Factory N+1

https://factory-n1.vercel.app/ Every generation has to prove it's better than the last. Factory N+1 is an AI team that redesigns a simulated package fulfillment center. Packages arrive, get sorted and packed, then leave through shipping docks. Agents research warehouse practices, propose equipment layouts, and test whether each change makes the factory faster with fewer mistakes and less damage. A 3D replay shows the original and improved factories processing the same packages side by side. The innovation is a measurable improvement loop. Agents cannot grade themselves or rewrite the simulator. A new layout becomes the next version only if it improves results on separate validation days without worsening the goal checks. The system tests individual edits to identify what actually helped, then records those measured lessons in shared memory. We also test whether that memory helps on new factories. Built with NVIDIA Nemotron on Crusoe Managed Inference, You.com search, TypeScript, and Three.js. A separate training experiment uses Python, PyTorch, Hugging Face Transformers, and LoRA on a Crusoe A100 to teach Nemotron Nano 8B to propose useful layout changes. The main agent workflow reduced the simulator's combined penalty score by 81.8% and shipped all 100 packages with zero damage and zero sorting errors across 15 development scenarios. Its final test split remains locked. Separately, the trained optimizer reduced average penalty by 12.64% on 20 unseen factories, versus 0.14% for the base model, though it did not eliminate damage. Shared memory hurt performance in our small transfer test, so we report that result too. This is a synthetic simulation, not a claim of real-world factory performance.