问HN：在人工智能辅助编码的时代，招聘有哪些有效的方法？
我看到HackerRank（YC S11）的招聘帖子（https://news.ycombinator.com/item?id=47667011），这让我意识到我已经不再了解如何有效评估候选人了。
具体来说，我们正在从三个维度改变招聘方式：
> 任务：真实世界的代码库任务 vs 标准算法风格的难题
> 评估：AI流利度、协调能力 vs 功能正确性
> 候选人体验：自主IDE vs 简单的代码编辑器
在“旧世界”中，你可以通过提问多个问题来三角测量候选人的技能。而现在，评估似乎在很大程度上依赖于不断变化的工具和模型。
所以我很好奇：
> 今天哪些信号与优秀工程师的表现相关？
> 如何设计面试，使其不会在下一个模型发布后变得过时？
> 算法面试还有用吗？
我很想听听那些最近改变招聘流程或使用这种新方法接受面试的人的看法。
查看原文
I saw the HackerRank (YC S11) hiring post (https://news.ycombinator.com/item?id=47667011) and it made me realize I no longer understand how to evaluate candidates effectively.<p>Specifically, we are changing hiring across 3 dimensions:
> Tasks: Real-world tasks on code repositories vs standard algorithmic-style puzzles
> Evaluation: AI fluency, orchestration skills vs functional correctness
> Candidate experience: Agentic IDE vs a simple code editor<p>In the “old world,” you could ask multiple questions and triangulate skill from answers. Now it seems like evaluation depends heavily on tools and models that keep changing month to month.<p>So I’m curious:
> What signals actually correlate with strong engineers today?
> How do you design interviews that don’t become obsolete with the next model release?
> Are algorithmic interviews still useful at all?<p>Would love to hear from people who have recently changed their hiring process or have been interviewed using this new approach.