Etz Chaim AI¶
Ten cognitive faculties. Ten distinct failure signals. One probe orchestrator that can fix them.
Why this exists¶
When a standard AI system fails, you get one error : the model hallucinated / was biased / repeated itself. You cannot tell which cognitive faculty actually broke, and fixing it usually means re-prompting and hoping.
Etz Chaim AI splits cognition into 10 explicit faculties — each with its own tests, tunable parameters, and persistent state. A built-in probe orchestrator monitors these faculties and can detect drift, name it, and apply corrections.
What is in v0.1.0¶
| Component | Role | Tests |
|---|---|---|
bridge/ |
loads the 1696-item specification corpus into code | 16 |
probes/ |
probe orchestrator + rectifier (observe / suggest / act) | 25 |
configurations/ |
6 composition layers + persistent learning trace | 150+ |
| internal corpus | primary-source specification (YAML) | 10+ |
Start here¶
- Getting started — install and run your first self-rectification cycle.
- Architecture — how the pieces fit together.
- Advanced — opt-in : the structural framework that inspired the architecture.
Project scope¶
This project is :
- An AI architecture with capability-level failure diagnostics.
- A set of small autonomous Python modules (~500 LoC each), each adding one cognitive capability.
- A specification corpus transposed with rigorous care and epistemic labeling (E1–E6).
This project is not :
- A generic framework for orchestrating arbitrary LLM agents.
- A trained model (we call Claude / Ollama / OpenAI under the hood).
- A general-purpose reference for any specific intellectual tradition.