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  1. Huxiu

    Why in-vehicle physical robots are erupting in a concentrated wave in 2026

    Author Zhu Dawei, writing in Chengzhu Dongjian, analyzes that in-vehicle physical robots are appearing in a concentrated wave in 2026, and that the real driving force is the growing homogenization of smart cockpits in screens, chips, and voice, with physical forms becoming a vehicle for differentiation, rather than the maturity of large models. The article compares three paths: Xiaomi Smart Display 2 adapted to models such as the SU7, SU7 Ultra, and YU7; Geely Eva opened pre-sales in May 2026, with reported pricing of 1,099 yuan; and Roewe Jiayue 07 incorporates the Doubao screen and Doubao cockpit assistant into the whole-vehicle solution. The author also mentions that during the 2026 Spring Festival travel rush, sales of several in-vehicle AI robots priced at around 200 yuan on Taobao showed 10,000+, and holds that long-term value depends on whether they can truly integrate into the whole-vehicle interaction ecosystem.

    Author Zhu Dawei, writing in Chengzhu Dongjian, analyzes that in-vehicle physical robots are appearing in a concentrated wave in 2026, and that the real driving force is the growing homogenization of smart cockpits in screens, chips, and voice, with physical forms becoming a vehicle for differentiation, rather than the maturity of large models. The article compares three paths: Xiaomi Smart Display 2 adapted to models such as the SU7, SU7 Ultra, and YU7; Geely Eva opened pre-sales in May 2026, w

    Matched fieldsSummary、Body

    #hot-signal#English text is being prepared.#Embodied AI#Multimodal

Thursday

  8. A6 a6-047 · Baseten Engineering Blog

    Baseten Engineering Blog: A Roundup of Model Performance Optimization Practices

    The model performance section of the Baseten engineering blog rounds up several inference optimization efforts, including speeding up pyannote's speaker diarization model by 9.6x, speeding up Kimi K3 tokenization by 18x under million-token agent workloads, and performing inference engineering optimization for DeepSeek V4 Pro 0813. The section also covers delta weight synchronization optimization for hosted RL rollouts, Coval voice AI benchmarking, and the practice of building APIs for Kimi K3 and GLM-5.2.

    The model performance section of the Baseten engineering blog rounds up several inference optimization efforts, including speeding up pyannote's speaker diarization model by 9.6x, speeding up Kimi K3 tokenization by 18x under million-token agent workloads, and performing inference engineering optimization for DeepSeek V4 Pro 0813. The section also covers delta weight synchronization optimization for hosted RL rollouts, Coval voice AI benchmarking, and the practice of building APIs for Kimi K3 an

    Matched fieldsSummary、Body

    #a6-editorial#Industry news#English text is being prepared.