A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

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Stanford researchers have introduced OpenJarvis, an open-source framework for building personal AI agents that run entirely on-device. The project comes from Stanford’s Scaling Intelligence Lab and is presented as both a research platform and deployment-ready infrastructure for local-first AI systems. Its focus is not only model execution, but also the broader software stack required to

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NVIDIA AI Releases Nemotron-Terminal: A Systematic Data Engineering Pipeline for Scaling LLM Terminal Agents

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The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency in terminal environments, the training strategies and data mixtures behind them have remained closely guarded secrets. This lack of transparency has forced researchers and devs into a costly cycle

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