Jian Hua

In this AI in Motion episode, we invite Sarah Tariq to tell what role autonomy plays in NVIDIA’s business and how both use multimodal and customized end-to-end approaches to get people around safely. Watch the full episode: https://lnkd.in/eDNqqcrC

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From composed image retrieval with LLMs to zero-shot 3D question answering, Amazon researchers are presenting work that's reshaping computer vision at this year's CVPR. Visit our booth to see live demos and connect with our scientists, or join our workshops for deeper dives into our research. Learn how we're advancing the future of computer vision and explore our papers: https://amzn.to/4dV09iK #CVPR2025

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What happens when two Cloud Native Computing Foundation (CNCF) Ambassadors talk open source and cloud-native innovation? Intuit’s Lisa-Marie Namphy and Carlos Santana from Amazon Web Services (AWS) discuss Argo, AI agents, and the future of collaboration. Watch the conversation 👇 Cloud Native Computing Foundation (CNCF) Intuit Open Source Argo Project

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💭 How is open source important to you? We had a great moment with Anish Maddipoti from NVIDIA -- and learned how he got started as an open source developer. 👇

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🧠💬 Memory in LLMs A practical guide showing how to implement conversational memory in LLMs using LangGraph, demonstrated through a therapy chatbot. Features code examples for basic retention, trimming, and summarization approaches. Learn to build memory-aware apps 👉 https://lnkd.in/gybcrV5v

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Are single GPU notebooks forcing you to downsample or don't let you experiment with large models? As datasets and models grow with the shift from structured to multimodal data, datasets and models no longer fit in the single GPU development environments. This blog highlights key considerations of distributed AI, how Ray lets you go multi-GPU without rewriting your code, and how Anyscale makes running it easy. https://lnkd.in/gWFYDuaY

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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