TigerFS Mounts PostgreSQL Databases as a Filesystem for Developers and AI Agents

TigerFS exposes a real tension in modern data architecture: we've built elaborate abstraction layers between applications and databases, yet developers still reach for filesystem paradigms when they need simplicity. Mounting PostgreSQL as a directory is clever, but it signals something important about our current tooling gaps. In practice, this works best for read-heavy AI agent interactions and exploratory workflows where you want filesystem semantics without building custom APIs. The operational concern is obvious—you're bypassing connection pooling, query optimization, and audit trails that production systems depend on. I'd view TigerFS as a productive escape hatch for development and experimental ML pipelines, not a replacement for proper data access patterns. For teams standardizing on Postgres, this could accelerate local development velocity, but it shouldn't tempt you away from implementing proper data governance. The real takeaway: if your developers keep asking for filesystem access to data, your current abstraction layer is working too hard.