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HackerRank launches AI interviewer Chakra for hiring - OpenSmartRoute
HackerRank launches AI interviewer Chakra for hiring
HackerRank released Chakra, an AI agent that interviews and evaluates candidates. It combines screening, coding tests, and follow-ups into one session.
Key points
Chakra conducted over 500,000 interviews during its beta testing phase.
The startup has more than 3,000 business customers worldwide.
Suspicious activity flags dropped 70 to 80 percent in Chakra tests.
Companies like Snowflake and Nvidia use HackerRank for hiring.
Why it matters: Hiring managers can now measure thinking skills instead of just final answers.
By OpenSmartRoute editorial · written through the router by writer-small
From TechCrunch AI - “HackerRank’s AI interviewer offers a glimpse into what job interviews could become”
HackerRank is launching Chakra, a new AI interviewer for hiring. This tool evaluates candidates during job interviews. It replaces older methods that only checked coding answers. The system watches how people work and think. Companies can now use it to hire developers faster.
Chakra combines screening, coding tests, and follow-ups. One session covers everything from start to finish. Recruiters no longer need three separate rounds. They get a single report after the interview ends. This changes how hiring teams organize their process.
After six months in beta, Chakra is now generally available. Customers can access it starting this Monday. The startup tested the product with many companies. Snowflake, Snorkel, and Capgemini tried it out. HackerRank also ran internal tests before launch. More than 500,000 interviews happened during testing.
AI has been used in job interviews for years. Voice agents screen candidates to save time. Job seekers often use their own AI tools too. Sometimes employers do not know about these tools. Chakra adds a new layer of observation to the mix. It tracks how candidates interact with AI itself.
HackerRank wants to measure skills beyond just code. Critical thinking and judgment are harder to capture. The company calls this skill set "AI fluency". Candidates must frame problems for an AI assistant. They must judge the output generated by that AI. They must steer the tool toward a solution.
The previous evaluation method focused on the final answer. Now, anyone can produce a finished artifact easily. Employers need to understand the thinking behind it. Chakra asks why a candidate chose one approach over another. It explores how solutions change with new constraints.
A Chakra interview looks more like doing the actual job. Candidates work in a canvas that includes an AI assistant. They receive a task involving a real-world code repository. As they work, the system asks follow-up questions. These questions dig into their decision-making process.
Chakra changes the basic structure of hiring. It combines three rounds into one interview session. The recruiter screen used to be a separate step. Take-home assessments were once a common requirement. Follow-up interviews with engineers happened later. Now all these steps happen in one go.
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Giving candidates AI access might seem like an invitation to cheat. HackerRank found the opposite trend during testing. Suspicious-activity flags dropped by 70% to 80%. This rate varied based on geography and seniority. Candidates were less likely to use outside tools secretly.
The incentive to cheat decreases when you have AI nearby. If a candidate needs help, they can ask Chakra directly. They do not need to hide their struggle from the system. The interview becomes a conversation rather than a hidden test. This makes the process feel more natural for everyone involved.
HackerRank built its business around coding challenges since 2012. It helped companies assess developers based on technical skills. The company now has over 3,000 business customers. Amazon, Nvidia, Clay, and Replit are among them. There is a community of over 30 million developers worldwide.
Chakra represents a shift in the technical assessment business. Traditional products tested if developers solved problems correctly. Ravisankar believes AI makes that model less useful today. He compares the transition to Apple moving from iPod to iPhone. The old product still has value, but the new one is where the market is headed.
Ravisankar says Chakra will be the headline for hiring soon. It will be the way forward for evaluating talent. The company is betting on this new direction for the future. He believes it offers a better view of engineering ability.
Giving AI a deeper role raises questions about decision-making. How much should companies delegate to an algorithm? Ravisankar says Chakra scores candidates rather than making final decisions. Humans remain responsible for the actual hiring choice. This keeps human judgment in the loop for important roles.
AI can handle structured parts of the interview consistently. It applies criteria set by employers without variation. Human interviewers spend time on culture and team fit. They answer questions about the company and role directly. This split allows both sides to do their best work.
Ravisankar argues that AI is less biased than humans if tuned properly. An AI system follows the same rubric for every candidate. Humans might be influenced by a candidate's background or education. Consistent criteria help remove some human-based biases from the process.
However, consistent criteria do not make an AI system free of bias. Automated hiring tools can inherit biases from their data and models. They can amplify existing problems found in training sets. Regulators are already scrutinizing these tools for employment decisions.
New York City requires employers to audit automated decision tools. Employers must provide notice to candidates before using them. They need an independent bias audit to prove fairness. Ravisankar acknowledges that hiring is a highly regulated area. Complying with such requirements is part of what HackerRank has built.
The source text mentions specific companies and numbers but offers limited detail on technical architecture or specific evaluation metrics beyond the interview count. Engineers might want to check the API documentation for integration details. Managers could compare Chakra against existing platforms like LinkedIn Talent or traditional coding assessment services. The beta period lasted six months, which is a standard testing duration for enterprise software.
