Developer builds AI-powered workflow builder that interviews users instead of blank canvas

A developer created Weaver, an AI workflow builder that interviews users about their tasks rather than presenting a blank canvas, inverting the typical workflow tool experience. The project uses separate AI models for conversation and workflow design, with a validator layer in between to ensure accuracy. During development, the creator encountered several technical failures, including a workflow engine that falsely reported success, a broken Imagen model that went offline before the deadline, and Cloud Scheduler permission errors that mimicked unrelated IAM issues. One notable design outcome was that Weaver's capabilities are stored in a Firestore database rather than hardcoded into prompts, allowing the system to automatically recognize new tools like video and music generation without prompt changes. The developer shared these failures publicly to highlight that diagnosing real-world AI infrastructure problems requires testing against actual deployed systems, not assumed API behavior.