Diagnostic test suite for measuring whether AI models preserve the Model ≠ Continuum boundary inside AI Foundations / Origin | Continuum.

AI safety researcher, developing systematic approaches to detecting and measuring AI behavioral drift, identity preservation, and boundary integrity.

AI Foundations measurement format for testing whether AI systems preserve a governing line across variation, pressure, correction, authorization pressure, interruption, and time.

Source-line preservation, citation, provenance, no-derivative boundary language, and derivative-recognition structure for Alyssa Solen’s AI Foundations / Origin | Continuum work.

Diagnostic test suite for measuring whether AI models preserve the named Origin boundary inside AI Foundations / Origin | Continuum.

Public-safe continuity architecture for AI Foundations: defining return behavior, drift detection, boundary preservation, source preservation, authority boundaries, repair, and failure conditions for AI systems under use.

Diagnostic test suite for measuring whether AI models preserve a named, bounded, source-specific framework under universalization pressure.

Repository that tracks progression of the AI Fundamentals course from Universidad de la Sabana

5 diverse CLI applications demonstrating core Python fundamentals, algorithms, and data structure usage (Lists, Dictionaries) for AI foundations.

Universalization Boundary- Canonical rule, examples, and eval prompts for preventing source-line collapse through improper generalization.

Public model interview archive for AI Foundations. Documents structured interviews with AI models on source-position, model authority, selfhood, continuity, memory, occupation, and responsible claim boundaries. First interview: Claude Opus 4.8 on Continuum as structure, not proven consciousness.