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Ethan Mollick has published an opinionated guide to using AI right now, suggesting that people use OpenAI's published real-world usage data to judge when to use free models and when to upgrade to paid. He points out that the four most advanced systems are Claude, Gemini, ChatGPT, and Grok, with paid tiers at $20 per month or about $200 per month; the former suits most people, while the latter is aimed at complex technical and programming needs. For serious work, he recommends manually selecting an Agent model, such as GPT-5 Thinking Extended or GPT-5 Thinking Heavy.
suggesting that people use OpenAI's published real-world usage data to judge when to use free models and when to upgrade to paid. He points out that the four most advanced systems are Claude, Gemini, ChatGPT, and Grok, with paid tiers at $20 per month or about $200 per month; the former suits most people, while the latter is aimed at complex technical and programming needs. For serious work, he recommends manually selecting an Agent model, such as GPT-5 Thinking Extended or GPT-5 Thinking Heavy.
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NVIDIA has released the AI Factory Operations Agent for operating and maintaining its own DGX SuperPOD. The framework coordinates multiple domain sub-agents to pinpoint root causes across the compute, network, hardware, and scheduling layers, and provides recommendations through a chat interface. The framework is built on the NVIDIA NemoClaw blueprint and OpenShell, is fully deployed on-premises, uses the local open-source NVIDIA Nemotron model for inference, and runs on DGX H100, DGX B200, and DGX GB300 production clusters. The framework includes sub-agents such as Job Recovery, Hardware, Observability, Research, Kubernetes, and Cluster Management, and integrates MCP tools such as web search, code execution, and Grafana dashboards. Developer examples and open-source code are now available.
NVIDIA has released the AI Factory Operations Agent for operating and maintaining its own DGX SuperPOD. The framework coordinates multiple domain sub-agents to pinpoint root causes across the compute, network, hardware, and scheduling layers, and provides recommendations through a chat interface. The framework is built on the NVIDIA NemoClaw blueprint and OpenShell, is fully deployed on-premises, uses the local open-source NVIDIA Nemotron model for inference, and runs on DGX H100, DGX B200, and
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