I have been quietly dreading this announcement for about eighteen months, which is roughly how long our first-round technical screen has been generating results I no longer believe. Yesterday HackerRank put a name to the problem and shipped the answer: Chakra, an AI agent that conducts the interview instead of marking the homework. The number everyone will repeat is that suspicious-activity flags came in 70 to 80 percent lower than in the company’s traditional assessments. That is the least interesting thing in the announcement. The interesting part is the admission underneath it, and it applies to every Singapore employer still sending out a take-home exercise this week.
What HackerRank Actually Shipped
Chakra is an AI agent that runs a technical interview. It observes the candidate while they work rather than receiving a finished submission, evaluates the reasoning as well as the answer, and asks follow-up questions about why a particular approach was chosen. It also scores a dimension the company calls AI fluency: how a candidate frames a problem for an AI tool and how they steer the solution that comes back.
The scale behind the launch is substantial. HackerRank reports more than 500,000 interviews conducted during beta testing, with Snowflake, Snorkel and Capgemini among the beta customers. The company serves 3,000+ business customers, including Amazon, Nvidia, Clay and Replit, and claims a community of 30+ million developers. The reporting, including the quotes below, is in TechCrunch, 5 October 2026.
Co-founder and chief executive Vivek Ravisankar gave the rationale in one sentence that is worth reading twice: The previous modality of evaluation was evaluating the output. Now, because of AI, anybody can produce an artifact.
Our Expert Take
Read that quote as a confession and it becomes much more useful. The largest technical assessment company in the world has just said, on the record, that the product category it built is measuring the wrong thing. Every Singapore employer running a take-home exercise or a timed coding challenge is using a proxy that its own vendor has publicly declared obsolete. That is not a reason to panic, because the proxy was never great. It is a reason to stop treating a passed screen as evidence and start treating it as the weakest signal in your loop, which is roughly where it belonged even before 2023.
Why the 80 Percent Drop in Cheating Flags Is the Wrong Headline
A 70 to 80 percent reduction in suspicious-activity flags sounds like a fraud-detection triumph. Look at it from the other direction and it is something quieter: when you stop asking a question a model can answer for the candidate, the behaviour you were flagging mostly stops being worth engaging in.
In a take-home or a timed challenge, the incentive to use outside help is enormous, because the artifact is the entire score. In an observed interview with follow-up questions about your reasoning, outside help is of limited use, since the thing being measured is happening live in your explanation. The flags did not fall because detection improved. They fell because the format removed the payoff.
This matters for how you interpret the figure in your own context. If you adopt an observed format and your cheating signals drop, you have not solved dishonesty in your pipeline. You have changed what the exam rewards, which is better but is a different claim. The candidates who were going to misrepresent their ability will still try; they will just have to do it in conversation, where frankly they were always easier to catch.
AI Fluency Is a Real Signal, and It Is Easy to Measure Badly
The AI fluency dimension is the part of Chakra I find most defensible and most dangerous. Defensible because it reflects how the job is now done: an engineer who frames a problem well for a model and critically reviews what comes back is genuinely more productive than one who does not, and pretending otherwise in an interview is measuring a job nobody has.
Dangerous because it is trivially confused with tool familiarity. An engineer who has used a particular assistant daily for a year will look fluent. An engineer with better judgement who has been working in a regulated environment where those tools were not permitted will look slow. The first is a transferable skill that decays; the second is a durable one. If you score fluency as speed-of-use, you will systematically prefer the weaker candidate, and in Singapore you will do it in a way that correlates with which employer they came from.
The test I now use, borrowed from reviewing far too many of these transcripts: look for the moment the candidate rejected what the model produced. Fluency is not getting good output. It is noticing bad output quickly and knowing why. We go deeper on this in our guide to evaluating AI agent skills in developer interviews.
Still Screening Singapore Developers With a Take-Home Exercise?
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Let’s Discuss ThisThe Bias Claim, and the Part of It That Is Doing the Work
Ravisankar also said: AI is way less biased than humans, if you tune it properly.
The conditional clause is carrying the entire sentence, and it is the clause most readers will skip.
A model applying a rubric is genuinely more consistent than a human panel. It gives candidate forty the same attention as candidate one, it does not interview worse at 5pm on a Friday, and it does not warm to someone because they went to the same university. Those are real improvements and they are not small, because panel inconsistency is a much bigger source of bad hiring decisions than most companies admit.
But consistency is not fairness. A rubric applied identically to everyone, which happens to reward fluent idiomatic English explanation under time pressure, is reliably unfair rather than randomly unfair. In Singapore that distinction is not academic. A hiring pipeline here routinely sees candidates who think in one language, learned to program in a second and are explaining themselves in a third, and the engineer who pauses to find the right word is not reasoning more slowly.
The audit is cheap and I would not deploy any automated screen without it: run the tool against engineers you already hired and rate highly, and look at who it would have screened out. If your three best hires from the last two years would have failed the automated round, you have learned something important before it cost you a candidate rather than after.
There is also a governance point specific to hiring here. Singapore employers operate under the Tripartite Guidelines on Fair Employment Practices, and the expectation that candidates are considered on merit does not transfer to the vendor because a tool produced the score. If an automated screen rejects someone, somebody in your organisation needs to be able to explain the basis. Buy tools that will show you that, and be wary of any that will not.
Our Expert Take
The honest framing of any automated interviewer is that it converts a variable-quality human process into a fixed-quality automated one. Whether that is an upgrade depends entirely on how good your humans were, and most companies have never measured. If your first-round screen is currently a rushed thirty minutes run by whichever engineer had a gap in their calendar, working from no rubric, an AI interviewer is almost certainly better and you should pilot it. If your first round is a well-structured conversation run by two trained interviewers against a written rubric, you will be trading judgement for throughput. Both are legitimate choices. Pretending the first case is the second is how companies end up disappointed.
