Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 33 items • Updated
choice stringlengths 12 46 | picked stringlengths 31 85 | why stringlengths 40 133 |
|---|---|---|
Work surface model | GLM 5.3, 753 billion parameters filed, FP8, on one 8-GPU node of the 141 GB HBM class | Bespoke license permits internal commercial use with attribution and no revenue trigger; one node holds the weights with KV headroom. |
Edge detector | RF-DETR, Apache-2.0, Nano to 2XL checkpoints from about 61 to 254 MB at 16 bit | Real-time on edge GPUs, fine tunable on the operator's own footage, fixed auditable classes. |
Site forecaster | Chronos-2, Apache-2.0, about 0.48 GB at FP32 | Zero-shot multivariate forecasting with covariates for the two-hour turn time, queue and reefer predictions. |
Site embedding model | Qwen3-Embedding-0.6B, Apache-2.0, about 1.2 GB at bf16 | The 32K window holds a whole shift's gate and appointment record in one passage. |
Edge tracker | Roboflow trackers, Apache-2.0 clean-room SORT, ByteTrack and OC-SORT | Per-object counts and conflict geometry at no model memory cost, on CPU beside the detector. |
Edge compute class | Fanless IP-rated enclosures with accelerator at the yard blocks, gate and quay | Sized from actual streams and models by bench measurement, never from a datasheet. |
Site inference server | One GPU node, H100-class 80 GB or L40S-class 48 GB | Holds forecaster and embedding model with room for 32K-window activation memory. |
Frontier node | One 8-GPU node of the 141 GB HBM class, about 10 kW | 904 GB required against 1,128 GB usable, inside the room's power envelope. |
Camera estate | Reuse of the 220 existing fixed cameras, decided per camera by six gates | Reuse existing CCTV or not is the first sizing rule; new buys carry no domestic US license restriction. |
Positioning station | RTKLIB with a site-owned GNSS base external positioning service. | Grounds RTG and truck positions without an |
Time synchronization OCXO, linuxptp and chrony | OCP Time Card grandmaster with holdover reads on one clock through power transfer. | Keeps camera frames, PLC cycles and gate |
One-way transfer | Lidi over a hardware data diode | Weights and images move inward; nothing queries back across the boundary. |
Container terminal ontology and model register for truck turn time prediction
The object model and the model and equipment register from Terminal Pulse: Predicted Truck Turn Time and Live Yard Sight for a Container Terminal, 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/truck-turn-container-terminal-us/
- DOI: https://doi.org/10.5281/zenodo.23159331
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/truck-turn-container-terminal-us
Files
| File | What it holds |
|---|---|
objects.json |
12 typed objects (vessel call, container, yard block, RTG, ship to shore crane, truck visit, gate lane, rail cut, reefer plug, yard person, transfer zone, safety event), each with its anchor system, properties, status vocabulary and 11 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/truck-turn-container-terminal-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. Terminal Pulse: Predicted Truck Turn Time and Live Yard Sight for a Container Terminal. CodeNinja. https://doi.org/10.5281/zenodo.23159331. CC BY 4.0.
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