Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 27 items • Updated
choice stringclasses 9
values | picked stringclasses 9
values | why stringclasses 9
values |
|---|---|---|
Language model parameters filed, FP8, MIT license | GLM 5.2, mixture-of-experts, 753 billion weights the cooperative owns; one 8-GPU | Agentic reasoning over the ontology with FP8 node holds it |
Detection model to 68 MB and 2XL at about 254 MB, BF16, Apache-2.0 | RF-DETR, Nano to Large checkpoints at 61 detection on the edge box class, fine-tuned on the cooperative's own fire-season frames | Smoke, pole, vegetation and equipment |
Edge compute class and sealed, NEMA 3R/4 enclosures | Industrial edge accelerator modules, fanless streams, rated for dust, heat, hail and cold | Sized decode-first from the actual camera |
Site inference server center, one node of 8 GPUs of the 141 GB HBM class | Ruggedised server class at the operations sized from filed parameters at FP8 | 1,128 GB against a 904 GB requirement, |
Serving runtimes Runtime or OpenVINO class at the edge | vLLM on site; Triton Inference Server, ONNX a bench measurement of the real streams | The edge runtime is pinned at design against |
Sizing rules thermal beats visible; reuse existing cameras or not; size GPUs from filed parameters | Size edge compute from streams; when measured duty rather than a vendor default | Every hardware choice derives from a |
Sensing camera feeds, truck and drone cameras, mesonet wind and humidity, 310 recloser fault indicators, AMI last gasp from 61,000 meters, crew AVL on 20 trucks | 42 substation PTZ cameras, 12 wildfire thermal only where a coverage gap is proven | Reused through the reuse gates; new fixed |
Patterns read-only systems of record; human-approved write-back; one-way diode for SCADA | System of context; adapters-only ingestion; record authoritative while the platform joins them | Keeps the grid protected and the systems of |
Ground as primary; sovereign cloud for overflow and recovery only | Cooperative servers at the operations center during a storm, and the risk picture cannot live outside the boundary | The model must survive an internet outage |
Wildfire ignition risk ontology and model register for an electric distribution cooperative
The object model and the model and equipment register from Feeder Firewatch: Live Ignition and Outage Risk for Every Distribution Feeder, an open reference architecture by CodeNinja for the 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/wildfire-risk-distribution-us/
- DOI: https://doi.org/10.5281/zenodo.23159328
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/wildfire-risk-distribution-us
Files
| File | What it holds |
|---|---|
objects.json |
14 typed objects (substation, feeder, feeder segment, pole, recloser, meter, pole inspection record, outage event, ignition risk score, red flag warning, wildfire camera station, field crew, work order, PSPS decision record), each with its anchor system, properties, status vocabulary and 12 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/wildfire-risk-distribution-us-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Made with
Reasoned 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 in beta; access by request. Load it with the hyper-ontology loader.
Citation
CodeNinja Engineering Team and Umar Bilal. 2026. Feeder Firewatch: Live Ignition and Outage Risk for Every Distribution Feeder. CodeNinja. https://doi.org/10.5281/zenodo.23159328. CC BY 4.0.
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