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Now, you can start a coding agent and proceed in two ways: turn the implementation into a specification, and then in a new session ask the agent to reimplement it, possibly forcing specific qualities, like: make it faster, or make the implementation incredibly easy to follow and understand (that’s a good trick to end with an implementation very far from others, given the fact that a lot of code seems to be designed for the opposite goal), or more modular, or resolve this fundamental limitation of the original implementation: all hints that will make it much simpler to significantly diverge from the original design. LLMs, when used in this way, don’t produce copies of what they saw in the past, but yet at the end you can use an agent to verify carefully if there is any violation, and if any, replace the occurrences with novel code.
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研究團隊首先從健康者及疾病不同階段的患者身上提取肺細胞，並以高解析度DNA定序生成大量資料，使他們得以觀察細胞在疾病發展過程中的變化。接著，他們建立一個生成式AI模型，模擬這些變化，描繪疾病進展時各類細胞狀態與細胞群的轉換，同時標記可用於診斷的生物標記與可能的治療靶點。
它将"你 + 一个 Agent"的对话模式，升级为 "你 → 你的主 Agent → MCO → 多个 Agent 并行执行" 的层级指挥模式。你只跟一个 Agent 对话，但背后有整支军团在为你冲锋。。超级工厂对此有专业解读