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English(EN) I Gave a GLM AI Model My Worst Multi-File Bug. Here's the Script and Results.

GLM AI模型5.3更有效地调试了复杂的多文件bug

一位开发者测试了GLM AI模型,特别是GLM 5.3,来处理一个之前让其他AI模型都感到棘手的复杂多文件bug。开发者创建了一个脚本来模拟真实的调试过程,AI进行分析、提出修复方案,然后脚本进行测试。GLM 5.3成功解决了bug,并且使用的token数量少于Qwen3-235B-A22B模型,这表明它在处理需要跨多个文件上下文的长程编码任务方面具有优势。 AI

影响 展示了AI在复杂代码调试方面日益增长的能力,可能改善开发者的工作流程和代理任务的完成。

排序理由 该条目描述了用户为特定任务(调试代码)测试AI模型,属于‘工具’类别。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

GLM AI模型5.3更有效地调试了复杂的多文件bug

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该条目描述了用户为特定任务(调试代码)测试AI模型,属于‘工具’类别。
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Noah Bennett ·

    我给GLM AI模型出了一个最糟糕的多文件Bug。这是脚本和结果。

    <p>The Bug That Survived Two Other Models</p> <p>I had a bug that had beaten two other models across six separate debugging sessions — a data pipeline ordering issue that only showed up when specific record types interleaved, requiring tracing across four files to actually fix. I…