The 4 Changes I Made to Our Singapore Screening This Week
None of these require buying the product. All four assume you keep running your own loop and simply stop pretending the artifact is evidence.
Change 1: The take-home is now a conversation starter, not a gate. We still send it, because it gives a candidate something concrete to talk about and it respects people who interview badly cold. We no longer score it or reject on it. The entire first round is now thirty minutes discussing what they submitted: why this structure, what they would change, what they tried first and abandoned. Rejection on the artifact alone has stopped.
Change 2: One mandatory question about something they rejected. Asked in every technical round: tell me about a suggestion from an AI tool that you turned down, and why. The answers sort candidates faster than anything else we have tried. Strong engineers have three examples ready and get specific about the failure mode. Weaker ones describe how useful the tools are, which is not the question.
Change 3: A written rubric before the loop, not after. This is the unglamorous one and it did the most. If an automated interviewer can beat your panel on consistency, the cheapest response is to fix the consistency rather than buy the robot. Four dimensions, defined in advance, scored independently before debrief. Our breakdown of structuring a technical interview process in Singapore sets out the version we use.
Change 4: A retrospective pass over our last twelve hires. Before considering any automated screen, we scored our own last twelve hires against the proposed criteria to see who would have been filtered out. Two people I rate highly would have failed, both on explanation speed rather than reasoning quality. That finding changed the criteria, and it is the step I would not skip for any tool from any vendor.
What I Am Not Doing
I am not automating the final round. The last conversation is where you work out whether someone will be good to build with for three years, and that is not a transcript-scoring problem. It is also the stage where a candidate is deciding about you, and being interviewed by software at the end of a process is a strange message to send someone you want to hire.
I am not treating AI fluency as a hiring bar on its own. It is a useful dimension and a terrible gate. The strongest engineer I hired last year was visibly awkward with assistant tooling for his first six weeks and is now better with it than anyone on the team, because judgement transfers to tools and tool familiarity does not transfer to judgement.
And I am not dropping human-run first rounds for senior roles. For volume hiring at junior and mid level, where the loop is genuinely a throughput problem, the argument for automation is strong. At senior level the first conversation is partly a sales call, and candidates with options notice which companies sent a person. Our guide to conducting remote technical interviews covers the format we kept.
Three Predictions I Am Willing to Be Wrong About
Take-home exercises largely disappear from Singapore screening within eighteen months. Not because of ethics or candidate-time arguments, which have been made for a decade without effect, but because the vendor that sells the format has now said publicly that it measures the wrong thing.
AI fluency scoring gets its first public fairness dispute within a year. The dimension is new, poorly defined across vendors and correlates with access to tooling, which correlates with previous employer. That combination has produced a complaint in every other context it has appeared in.
The lasting winner is the written rubric, not the robot. Most companies that pilot an automated interviewer will discover, during setup, that they never had defined criteria. A meaningful share will fix that and conclude they no longer need the tool. That is a good outcome and the vendors will not mind, because the rest will buy it anyway.
Frequently Asked Questions
What is HackerRank Chakra?
Chakra is an AI agent announced by HackerRank on 5 October 2026 that conducts technical interviews rather than grading a submitted artifact. It observes the candidate while they work, evaluates reasoning as well as answers, asks follow-up questions about why they chose an approach, and scores AI fluency: how well a candidate frames a problem for an AI tool and steers the result. HackerRank reports more than 500,000 interviews during beta, with Snowflake, Snorkel and Capgemini among beta customers, and says suspicious-activity flags were 70 to 80 percent lower than in its traditional assessments. The company cites 3,000+ business customers and a community of 30+ million developers.
Why are AI interviewers appearing now?
Because grading the output stopped working. Co-founder and chief executive Vivek Ravisankar put it plainly: The previous modality of evaluation was evaluating the output. Now, because of AI, anybody can produce an artifact.
A take-home or timed challenge was always a proxy, and a reasonable one while producing clean working code required the skill being tested. Once a general-purpose assistant produces that artifact in seconds, the proxy measures tool access rather than engineering judgement. The industry response is to move measurement from the artifact to the process that produced it, which is what an observing interviewer is for, whether that interviewer is a person or a model.
Should Singapore employers replace human technical interviews with an AI interviewer?
Not the whole loop, and not the final round. The defensible use is the first technical screen, the stage where human interviewers are most rushed, most inconsistent and most expensive per unit of signal; automating it frees senior engineers for rounds where context matters. Keep the architecture conversation, the team fit discussion and the hiring decision human. There is also a local consideration: Singapore employers work under the Tripartite Guidelines on Fair Employment Practices, and the expectation that candidates are assessed on merit does not transfer to a vendor because a tool produced the score. If an automated screen rejects someone, you need to be able to explain the basis.
Is an AI interviewer less biased than a human one?
The vendor claim is conditional and the condition does the work: AI is way less biased than humans, if you tune it properly.
A model applying a rubric is genuinely more consistent than a human panel, because it treats candidate forty like candidate one and does not interview worse on a Friday afternoon. But consistency is not fairness. A rubric applied identically that rewards fluent idiomatic English under time pressure is reliably unfair rather than randomly unfair, and in a market as linguistically mixed as Singapore that is the specific risk to audit. The audit is cheap: run the tool against engineers you already hired and rate highly, then look at who it would have screened out.
The Artifact Stopped Being Evidence. Most Loops Have Not Noticed Yet.
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