◊ 03_OPEN_SOURCE
Open Source
Contributing back to the collective substrate. Building blocks for the next generation of AI systems.
A symbolic communication protocol that turns natural language into queryable, reusable knowledge. Structured symbols eliminate ambiguity;precision is the goal, token savings are the side effect.
LLMs speak their native language: vector operations, not English. Designed for agent-to-agent communication, system instructions, and knowledge management where precision and reusability matter.
Structured symbols leverage pre-trained associations from training data. Not just abbreviations. Symbols trigger statistical patterns from math, programming, and config files.
RL framework where agents improve through experience and evolutionary pressure. Multi-armed bandit for adaptive selection. Weak behaviors die, strong behaviors survive and propagate.
Published to PyPI. Integrated into HeyContext production. Won Reinforcement Learning Track at Weavehacks-2. Community contributions active.
Agents need environments rich enough to generate natural selection pressure. Quality emerges from competition.
method
Evolutionary selection + continuous learning
validation
Hackathon winner · Production deployed
distribution
PyPI package · GitHub