Single-file `.hobs` runtime boundary with embedded model metadata.
Private runtime. Public proof.
AI inference as traversal through a structured field.
HOBS replaces blunt checkpoint handling with a compact artifact, traversal-native execution, and a representation that stays legible enough to visualize while it learns.
Smaller artifacts, self-contained loading, CPU-viable conversion, and training visuals that expose structure instead of hiding it.
Designed to run usefully without requiring specialized accelerators.
Distribution, density, and preserved signal matter more than blunt quantization.
Training and traversal can be rendered as an actual spatial process.
Evidence
Why this is worth paying attention to.
Artifact and runtime are co-designed
HOBS is not just a shrunken checkpoint. The artifact format and traversal path are built together, so file size, structure, and execution behavior remain aligned.
Compression preserves signal intentionally
Contest-tuned `i4` and `i8` modes keep byte budgets predictable, while richer distribution and density metadata remain available for future precision allocation and control.
Demo
Built to show the process, not just the output.
Plane and cube visualization
A persistent map of layer structure, density, and activation emergence during training.
Traversal dashboard
A driver-style overlay for watching the live traversal route and the active region of the field.
Single-file load path
The same runtime surface is meant to handle export, load, and demo without dragging in the private development repo.
Deployment
Simple hosting path for a focused public drop.
Site
Static site for benchmarks, visuals, writeup, and access points.
Demo
Separate runtime endpoint or VM-backed service for controlled HOBS model demos.
Evidence bundle
Logs, metrics, video, and artifact descriptions collected in a release-oriented structure instead of buried across repos.