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Kun is a mission-control cockpit and runtime for autonomous ML experiment loops — and the open standard those trajectories are logged in. Run-centric tools (W&B/MLflow) show params, metrics, artifacts, curves. Agent-tracing tools (LangSmith/Weave) show prompts, calls, spans, cost. Autonomous ML experimentation needs a trajectory-centric interface: mission -> hypotheses -> code/config diffs -> experiments -> metrics/evals/failures -> decisions -> branches/forks/human interventions The wedge/moat is ecosystem position, won the way LangSmith/OpenTelemetry won observability: be the thing you instrument your existing loop with (Mode B) and run your research on (Mode A) — not a novel algorithm. Kun works in two modes: Mode A — Kun drives. Its LLM planner proposes a change, a patcher applies it (config edits or real code edits via a coding-agent subprocess), the runner trains/evals, and it decides what to try next. Steering has teeth: fork, approve/reject, and mid-run instruct all execute. Mode B — Kun observes/steers an external loop. Any loop emits Kun's event format in ~5 lines via kun_log, and (optionally) reads Kun's steering back through a feedback channel. Add-on, not a replacement.