you can see how OpenAI is building around Python + UV while Anthropic is building around TS + Bun
@theahmadosman
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SGLang and vLLM Recommended Over llama.cpp for GPU Inference
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llama.cpp is not what you should base your experience on try Sglang and vLLM with GPUs so you can have a proper baseline
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RTX PRO 6000 Server vs DGX Station: Which to Choose
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It’s between building your own RTX PRO 6000 server and the DGX Station, unfortunately haven’t had a chance to review the DGX Station yet to tell you which one for sure
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Unified Memory Issues Solved by vLLM or SGLang on GPUs
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No, it is not a model or a local thing It’s a Unified Memory thing You use vLLM or Sglang with GPUs and you won’t have this problem
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Why Macs Fall Short for Agentic LLM Workloads
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this is exactly why I tell people to NOT get a Mac anything for LLMs useless for concurrency and agents, basically a single chat interface that doesn’t scale up neither with sessions nor with kvcache you want to be ready for the agentic world you Buy a GPU
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Gemini ChatGPT Claude Positioned for Distinct AI Market Roles
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Gemini will be the Android / iOS model ChatGPT will be the enterprise model Claude will be the specialized agents model
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MiniMax M2.x Insight: Models Must Recursively Evolve Their Own Harness
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The model’s ability to recursively evolve its own harness is critical – Insight from MiniMax as they iterate through M2.x
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MiniMax M2.7 Impresses Users Ahead of M3 Release
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MiniMax M2.7 is looking realllly good Cannot wait to try to it locally, also MiniMax M3 is probably gonna be massive
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Seeking Jensen Huang Signature on DGX Station GB300
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I think my next goal is to get a Jensen signed DGX Station GB300
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Team Upgrades to RTX PRO 6000 GPU Stack
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We’re leveling up, moving forward only stacking RTX PRO 6000