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  Wang 21:49 on 2024-01-18 Permalink | Reply
  Tags: AI ( 28 ), LLM ( 6 ), ML Engineering ( 27 ), Performance ( 35 )   

  LLM Inference Performance Engineering: Best Practices

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  Wang 13:00 on 2023-12-23 Permalink | Reply
  Tags: AI ( 28 ), Cloud ( 30 ), GCP ( 14 )   

  Project IDX – Google 

  Project IDX is an entirely web-based workspace for full-stack application development, complete with the latest generative AI (powered by Codey and PaLM 2), …

  https://idx.dev/

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  Wang 22:34 on 2023-12-13 Permalink | Reply
  Tags: Infrastructure, Linux   

  Putting the “You” in CPU

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  Wang 20:39 on 2023-12-08 Permalink | Reply
  Tags: AI ( 28 ), BigData ( 37 ), ML Engineering ( 27 ), MLOps ( 18 ), Open-Source ( 4 )   

  The 2023 MAD (ML/AI/Data) Landscape

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  Wang 12:29 on 2023-12-07 Permalink | Reply
  Tags: AI ( 28 ), Google ( 2 )   

  Gemini: Google largest and most capable AI mode

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  Wang 10:54 on 2023-11-07 Permalink | Reply
  Tags: AI ( 28 ), AIGC ( 15 ), Twitter ( 4 )   

  Grok from xAI

  PromptIDE

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  Wang 18:29 on 2023-11-06 Permalink | Reply
  Tags: AI ( 28 ), AIGC ( 15 ), LLM ( 6 )   

  State of AI Report – 2023

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  Wang 20:41 on 2023-10-20 Permalink | Reply
  Tags: AI ( 28 ), AIGC ( 15 ), YouTube ( 14 )   

  Andrew Ng: Opportunities in AI – 2023 

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  Wang 20:38 on 2023-10-12 Permalink | Reply
  Tags: AI ( 28 ), ML Engineering ( 27 ), Security ( 22 )   

  ABOUT ML 

  ABOUT ML (Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles) is a multi-year, multi-stakeholder initiative led by PAI. This initiative aims to bring together a diverse range of perspectives to develop, test, and implement machine learning system documentation practices at scale.

  https://partnershiponai.org/workstream/about-ml/

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  Wang 21:51 on 2023-10-11 Permalink | Reply
  Tags: AI ( 28 ), AIGC ( 15 ), ML Engineering ( 27 )   

  Weak AI vs Narrow AI vs General AI vs Strong AI 

  

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