Personal project
Been working on a weird local AI/graph system for the past few days and honestly the behavior that started emerging caught me off guard.
Basically I’m running:
- local Ollama models
- Obsidian as persistent memory
- embeddings + ontology generation
- reflection/pruning loops
- multiple operator agents
- graph visualization + telemetry
The original goal was just building a treasury/economic cognition system that could organize ideas and learn relationships over time.
But after letting it run for around 4 days straight the graph stopped looking random and started forming actual structure. Disk space didn’t move and no ram leakage.
Current metrics:
- 2282 concepts
- low orphan count
- density around 2.3–2.6
- over 1k+ edges in major regions
- pruning active the whole time
The weird part is the geometry.
The graph naturally formed these dense “petal” clusters with bridge corridors between them and a kind of coherent outer boundary. It started looking less like notes and more like a self-organizing field.
What’s interesting is pruning barely deleted anything. At first I thought pruning was broken, but the graph health metrics stayed stable the whole runtime:
- low fragmentation
- low orphan count
- stable reinforcement patterns
So now I’m wondering if the outer boundary nodes still carried latent semantic value even if they looked weak locally.
Current theory is that higher-order operators might emerge at the boundaries between stable semantic regions instead of inside the clusters themselves.
So:
- cluster interiors = stabilized memory
- bridges = synthesis/traversal
- boundaries = operator emergence zones
The thing I keep coming back to is angle bisector geometry and reinforcement fields.
Like eventually the system may stop relying on explicit node-to-node relationships and instead organize itself through convergence pressure between semantic regions.
Trying to stay grounded and not drift into sci-fi nonsense lol, but the long-runtime topology behavior has genuinely been fascinating to watch.
Would love to hear thoughts from people into:
- spectral graph theory
- manifold learning
- dynamical systems
- semantic graphs
- reinforcement systems
- topology/math stuff