Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 37 items • Updated
choice stringlengths 6 647 | picked stringlengths 6 104 | why stringlengths 4 120 |
|---|---|---|
Detection and | RF-DETR fine tuned on site footage | Apache-2.0 from package to checkpoints; |
segmentation | the JV owns and hardens weights a | packaged safety product would rent |
Tracking | Roboflow trackers, ByteTrack class | Apache-2.0 motion only identity without the AGPL exposure of the BoxMOT collection |
Forecasting | Chronos-2 universal forecasting | Apache-2.0 with no field of use restriction; weights may be held, fine tuned and redistributed on site |
Embedding | BGE-M3 hybrid multilingual embedder | MIT with no field of use restriction; multilingual retrieval across a workforce tagged by language |
Frontier | One node of eight 141 GB HBM GPUs (H200 | 1,128 GB holds the 904 GB of FP8 weights, |
hardware | class) | KV cache and activations with headroom for sessions |
Edge compute powered enclosures | Jetson Orin industrial edge class in solar coverage at gates and laydown areas | Detection survives patchy private LTE |
Site inference | Eight L40S class PCIe GPUs, 48 GB class | Forecasting, embedding and breach |
server | aggregation on JV hardware in the site data | room |
Camera class Profile S/T; thermal at 9 Hz or less | Fixed visible cameras, Frigate ready ONVIF a reuse survey holds; the 9 Hz ceiling keeps | The existing 38 fixed cameras serve where US origin thermal units outside the 6A003.b.4.b export license to Saudi Arabia |
Sizing rules gain, with a cleaning interval as a design parameter | Enclosure thermal budget including solar accuracy problems unless the thermal budget is engineered first | Dust, glare and 50 C summer ambient fail as |
Sensing haulage GPS for 40 tracked trucks plus proposed trackers for 20 mixers, weather station, biometric turnstiles, batch plant SCADA | Crane anti-collision logs, crawler telematics, crews actually did, joined through the adapters | Ground truth of what cranes, trucks and |
Pattern recommendation with a named approver PA R T I I I · C H A P T E R 9 Shadow Mode Comes Before Any Flag Is Trusted Three phases with counted items and hard gates prove the slip forecast and the breach detection against the record before anyone acts on a flag. Chapter 8 fixed the models, their licenses and the har... | Adapter tier, one object model, never directly, and every re-sequencing | Every source enters through an adapter, output stays a recommendation a person approves |
Construction structure phase ontology and model register for a site in Saudi Arabia
The object model and the model and equipment register from Structure Phase Watch: Live Production, Crane and Delivery Evidence for Every Pour on a Construction Site, an open reference architecture by CodeNinja for Saudi Arabia. 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/structure-phase-construction-saudi-arabia/
- DOI: https://doi.org/10.5281/zenodo.23126448
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/structure-phase-construction-saudi-arabia
Files
| File | What it holds |
|---|---|
objects.json |
15 typed objects (tower crane, crawler crane, casting bed, precast unit, truck, batch ticket, P6 activity, permit, observation form, gate movement, delivery window, lift schedule entry, pour, worker, zone), 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/structure-phase-construction-saudi-arabia-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. Structure Phase Watch: Live Production, Crane and Delivery Evidence for Every Pour on a Construction Site. CodeNinja. https://doi.org/10.5281/zenodo.23126448. CC BY 4.0.
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