HackerRank 推出 Chakra 智能体面试官，考核重心从算法题转向 AI 协作能力
Sorry we made you grind LeetCode
HackerRank 推出智能体面试产品 Chakra，让候选人在真实代码仓库中借助 AI 工具完成实际任务，并接受方案选择、改进方向以及对 AI 生成代码验证等追问，把考核重点从传统算法题转向 AI 协作与判断能力。
对比传统 LeetCode 式算法面试与 Chakra 的智能体评估方式，可看到 AI 时代技术招聘的考核重心正从代码结果转向 AI 协作与判断能力。
BackI’m Vivek, co-founder/ceo of HackerRank. We’re sorry we made you grind leetcode. That line is on billboards around SF now.
HackerRank didn’t invent algorithmic interviews, but we helped normalize a version of technical hiring that turned into months of grinding questions with little connection to the actual job.
Technical interviews have come a long way: from “How many golf balls fit in a 747?” to reading code over the phone, writing it on a whiteboard, and eventually solving LeetCode-style problems.
Each step got us a little closer to the work. But we were still mostly evaluating how well someone had prepared for an interview, not how well they could build software.
AI has changed that work.
Developers increasingly plan with agents, build with them, and review their output. They need to know when the code is good, when it needs another pass, and when it’s completely wrong.
The hiring process needs to evolve too.
At HackerRank, we’re changing three things:
- Tasks: Algorithmic questions → real work in code repositories
- Evaluation: Code correctness → AI fluency and judgment
- Candidate experience: IDE → agentic development environment
We’ve packaged this into Chakra, our agentic interviewer.
Candidates get a realistic problem and access to AI tools. They solve it as they would in the real world. Chakra then digs deeper: Why did you choose that approach? What would you change? How did you validate the AI-generated code?
You can now see how well someone solved the problem and not just the output.
Do algorithm questions still have value? Yes, they do but the problem is the weightage associated with them.
Vivek
Co-founder & CEO
来源：Hacker News · hackerrank.com