Cursor vs ML.ai

Give it an issue and it reproduces the bug, edits the code, runs your tests and opens a pull request while you focus on decisions.

The agent proposes a task list and names the files it expects to change. You select the tasks, and edits outside the declared scope stop and ask first.

Each step of a task is routed to the model that fits it, with the vendor abstracted behind an effort setting and a stated outcome.

Launch many jobs across projects at the same time. Each agent runs in its own sandbox and reports back with a reviewable diff.