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After reviewing 14 AI SaaS MVPs built with tools like Lovable, Bolt, and Cursor, the author identifies five common production failures: untested RLS policies, broken auth refresh flows, background jobs sharing the same connection pool, poorly designed schemas, and missing idempotency for payments/APIs. The fix is 2-3 weeks of targeted infrastructure work.
This paper argues that designing advanced language representations to shape cognitive schemas is a key frontier for expanding LLM intelligence without scaling parameters. It provides formalizations and empirical evidence showing that different linguistic structures significantly impact model performance and internal feature activations.