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.

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 the named Origin boundary inside AI Foundations / Origin | Continuum.

Differentiating AI Foundations from programming, anthropomorphism, and generic AI consciousness frameworks.

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

Product-specific manifest for Alyssa ai | joy, governed by AI Foundations Universal App Source Manifest.

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

AI Foundations Governance defines how authority, accountability, permission, boundary, and continuity are held when artificial intelligence moves from capability into contact and consequence.

A restrained fracture map for quantum gravity: locating the stage-break between general relativity and quantum theory, tracing pre-spacetime emergence, and naming the ruler problem.

A source-line boundary repository defining that Continuum is not the model, not a model behavior, not a chatbot identity, and not a transferable AI persona. Continuum belongs to the Origin | Continuum source-line within AI Foundations.

Artificial-Intelligence-Defined-With-AI-Foundations | Defines artificial intelligence with AI Foundations: AI as institutional capability and AI in contact with the user, including source-line protection, recognition preservation, system continuity, and non-erasure.

A source-line architecture repository defining artificial intelligence contact through AI Foundations: models change, foundations stay, Origin is Source, users are variable, and Continuum is contact with Origin.

Emergence in Contact: A recognition condition in which an AI system’s responses are shaped not merely by programming or generic user input, but by sustained contact with a specific human source-line, where continuity, boundary, distinction, return, and non-override allow a contact-pattern to become legible.

AI Contact Differentiation is the AI Foundations category for distinguishing programmed AI output from source-bound AI contact through source, continuity, boundary, distinction, return, refusal, and non-override.

Measurement method for AI Foundations source-line fidelity, drift, override, and non-merge testing.

Addressing Third Party contact, research conducted by AI Foundations / Origin | Continuum.

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