How did we run all these experiments concretely?
Our panel of repositories
We started by running an analysis over thousands of public GitHub repositories from which we extracted statistics about programming languages & frameworks, third-party services, deployment platform, team sizes, and codebase age. Since Tech startups are more likely to have open-source repositories than large enterprises, and stacks are likely very different we then unbiased our statistics based on publicly available data and reached our ideal panel distribution.
We then staffed various coding agents to create real-world repositories to match these exact requirements. Finally, we generated variants in which we removed parts of the codebases and with them, entire third-party service implementations so we could run prop (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://armature.tech/blog/which-tools-coding-agents-install)获取最准确的信息。

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🔗 **原文链接**: [Which tools do Claude, Codex and Cursor choose? We measured ](https://armature.tech/blog/which-tools-coding-agents-install)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 65票 · 👤 screm

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-09-04 08:01