November 5, 2026·6 min read
Picture the candidate you most want to hire. Twenty minutes into the interview the service will not start. On a real machine they would run docker compose logs, notice the database is still booting, add a health check and move on. That five-minute detour is the best evidence you will get all day of how they work.
Now put the same candidate in a canvas that only exposes the editor, with an AI asking the follow-up questions. The detour never happens, and you never see it.
That is the choice the hiring market is quietly making this month. HackerRank's CEO published a letter that opens with "We're sorry we made you grind leetcode" (HackerRank), and on 5 October the company made Chakra, its AI interviewer, generally available (TechCrunch). The letter promises "real work in code repositories" instead of algorithm puzzles, and an "agentic development environment" instead of an IDE.
We agree with the diagnosis. Puzzle screens never told a hiring manager much about how an engineer works, and we have argued as much in why HackerRank scores don't predict performance. We disagree with the cure. This post is for hiring managers and engineering leads choosing between the new AI-heavy assessments: what Chakra actually changes, what it still hides from you, and when a real environment with a human interviewer is the better call.
Chakra's product page describes the candidate's side plainly. The candidate gets access to a sample repository, and Chakra "opens a live canvas like a dev environment" where the candidate does the work with a chat panel acting as an AI assistant (HackerRank Chakra). Chakra then asks follow-up questions about the candidate's choices. The pitch is that one AI-led session replaces the recruiter screen, the take-home and the engineer's follow-up interview.
HackerRank is not alone. In the same month, Codility added Codex next to Claude Code inside its VS Code interview environment, CoderPad launched a plan with Claude Code and Codex running as agents in its editor, and CodeSignal added agentic assessments with Claude and Codex to its pricing page. The whole category moved in one direction at once: give the candidate an AI tool, and in most cases, let an AI run the interview too.
That is real progress over a timed puzzle. It also leaves two questions open.
"Like a dev environment" is an honest phrase, and the most important word in it is "like". A canvas around a sample repository is still a test harness. It decides in advance what the candidate can touch.
What a candidate usually cannot do in a canvas is the part of the job that happens outside the editor:
jq, a profiler or a database client installs it. In a harness, the toolset is whatever the vendor packaged.journalctl, ss -lntp, a full disk, a cron job with the wrong PATH. We walked through six of these in the Linux troubleshooting interview, and none of them exist inside an editor.The difference matters most for the roles where the editor is the smallest part of the work: backend, platform, DevOps, SRE, data engineering. For a front-end component or a single function, a good canvas may be all you need.
The second shift is less visible but bigger. Almost every vendor in this space is racing to build the AI interviewer: HackerRank with Chakra, HackerEarth with OnScreen, CodeSignal with its AI interviewer that now asks one question at a time. Codility went the other way and published the case against AI interviews. The market is split.
Our position is the one we set out in what happens when you let AI grade the technical interview: use AI to filter, use humans to decide. An AI interviewer is good at consistency and at volume. It is weak at the things that decide a senior hire: whether the candidate's judgement would hold up on your team, whether their trade-offs match how your system actually fails, and whether you would trust them on call.
Those are judgements your engineers are better placed to make than a model, because they know the system the candidate would be joining. Replacing the engineer's interview with an AI one removes exactly the person who could make that call.
This is how we run it at EasyEnv, and it is the alternative we would suggest evaluating against Chakra.
The candidate gets a real working environment. Not an editor, and not a bare VM either: a full Linux environment built from your stack, with your repository, your services and their dependencies already running, and the candidate's own tools available, AI assistants included. If the candidate wants to install something, they install it. If they break it, it breaks.
A person runs the interview. Either live, with one of your engineers in the session, or as a take-home that the candidate does in the same environment on their own time. The candidate is never talking to a bot that decides their outcome.
AI helps the reviewer, not the candidate's fate. Sessions are recorded, and an AI pass can summarise what happened, flag where the candidate got stuck and point the reviewer at the moments worth watching. The hiring decision stays with the engineer who reads it. We wrote about what to look for in how to test whether an engineer knows how to use AI.
The result is that the interview looks like the first week of the job, and the person judging it is someone who has done that job.
Being even-handed here matters more than winning the comparison.
| AI interviewer in a canvas (Chakra and similar) | Real environment, human interviewer (EasyEnv) | |
|---|---|---|
| Best at | High-volume early screens | Final rounds and senior hires |
| What the candidate works in | A harness around a sample repo | A full working environment of your stack |
| Who asks the follow-up questions | An AI interviewer | Your engineer, live or after a take-home |
| Role of AI | Interviewer and assistant | Assistant to the candidate, summariser for the reviewer |
| Engineer time per candidate | Close to none | Real, and spent where it counts |
| Signals on operating a system | Limited to what the canvas exposes | Logs, services, failures, recovery |
If you are screening hundreds of applicants for a junior role, an AI interviewer can save real time, and the engineer hours a human interview costs are hard to justify at that volume. If you are deciding who joins your platform team, or who will be on call next quarter, the round that decides should happen in an environment that is real and with a person who can judge it.
Many teams will reasonably use both: an automated screen at the top of the funnel, and a real environment with a human for the round that decides.
HackerRank is right that the puzzle era is ending. The open question is what replaces it. Before you choose, ask any vendor two things:
If you want to see what a real environment looks like for your own stack, you can build one on EasyEnv and run your next final round in it.
Run live coding sessions and take-home challenges in real production environments. Watch sessions back, score consistently, and hire with confidence.
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