LLM Status vs Ship

Track where every AI model is used across your apps, and get warned before one is deprecated or retired.

Assign an issue to SHIP. An agent team of specialists autonomously covers your SDLC, following your practices, verifying, proving, and reporting every change.

About LLM Status

Track where every AI model is used across your apps, and get warned before one is deprecated or retired. Run mm for the full terminal dashboard: it scans the current repo on launch, then you arrow through every model it calls, browse the registry, and see what's new, live. Both pull the signed registry from cdn.llmstatus.ai once, then run offline. The Here and What's New tabs read straight off the signed registry, no account, works offline.

About Ship

Assign an issue to SHIP. An agent team of specialists autonomously covers your SDLC, following your practices, verifying, proving, and reporting every change. It lands faster than teams can verify its quality or account for its cost. Teams pick their own agents, tools, and models, and nothing connects that choice to what ships or the uneven quality it ships at. As agent adoption grows, that gap becomes unreviewed code, unpredictable spend, and outcomes no one can attribute.