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JAPIE: How an Incoherent Mess Became a Self-Improving AI Orchestrator
April 2026 — on shipping fast, discovering chaos, systematically fixing it, and then building a system that improves itself
There’s a particular kind of technical debt that emerges when you ask an LLM to design an AI orchestrator without a clear spec. The result lands in your codebase looking plausible: proper error handling, metrics collection, a learning loop. But when you actually try to run it against real workflows, you discover the wires are loose, the assumptions are broken, and half the system assumes the other half already exists.