本文基于Linux.do社区讨论,聚焦一个看似简单却关键的前端转盘抽奖任务。通过测试DeepSeek、Qwen、GPT、Gemini、Claude等AI模型,发现大量模型在实现时出现视觉与逻辑不一致的错误,如结果显示一等奖但指针指向其他奖项。这不仅暴露了AI编码能力的短板,尤其国产模型与国际品牌的差距,也突显了细节处理在AI开发中的重要性。研究为优化AI模型提供方向,提醒开发者注意实际应用中的陷阱。
原文链接:Linux.do
本文基于Linux.do社区讨论,聚焦一个看似简单却关键的前端转盘抽奖任务。通过测试DeepSeek、Qwen、GPT、Gemini、Claude等AI模型,发现大量模型在实现时出现视觉与逻辑不一致的错误,如结果显示一等奖但指针指向其他奖项。这不仅暴露了AI编码能力的短板,尤其国产模型与国际品牌的差距,也突显了细节处理在AI开发中的重要性。研究为优化AI模型提供方向,提醒开发者注意实际应用中的陷阱。
原文链接:Linux.do
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