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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →HackerRank Chakra turns a technical interview into a hands-on coding task: candidates work in a code repository with an AI assistant while the system observes their process and asks follow-up questions. HackerRank says it generates competency scores and reports, while hiring teams make the final decision. The format points toward interviews that assess not just what a candidate produces, but how they reason, communicate and use AI along the way.
What happens in a Chakra interview?
HackerRank describes Chakra as a technical interview conducted in a practical coding environment. Rather than answer only a self-contained question, a candidate works with a real-world code repository in a canvas that includes an AI assistant. The system can observe the work and ask questions tied to what the candidate is doing—for example, why they chose an approach or how they would adapt if a requirement changed.
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HackerRank says the resulting report scores competencies and gives a rationale supported by the interview transcript and evidence from the candidate’s work. The intended shift is from treating the final code or answer as the main evidence to also considering the decisions and problem-solving that produced it.
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Why does this format matter now?
Generative AI has made it easier to produce a polished artifact without making the author’s own contribution obvious. That complicates interviews built around a correct final answer. HackerRank co-founder and CEO Vivek Ravisankar told TechCrunch, “The previous modality of evaluation was evaluating the output. Now, because of AI, anybody can produce an artifact.”
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A work-in-progress interview could give an employer more evidence about technical judgment, communication and how a candidate uses AI—not merely whether the final result looks right. That is the promise of the format, not proof that observing a process produces a more accurate hiring decision. The quality of the evidence still depends on what the task measures, how the scoring works and how decision-makers interpret the report.
How does Chakra compare with other interview formats?
Interview formats reveal different kinds of evidence. A voice interview centers on conversation; a conventional coding assessment often centers on an answer or score; Chakra is designed to capture work in progress as well. The following distinctions describe the formats in the sources cited here, not every employer’s implementation.
| Format | What the candidate does | Evidence emphasized | What is established about decisions |
|---|---|---|---|
| Traditional coding assessment | Completes a coding assessment; specific tools and rules vary by assessment. | Often the submitted result. HackerRank’s CEO described output evaluation as the previous modality. | The cited coverage does not specify who reviews every employer’s assessment or how each one affects a hiring decision. |
| HackerRank Chakra | Works in a code repository with an AI assistant and answers contextual follow-up questions. | HackerRank says its report considers competencies and supports its rationale with transcript and work evidence. | HackerRank says AI generates scores and reports, while human hiring teams retain the final decision. |
| AI voice-agent interviews in a 2026 field experiment | Completes an interview with an AI voice agent or a human recruiter. | The working paper studied voice interviews, not hands-on coding tasks. | In the studied setup, human recruiters evaluated interviews and made hiring decisions. |
For candidates and employers, useful questions include whether the interview is a conversation or a hands-on task, whether it evaluates a finished answer or the work process, what AI assistance is allowed and recorded, who reviews the scores, and what privacy and accommodation procedures apply.
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TechCrunch reported on October 5, 2026, that Chakra was becoming generally available after about six months in beta. The publication reported HackerRank’s statement that more than 500,000 interviews had taken place during testing, and named Snowflake, Snorkel and Capgemini among organizations that had tried it. These are company-reported figures and participation, not independently audited findings.
TechCrunch also reported Ravisankar’s claim that Chakra interviews produced 70% to 80% fewer suspicious-activity flags than comparable traditional HackerRank assessments. He said the rate varied by geography and seniority. This is a company-reported comparison; the report does not establish an independent finding or show that all forms of misconduct are prevented.
Separately, HackerRank’s product page, accessed October 7, 2026, says Chakra’s average candidate rating is above 4.8 across 500,000+ interviews. That is a vendor claim and a separate measure from the testing-volume figure reported by TechCrunch. Neither figure by itself establishes whether the tool improves hiring quality or fairness.
What does independent research say about AI interviews?
A 2026 working paper by Brian Jabarian and Luca Henkel offers broader context, but it studied AI voice-agent interviews, not Chakra or coding interviews. In a natural field experiment involving 70,000 applicants assigned to AI voice-agent or human-recruiter interviews, the authors report that applicants interviewed by AI agents were 12% more likely to receive a job offer. They report no decline in productivity among hired workers; human recruiters evaluated the interviews and made the hiring decisions.
Those results apply to the firms and setup studied. They do not show that Chakra has the same effects, that AI interviews generally increase offer rates, or that the format identifies stronger candidates.
What should candidates know about data, notice and human review?
HackerRank’s candidate notice says employers may use AI features to evaluate performance and participation integrity, conduct autonomous interviews and follow-up questions, and assess coding, problem-solving, communication, work patterns, rule adherence and AI fluency. Depending on the feature, processing may include webcam images or other signals.
The notice says that, depending on a candidate’s location, options may include requesting an alternative selection process or accommodation, human review, correction of inaccurate information, or an explanation of AI use after an adverse decision. It also describes separate informed written consent and collection and retention conditions if biometric information is deemed to be processed. These statements describe HackerRank’s notice; what applies depends on the feature used and the relevant jurisdiction. Candidates should read the notice for the specific interview and ask the employer how the system is used, what data it processes and how to request an accommodation or review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What rules apply to automated hiring tools?
Rules depend on where the hiring takes place and how a tool is used. In New York City, the Department of Consumer and Worker Protection says Local Law 144 bars covered employers and employment agencies from using a covered automated employment decision tool unless it has undergone a bias audit within one year of use, audit information is publicly available, and required notices are provided. Whether a particular tool and use fall within the law’s scope is a legal question; this requirement should not be generalized to every AI interview or location.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11HackerRank says it uses expert rubrics, human annotations, human-AI agreement checks and frequent third-party bias analyses. Those are descriptions of the vendor’s processes, not independent proof that a system is valid, unbiased or fair for every group. A consistent rubric can make evaluation more structured, but consistency alone does not establish fairness.
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What would make this kind of interview more trustworthy?
For employers, the central challenge is not simply adding an AI interviewer. It is showing that the interview measures skills relevant to the job and that the resulting evidence is reviewed responsibly. Useful safeguards include:
- Job-relevant tasks: Make the exercise reflect actual work rather than reward familiarity with an interview format.
- Clear candidate notice: Explain what is recorded or analyzed, what AI is permitted, and how the resulting report may be used.
- Meaningful human review: Give reviewers access to the underlying work and context, not only a score, and retain human responsibility for the hiring decision.
- Accessible alternatives: Provide a way to request accommodations or an alternative process where applicable.
- Evidence beyond vendor assurances: Evaluate whether scores predict job-relevant performance and examine outcomes across relevant groups and jurisdictions.
Ravisankar told TechCrunch, “AI is way less biased than humans, if you tune it properly.” That is his argument, not an established result. To evaluate it, employers and candidates need evidence about the particular tool, task, scoring process and use—not a general claim about AI compared with people.
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