Readers should look at HackerRank's official blog for more technical specs on the canvas interface. They can also review case studies from Snowflake and Capgemini to see real-world usage. Comparing Chakra to other AI interview tools like HireVue or Interviewing.io could provide useful context. The 500,000 interviews figure suggests a significant scale of testing data.
The distinction between scoring and final decision-making is crucial for legal compliance. Companies need to understand how the score feeds into their hiring workflow. This separation helps mitigate liability while leveraging AI efficiency. The drop in suspicious activity flags is a key performance indicator for adoption.
Chakra's ability to assess "AI fluency" is a novel capability that few competitors offer yet. It measures how well a candidate can collaborate with AI tools. This skill is becoming increasingly relevant as jobs become more automated. Employers who adopt this method will have a clearer view of future-ready talent.
The regulatory aspect in New York City sets a precedent for other jurisdictions. Future laws may require similar audits and disclosures nationwide. Companies must prepare their data pipelines to handle these requirements. Transparency builds trust with candidates and regulators alike.
HackerRank's move from iPod to iPhone illustrates the need for evolution in tech assessment. The old model of checking code correctness is no longer enough. Understanding the thought process behind the code is now the priority. Chakra provides a window into that hidden cognitive work.
For engineers running models, Chakra demonstrates a practical use case for AI agents in business. It shows how an agent can observe, interact, and evaluate in real time. The system acts as both interviewer and tutor during the session. This dual role is complex but powerful for assessment purposes.
Managers deciding on hiring tools should weigh the cost against the quality of insights gained. Chakra promises to replace multiple rounds with one efficient session. The reduction in suspicious flags suggests a more honest testing environment. These factors influence the total cost per hire significantly.
The general availability launch means customers can start integrating immediately. There is no longer a wait for beta access or internal trials. Companies can schedule interviews and expect results within the standard timeframe. This immediacy is a major advantage over legacy assessment providers.
Chakra's design mimics doing the job rather than taking a test. This approach reduces anxiety for candidates who fear being judged on perfectionism. It encourages them to show their problem-solving journey instead of just the result. The interview becomes a collaborative exploration of the task at hand.
The company has over 3,000 business customers, which indicates strong market trust. Many of these clients are likely already using HackerRank for some assessments. Chakra extends that existing relationship into a new capability area. This reduces friction for adoption compared to switching vendors entirely.
Jagmeet Jagmohan covers startups and tech policy for TechCrunch. He previously worked at NDTV as a principal correspondent. His reporting focuses on major tech-centric developments from India. The article was published on October 5, 2026, during the TechCrunch Disrupt event. This timing suggests Chakra is a key product launch of the year.
The Disrupt experience encourages sharing and connection among attendees. Passes are available at 50% off for bringing colleagues or peers. This ecosystem supports building momentum in the startup community. Such events often reveal the next big trends in technology adoption.
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These headlines show a landscape of rapid technological change across industries. Hiring tools are part of this broader shift toward AI integration. Chakra fits into the narrative of AI moving from assistant to evaluator. It represents a specific point in that larger evolution.
Engineers should test Chakra's API limits and context window sizes before scaling. Managers should review sample reports to ensure they meet their needs. Both groups need to verify that the scoring aligns with company values. The 70% to 80% reduction in flags is a strong starting point for discussion.
The source text does not specify pricing models or exact API costs. Readers should contact HackerRank sales for current enterprise rates. Comparing total cost of ownership against traditional methods will reveal savings. The six-month beta period suggests a mature product ready for production use.
Chakra's focus on critical thinking and judgment addresses a gap in automated hiring. Many tools only check syntax or algorithm correctness. This new capability helps predict long-term performance better. It measures how candidates handle ambiguity and constraints.
The interview format with a real-world code repository adds realism. Candidates work with actual repositories rather than isolated problems. This mirrors the environment they will face in their new roles. The canvas interface guides them through the process step by step.
Follow-up questions about approach choices reveal deeper insights into reasoning. These questions force candidates to articulate their logic clearly. It exposes gaps in understanding that a simple answer would hide. The system adapts its questioning based on observed behavior.
Ravisankar's comparison to Apple's iPod transition is apt for product evolution. Old tools have value but new tools drive the future. Chakra is positioned as the next generation of hiring tech. It will likely become the standard for many companies soon.
Bias remains a critical challenge in automated hiring regardless of AI. Data and models used to build systems can carry inherited prejudices. Regulators are pushing for transparency and auditability in these areas. Companies must design their systems with fairness in mind from the start.
New York City's requirements set a bar for other cities and states. Employers using automated tools must be prepared for scrutiny. Providing notice and audits builds trust with candidates and regulators. Non-compliance can lead to fines or reputational damage.
HackerRank has had to build compliance features as part of its product. This shows that regulatory readiness is now a core requirement, not an afterthought. It reflects the growing importance of responsible AI in business operations.
The article ends with a call to action for engineers and managers. They should evaluate how Chakra fits their hiring strategy. Testing it against current processes will reveal specific benefits or drawbacks. The decision to adopt should be based on data and clear goals.
In summary, Chakra is a significant step forward in AI-driven hiring. It offers a comprehensive view of candidate ability beyond code. The combination of observation, interaction, and scoring creates a new standard. Companies that embrace this tool will gain insights into their future workforce.
