ATOMA / ACTIVE COACH SYSTEM
An AI system built around action.
Auratomic replaces passive schedules with an active autonomous AI Discipline Coach (ATOMA) holding full read/write access to your goals, learning graphs, and local memory indexes.
Python Syntax Check
Execute 45-minute deep focus interval on vector databases.
Directed Acyclic Skill Graphs
Prerequisites are mapped into state nodes. Hover or click nodes in our Compose Canvas mockup below to explore dependencies and unlock learning paths.
Python Core
Fundamental scripting, syntax mapping, and package installation dependencies. Required to initialize vector processing.
Vector Memory Manager
ATOMA partitions context vectors on-demand. Review Acquired memory parameters and autonomous system change history logs:
User Persona Vector
Stores primary user attributes, learning styles, and technical roles (e.g. software engineer, focused intervals, sleep schedules).
Objective Milestones
Tracks high-level milestones like "Build compiler in Rust" or "Finish Android SDK refactor" and links them to skill graph nodes.
Coaching Dialect & Tone
Adjusts ATOMA dialog structures. Users can toggle preferences between supportive, metric-driven, or military-hard coaching structures.
Change History (Diff Logs)
VM comparisons of Memory Change Entries displaying old parameters vs newly inferred values: