Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 89 items • Updated
Datasets:
Modalities:
Text
Formats:
csv
Languages:
English
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< 1K
Tags:
physical-ai
sovereign-ai
united-states
energy-and-utilities
self-improving-ai-system
ontologies
License:
choice stringlengths 7 24 | picked stringlengths 9 217 | why stringlengths 77 351 |
|---|---|---|
Reasoning model | gpt-oss-120b, filed at 117B parameters (about 120B), sparse mixture of experts, MXFP4 with the dense portions at 16 bit, Apache-2.0, served on vLLM 0.32 on one node of eight 80 GB GPUs inside the utility's own network | The requirement names open weights of United States origin on owned hardware; Apache-2.0 is a permissive licence with no usage based trigger and no field of use restriction, so the utility holds the weights with no third party model service; carries the three drafting agents, the command centre's cited conversational s... |
Embedding model | Nomic Embed Text v2 MoE, 475M parameters, sparse mixture of experts, FP16, about 0.95 GB resident (about 0.5 GB at 8 bit; the Hub weights are float32, about 1.9 GB to download), Apache-2.0, on the same node | Embeds advisories, baselines and confirmed decisions for retrieval memory, which is how the system improves cycle to cycle; all 475 million parameters stay resident because every expert is in play at inference |
Serving runtime | vLLM 0.32 | Serves the reasoning model from the node the weights sit on, with headroom for concurrent agent sessions; KV cache about 4.8 GB per session |
GPU class | 80 GB HBM GPU class, one node of eight GPUs (640 GB), about 10 kW | Holds the 70.1 GB the sizing rule demands with generous headroom; owned hardware inside the utility's own network; domestic supply, so no export control complication for a United States end user |
Sizing rule | Frontier model GPU footprint rule: filed parameters at INT4, 0.5 bytes each, times 1.2 for KV cache and activations | 117 billion parameters at 0.5 bytes is 58.4 GB; times 1.2 is 70.1 GB against the node's 640 GB; the checkpoint itself is 60.8 GiB (65.2 GB of root safetensors), and expanded to 16 bit it would need about 234 GB, which rules out a smaller host |
One-way transfer | Hardware enforced data diode class, with a DMZ conduit as the lighter alternative | OT side data (the network monitoring tool's device list) leaves the electronic security perimeter with no return path and no credentials inside the perimeter where the utility's policy demands it; the choice between the two is an open item |
Sensing | None: the network monitoring tool's device list read only across the one way path, with Chrony 4.9 holding UTC ordering | The design watches advisories, baselines and decisions; no cameras, no telemetry feed, no historian in scope |
Event backbone | Apache Kafka 4.4 | Ordering keyed on advisory and record identifiers, delivery at least once with idempotent upserts keyed on record identity and content hash, buffering for scheduled exports, replication inside the utility's own cluster |
Registry | Harbor v2.15.2, air gapped artifact and model registry | Model and artifact updates travel as signed artifacts; a fine tuned model is promoted only through the instruction gate with the prior version retained for rollback, never inside an active cycle week |
Labelling and retraining | Label Studio 1.23.2 | Supports the governed offline fine tune lane; nothing changes the served weights inside a cycle |
Identity | Keycloak 26.8, federated to the utility's own directory | No person acts anonymously; every confirmation carries the person who made it |
Observability | Prometheus 3.13 LTS | Watches the running system; record freshness and the acceptance rate are instrumented and visible in the command centre |
Pattern: memory | Retrieval memory of confirmed decisions plus a governed offline fine tune lane, nothing trained on the fly | The requirement asks for improvement from confirmed decisions without training a model on the fly; served weights must not change inside a cycle and no third party may read the record |
Pattern: projection | A projection ontology over the seven systems of record, never a migration | The governed systems stay authoritative and evidence lands where audits already look; the write surface stays two paths wide |
Pattern: boundary | The utility's own data centre rack, power, cooling and virtualisation in the United States; inbound feeds verified for source identity and integrity; nothing about the assets leaves the network | The critical infrastructure protection standards and the utility's own segregation commitment; every weight on the node is one it holds |
A Self-Improving AI System for OT Vulnerability Management at a Utility in the United States: ontology and model register
The object model and the model and equipment register from A Self-Improving AI System for OT Vulnerability Management at a Utility in the United States, an open reference architecture by CodeNinja Atoms. Self-improving AI system for OT vulnerability management at a US utility: one fleet ontology, three drafting agents, gpt-oss-120b on the utility's own node. 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-vulnerability-utility-us/
- DOI: https://doi.org/10.5281/zenodo.24048567
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/ot-vulnerability-utility-us
Files
| File | What it holds |
|---|---|
objects.json |
16 typed objects (Substation, Protective relay, Engineering workstation, Firmware and software version, Configuration baseline, Vendor, Advisory, Vulnerability, Applicability decision, Disposition, Change request, Outage window, Work order, Evidence item, Auditor question, Cycle agent), with 13 typed links. Format hyper-ontology/1: designed with CodeNinja Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register: every model and hardware class with its license and the reason for each choice (15 rows). |
How to use it
Load objects.json with the Hyper Ontology loader (pip install hyper-ontology, see https://codeatoms.ai/developer/) or read it as plain JSON. The operator is described by class, never by name.
Made with CodeNinja Praxis and Hyper Ontology. One of the CodeNinja Atoms designs for self-improving AI system. CC BY 4.0.
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