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Home - Asia - MossCode Raises Million-Greenback Angel Spherical at a $100 Million Valuation
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MossCode Raises Million-Greenback Angel Spherical at a $100 Million Valuation

NextTechBy NextTechFebruary 10, 2026No Comments2 Mins Read
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On February 9, 2026, AI-powered sensible sports activities wearables model MossCode introduced that it has accomplished a million-dollar angel financing spherical, valuing the corporate at $100 million. The spherical was led by XVC and Qingliu Capital (ClearVue Companions). Proceeds will likely be used to increase the R&D group and advance mass manufacturing, with the model planning to formally enter the European and U.S. markets within the first half of 2026.

MossCode’s fundraising progressed at notable pace, taking only one month from preliminary investor engagement to ultimate commitments, with two current shareholders growing their stakes following the shut of the spherical. The core founding group comes from main organizations together with Sequoia China, OPPO, and Apple, in addition to prime tutorial establishments reminiscent of Stanford College. With a median age of 30, the group combines hands-on expertise in sensible {hardware} commercialization with confirmed capabilities in constructing abroad shopper manufacturers. Founder Ni Ruoyang and co-founder Ma Xiao each deliver sturdy monitor data in worldwide market execution.

Leveraging the group’s world growth background, MossCode has adopted a “go world first, then return dwelling” commercialization technique. Within the first half of the 12 months, the corporate will launch merchandise concentrating on outside sports activities fans in Europe and the U.S., with preliminary shipments scheduled for Q3. The rollout will focus first on core segments reminiscent of operating and biking, earlier than increasing to a broader well being and health viewers. On the provision chain facet, MossCode has partnered with Luxshare Precision Business, a number one sensible wearables producer, to make sure product high quality and manufacturing reliability.

Supply: Leiphone

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Methods to Design Advanced Deep Studying Tensor Pipelines Utilizing Einops with Imaginative and prescient, Consideration, and Multimodal Examples

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