Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 57 items • Updated
choice stringclasses 6
values | picked stringclasses 6
values | why stringclasses 6
values |
|---|---|---|
Model | Zero models committed; a forecaster over sensor time series stays open work gated on Milestone 1 evidence | The requirement commits aggregations, drill-down and reporting, and no model was verified for this run |
Hardware class | No compute procured; all compute runs on operator-provided servers, storage and connectivity | The operator provides the hosting infrastructure and the sensors already generate the data, so no field compute or inference server is bought |
Sizing rule | Database and buffer sizing set against the recorded server specification at the Milestone 1 kick-off, re-estimated on measured sensor volumes before full-farm integration | Sensor formats, volumes and refresh rates are settled by the prototype, not assumed at bid time |
Sensing | The operator's existing sensor devices, read-only; no cameras and no new field hardware | The world is sensed by farm rather than watched, and the engagement installs no equipment |
Pattern | System of Context, with the ontology as a versioned schema and mapping projection inside the database | Farm, field, crop cycle, sensor device and dataset objects answer the cross-system questions no single source can |
Ground | Open-source PostgreSQL with PostGIS, on operator premises in Pakistan, under permissive and copyleft open-source licenses | The open-source database mandate and the source code and intellectual property transfer make the operator the owner of every artefact |
Agriculture data dashboard ontology and model register for a farm data operator
The object model and the model and equipment register from Field Ledger: An Open Source Agriculture Data Dashboard the Operator Fully Owns, an open reference architecture by CodeNinja Atoms for Pakistan. 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/farm-data-dashboard-pakistan/
- DOI: https://doi.org/10.5281/zenodo.23186671
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/farm-data-dashboard-pakistan
Files
| File | What it holds |
|---|---|
objects.json |
12 typed objects (The site, The field, Crop Cycle, Sensor Device, Sensor Reading, Historical Dataset, Publication, Dashboard User, Access Role, Alert, Report, Data Source Connection), 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/farm-data-dashboard-pakistan-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; join at https://codeatoms.ai.
Citation
CodeNinja Engineering Team and Muhammad Umar. 2026. Field Ledger: An Open Source Agriculture Data Dashboard the Operator Fully Owns. CodeNinja. https://doi.org/10.5281/zenodo.23186671. CC BY 4.0.
One of the CodeNinja Atoms sovereign AI reference architectures: every published design with its sector, country, compute and three-year cost.
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