Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
碎片化的背后，藏着一个被大多数人忽视的机会——编排层。
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魔法原子（MagicLab）成立于2024年1月，是一家专注于通用人形机器人和具身智能技术研发与落地应用的全球化公司。公司核心成员汇聚清华、上海交大、密西根大学等国内外顶尖高校，研发人员占比超70%，拥有全栈自研的软硬件技术体系。其核心硬件自研比例超90%，覆盖关节模组、灵巧手等关键零部件，并自研VLA大模型“原子万象”，采取VLM+MoE分层架构，已采集约36万条真机数据，实现了多模态感知与具身操作的算法领先。