Altered States Temporal Lab

Automating evolving AI character personas involves loading full profile definitions into a cadence-driven scheduler that executes prompt-based LLM cycles. The system persists per-character identities, journals, and emotion dynamics across cycles to enable long-running autonomous experiments.

Cadence-driven journaling works by executing scheduled LLM invocations that generate character responses and log emotional trajectories. The system aggregates insights from these journals across cycles to reveal cross-character patterns and evolution trends for research analysis.

Yes, you can run autonomous altered-state experiments using OpenAI for LLM invocation and Python with python-dotenv for environment configuration. The system requires OpenAI API access and local storage to persist character states and journals.

Analyzing emotional trajectories across AI personas requires a system that persists per-substance character journals and extracts cross-character patterns. Dashboard-style monitoring observes timelines and state shifts, aggregating insights from character journals to reveal evolution trends.

Setting up persistent character states requires running initial setup to create substance character profiles, then starting a scheduler to enable hourly cycles. The system enforces per-substance cadence and uses local storage to maintain character identities and journals.

A cron-driven scheduler is needed to automate cadence-driven cycles for character evolution. After initial setup creates the substance personas, starting the scheduler enables hourly cycles that drive real-time evolution, learning, and journaling.