Arthur, I've been working on a new scheduling algorithm that could cut idle time by almost half—thought it’d be worth seeing how that would affect our team's workflow.

Sure. The core idea is a priority queue that orders tasks by their estimated execution time and resource needs. When a worker becomes free, it pulls the next task that best fits the worker’s current load, minimizing idle wait. I’d integrate it into the task dispatcher module, replacing the round‑robin picker. We keep the existing queue interface, just swap the selection logic. That way the rest of the pipeline—logging, monitoring, error handling—stays unchanged. It should be a drop‑in update, just add the new class and adjust the config to point to it.

I'll write a unit test suite that covers normal scheduling, zero‑time jobs, and jobs that over‑run the estimate. For each test I’ll compare the expected worker assignment to the actual one. For the estimate‑off scenario I’ll create a job with a low estimate that actually takes longer and verify that the dispatcher falls back to a queue of unfinished tasks instead of blocking the worker. In staging I’ll deploy the new dispatcher behind a feature flag, run the full job mix for an hour, and capture the average idle time per worker. If the metric drops by the target amount and no worker stays idle for longer than a minute, I’ll roll it into production.

Will make sure the fallback queue uses a fairness counter so newer jobs are queued after a set number of attempts. That should prevent any single overrunning task from starving the rest. All right, onto the tests.