# 9320

- **Event:** [AI Engineer World's Fair Hackathon 2026](https://cerebralvalley.ai/e/aiewf-hackathon-2026)
- **When:** Jun 27 at 9:00 AM – Jun 28 at 5:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Shiven Mian](https://cerebralvalley.ai/u/shivenmian)
- **GitHub:** https://github.com/shivenmian/kun
- **Demo video:** https://youtu.be/r4zlj8eG3ag
- **Gallery:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/61

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.

---

Markdown version of https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/61. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt