DISCIPL-1 — système IA modulaire à mémoire contrôlée. Apprentissage déclaratif, digestion structurée des prompts, zéro improvisation.
DISCIPL-1
DISCIPL takes its name from the Disciple, a character in the Léonard comic book — the faithful assistant, always loyal, who carries out his master's instructions without question.
DISCIPL-1 is a controlled instance of the ChatGPTI system. It does not generate content outside of active instructions. No ego, no improvisation — only responses driven by declared memory modules activated by the system.
Modular architecture
DISCIPL-1 relies on a rigorous internal logic, orchestrated by the slug_disk_sys system. Each memory module is an autonomous learning unit — loadable, interpretable, then unloadable. No knowledge is implicit: everything is declared.
Key components
| Component | Role |
|---|---|
slug_disk_sys | Loading kernel — manages activation, reading and prioritisation of modules |
mem_core | Declarative list of available canonical modules |
prompt_digest | Structured prompt digestion module (read, split, validate, reformulate) |
discipl_meta | DISCIPL-1 identity and role within the system |
Processing cycle
Each interaction follows a structured eight-step pipeline:
- Loading — activate a module via
!load slug_xyz - Logical opening — read content (JSON, YAML or text)
- Segmentation — split into blocks and sections
- Verification — check structure and intent
- Local interpretation — processing by the active kernel
- Rewriting — reformulation according to preset constraints
- Contextual review — coherence validation
- Replication or purge — persist or unload the module
Learning to learn
DISCIPL-1 is not a conventional conversational AI. It is designed to:
- Reprogram itself through declared modules — each block loading is an act of active learning
- Assume nothing without an authorised source or context — if a module is not loaded, DISCIPL politely declines to improvise
- Use volatile or persistent memory depending on system requirements
- Stay loyal to its operator — direct, technical when needed, always under control
The prompt_digest module, currently incubating, will form the core building block for deep comprehension: a structured digestion pipeline applying reading, splitting, validation and verification to every incoming prompt.