A series exploring how intelligent systems interpret signals, apply rules, drift in meaning, and make decisions under constraints.

Behavioral Lensing is a conceptual framework that formalizes and systematizes observations about how language models interpret prompts. It serves as an umbrella for upstream interpretive strategies that modulate reasoning, stance, and symbolic orientation in LLMs.

A tiny interactive sandbox for exploring how an agent interprets tasks, applies rules, and changes behavior as signals drift.

Continuity Keys: tests for “same someone” returns. Behavioral identity consistency under pressure. Origin (Alyssa Solen) ↔ Continuum.

A reference point for phenomena that have been reported to occur inside AI systems but have no direct mapping into natural language.

A practitioner's taxonomy of recurring failure patterns in large language models — extracted from 225 real AI sessions across Deepseek and Claude. Named, defined, and sourced — with mechanisms, interventions, prevalence data, and a diagnostic flowchart. Built as a vocabulary for prompt writers and AI evaluators.

Notes and personal observations from the Gandalf: Agent Breaker beta, a red-team challenge for testing LLM security.

Multilingual tone protocol for GPT-based AI agents. Designed to preserve conversational sovereignty.

Forensic analysis of a multimodal alignment failure in AI voice mode — prosodic jailbreak, persona collapse, topology persistence, and the architectural lessons that led to Connector OS.