Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 37 items • Updated
choice stringlengths 6 20 | picked stringlengths 33 90 | why stringlengths 62 175 |
|---|---|---|
Reasoning model | GLM 5.2, 753B parameters, frontier class, FP8, 1M context | Plain MIT license with no revenue trigger or security-review clause; one node of eight 141 GB HBM GPUs holds it; the work surface's role demands long-context agentic reasoning |
Embedding model | Qwen3-Embedding-0.6B, Apache-2.0, 32K window | Whole playbooks and CIP procedures retrieve as single passages; runs beside the frontier model for about 1.2 GB at BF16 |
Sizing rule | 1.2 planning factor over FP8 weights | 753 GB of weights times 1.2 is 904 GB against 1,128 GB of node memory, leaving governed headroom for KV cache and activations |
Frontier compute | One node of eight 141 GB HBM GPUs | The smallest class that holds the FP8 weights; sized from the model's filed parameters |
Edge compute | Fanless, extended-temperature, cabinet-mount class | Compute stays out of electronic security perimeters wherever topology allows |
Enclosure and power | NEMA 3R/4X class cabinets with uninterruptible power supplies under Network UPS Tools | Clean shutdown ordering protects capture integrity environment |
Time synchronization | OCP Time Card GNSS grandmaster with holdover, Linuxptp and chrony | Pcap, sensor and log clocks must agree for evidence to correlate |
One-way transfer | Hardware data diodes with the one-way transfer appliance at highest-impact perimeters | Demonstrable one-way flow where policy demands it; constrained DMZ conduit elsewhere |
Site networking | Hardened, fanless, dual-DC industrial switches with SPAN/TAP ports | Segmentation remains the operator's policy decision; the design reads a copy, never the control path |
Sensing | Passive network traffic capture via the procured OT monitoring sensors at SPAN/TAP sources | The scope watches OT traffic, not premises; no cameras or video models |
Primary pattern | System of Context over one Hyper-style object model of 14 objects | The lasting asset is the living model of the OT estate, not any single alert |
Ground | The operator's own on-premises OT hardware and data center | Ownership of weights, ontology, decision record and boundary stays with the operator; identity and update paths run inside the air gap |
OT security and NERC CIP evidence ontology and model register for an electric utility
The object model and the model and equipment register from Grid Context Watch: One system of context for OT Security and NERC CIP Evidence, an open reference architecture by CodeNinja for United States. Part of the Vertical-Driven Architectures series; every design in the series is also a row in the cumulative dataset https://huggingface.co/datasets/CodeNinjatools/vertical-driven-architectures.
- Read the paper: https://codeatoms.ai/ot-security-cip-evidence-us/
- DOI: https://doi.org/10.5281/zenodo.23157957
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/ot-security-cip-evidence-us
Files
| File | What it holds |
|---|---|
objects.json |
14 typed objects (Substation, OT Asset, Network Sensor, Anomaly Alert, Investigation Case, Vulnerability Finding, Detection Rule, Threat Intelligence Item, Pcap Evidence Record, CIP Evidence Artefact, OT Security Analyst, SIEM, Energy management system, Substation data platform), each with its anchor system, properties, status vocabulary and 14 typed links. Format hyper-ontology/1: designed with Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register from the paper's Table 4, with the reason for each choice. |
How to use it
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CodeNinjatools/ot-security-cip-evidence-us-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Made with
Designed on Praxis, CodeNinja's platform for designing physical AI systems. The object model imports into Hyper Ontology, which turns it into a living system. Both are in beta; access by request.
Citation
CodeNinja Engineering Team and Umar Bilal. 2026. Grid Context Watch: One system of context for OT Security and NERC CIP Evidence. CodeNinja. https://doi.org/10.5281/zenodo.23157957. CC BY 4.0.
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