Time-Series Metrics Store
Data / Observability interview. Build an embeddable metrics store that ingests high-rate points, downsamples into rollups, enforces retention, and answers range queries under a bounded memory budget. Senior signal: bounded memory, correct aggregation across rollup boundaries, and a clear late-arriving-point policy.
- time-series
- metrics
- observability
- data
Start this challenge
Run this in your terminal. You need Node.js 20+ and git.
- The CLI signs you in through the browser if needed and asks which agent you will use: Claude Code, Cursor, or Codex.
- It downloads PROBLEM.md, starter files and tests, inits a git repo, and starts a 120-minute timer. The full problem statement is revealed only once you start.
- You solve it on your own machine, in your own editor, with your own agent. No browser sandbox.
- When you are done,
npx @kodwai/cli submitpackages your code, git history, test runs, agent transcript, and the time you took, then ships it for scoring.
How you are scored
Your session gets a score from 0 to 100 across three axes. The score is dominated by how you direct the agent, the part a careless prompt cannot fake: passing tests is necessary but not sufficient. Every signal cites its own evidence from your transcript, commits, and test runs, and your score comes with a confidence interval instead of false precision.
Direction
How you steer, verify, and decompose.
Spec Precision · Verification Rigor · Decomposition · Recovery · Intent Fidelity · Engagement
Outcome
What shipped, replayed and stress-tested to prove it holds.
Tests · Code Quality · Complexity
Lift
How far you beat a solo AI, not just that you passed.
Edge-Case Coverage · Lift over AI
Read how the AI Collaboration Score for coding agents works for every signal and how the axes are weighted.
Try Time-Series Metrics Store with your agent.
Create a free account, then run the command above. Solving challenges, your score, your profile, and the leaderboard are free for developers.