How does it handle different data types?

It uses a multi-interpreter approach: DuckDB for SQL pattern storage, JAX for Python-based predictive training, and Ruby for discovering skill frequency and coherence.

What is the primary role of Agent-o-rama?

It functions as a Layer 4 learning engine that extracts behavioral patterns from interaction data to generate models used by cognitive surrogate systems.

What are ACSets in this context?

ACSets (Algebraic C-Sets) are used within the Hy bidirectional learner to represent structured relations between threads, messages, concepts, and files.

Can I use this for real-time prediction?

Yes, the InteractionPredictor class is designed to be trained on historical data and then used to predict the next interaction based on a given history.

Agent-o-rama Pattern Learner

Name: Agent-o-rama Pattern Learner
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Extracts behavioral patterns and interaction sequences to train cognitive surrogate systems and predictive models.

소개

Agent-o-rama is a sophisticated learning and pattern extraction engine designed for Layer 4 cognitive surrogate systems. It processes raw interaction sequences to identify and extract temporal, topic, and network patterns using a unique multi-interpreter architecture. By leveraging DuckDB for storage, JAX-powered Python for high-performance prediction, and Ruby for skill discovery, it transforms unstructured data into structured models. This skill is ideal for developers building autonomous agents that need to understand historical context, predict future interactions, and maintain high levels of behavioral coherence through algebraic C-Sets (ACSets) and bidirectional learning.

주요 기능

DuckDB integration for persistent, queryable behavioral pattern storage
2 GitHub stars
Multi-interpreter pattern extraction across Python, Ruby, Hy, and SQL
Skill discovery via condensed stack descent and coherence analysis
Bidirectional thread learning for simultaneous analysis and synthesis
High-performance interaction sequence training using JAX and JNP

사용 사례

Developing cognitive surrogates that predict and mimic user interaction patterns
Extracting latent skill sets and behavioral structures from raw message logs
Building self-validating learning loops for autonomous agent improvement

소개

주요 기능

DuckDB integration for persistent, queryable behavioral pattern storage
2 GitHub stars
Multi-interpreter pattern extraction across Python, Ruby, Hy, and SQL
Skill discovery via condensed stack descent and coherence analysis
Bidirectional thread learning for simultaneous analysis and synthesis
High-performance interaction sequence training using JAX and JNP

사용 사례

Developing cognitive surrogates that predict and mimic user interaction patterns
Extracting latent skill sets and behavioral structures from raw message logs
Building self-validating learning loops for autonomous agent improvement