August 2026 | Added an Algorithm Problem to the AI Collaboration Format

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We've added an algorithm problem to the AI Collaboration format.

Until now, problems in this format have been close to real work: gathering requirements, implementing, and writing documentation. That makes them demanding, so most customers have used them for mid-career hiring at the middle and senior levels.

The problem we've added takes the algorithm style familiar from the Coding format and brings it into AI Collaboration. Algorithm problems are easier to tune for difficulty, and candidates generally find them straightforward to approach.

You can now set an easier bar

Because the problem is built on an algorithm, we can offer something less demanding than before. That opens up AI Collaboration for new-graduate and potential hiring, which was hard to cover with the existing problems.

You can evaluate how candidates pin down requirements, not just how they code

The problem statement deliberately leaves out information the candidate needs to produce the right answer. Candidates ask the AI interviewer over chat and confirm the specification themselves as they work.

Because the implementation is graded with hidden test cases, code written without asking the right questions fails the tests. That ties "did they confirm it in conversation" to "does it actually work," which is much closer to how problem-solving plays out on the job.

The problem we've added

The new problem is "Compute the minimum number of meeting rooms from a reservation log."

Candidates read an access log from a meeting-room reservation system and compute the smallest number of rooms needed to hold every reservation. They pick one language to work in from C++, Go, Java, PHP, Python, Ruby, and TypeScript.

The log's specification isn't in the problem statement. That reservations can be updated and cancelled, that lines from other requests are mixed in, how failed requests should be treated, the time granularity, and how overlaps are counted all have to be confirmed by asking the interviewer.

Skills you can evaluate

  • Problem-solving: spotting the details that decide the answer (rebuilding state, filtering out irrelevant lines, handling boundaries) in what looks like a simple counting task
  • Communication: drawing out the parts of the log specification that affect scoring, without gaps or excess
  • Implementation: reconstructing reservation state from the log and computing the maximum overlap correctly and efficiently, including on large logs
  • AI collaboration: making progress with a coding agent

Who it's for

Junior to mid-level engineers who can write basic algorithms and confirm requirements. It also works for new-graduate and potential hiring.

How to use it

You can find and add the problem from the AI Collaboration problem list on the test creation screen.