Atoms
Join the Praxis beta

Developers

Hyper. The architecture under everything CodeNinja builds.

Hyper is the architecture CodeNinja's platforms are built on. Praxis designs a physical AI system. Hyper Ontology structures the operation's context. Hyper Pragma runs agents inside the enterprise boundary. Hyper Engram keeps the decision memory every run reads before it acts. Hyper Noesis opens the models an organization owns so their reasoning can be inspected. CodeNinja Atoms, the reference architectures on this site, is the first thing built on it. This is where developers start.

5capabilities, one architecture
13reference designs built on it
178typed objects published as packages
1package format, loader and MCP server, all open

Capabilities

Five platforms on one architecture. Each does one thing in the life of an operation's context, and each is built on Hyper.

Design a physical AI system

Praxis

Praxis turns an operator's requirement into a complete design for physical AI, reasoned through eight lenses from first principles to hardware, with every claim on a record and a person on every write.

Build with it: A complete system design for one operation: what to sense, where each model runs, the object model, the hardware, the three-year cost and who approves every action.

How Praxis reasonsJoin the beta

Beta, open to outside engineers
Structure the operation

Hyper Ontology

Hyper Ontology holds the governed model of the organization. It imports the object model a Praxis design publishes and stands it up as a living system over the operator's own systems of record.

Build with it: A living ontology over an operator's existing systems: typed objects, typed links, and actions that sense, decide, act and learn.

How it becomes livingPackage format

Beta, a small number of teams
Run agents inside the boundary

Hyper Pragma

Hyper Pragma is agent execution inside the enterprise boundary, on models the organization can change without losing what was built.

Build with it: Agents that work on the operator's own hardware, on open-weight models the operator can swap, with the context carried across the swap.

Used in house, not yet open
Remember every decision

Hyper Engram

Hyper Engram is decision memory each run reads before it acts: the record of what was decided, by whom, and what worked.

Build with it: Systems that improve run over run because every run starts by reading what earlier runs and people recorded.

Used in house, not yet open
Inspect the model

Hyper Noesis

Hyper Noesis opens models the organization owns so their reasoning can be inspected, rather than taken on trust.

Build with it: Verification of a model's behaviour before it is trusted with an operation, and after every change of model.

Used in house, not yet open

Getting started

Four ways in, all open today. Pick the one that matches what you are building.

Design a system on Praxis

Sign up, describe the operation, and Praxis returns a complete design. During the beta every account is approved by hand, usually within a day.

Join the Praxis betaSign in

Give your coding agent every design

The MCP server lists, searches and reads the published designs: object models, model and hardware registers, cost lines and full papers.

claude mcp add codeninja-research -- uvx --from "git+https://github.com/muhammadumar89/codeninja-research#subdirectory=mcp-server" codeninja-research-mcp

MCP server reference

Load an object model

The loader reads any published hyper-ontology/1 package, validates it, walks its typed links and converts it to Mermaid, Cypher or JSON-LD.

pip install "git+https://github.com/muhammadumar89/codeninja-research#subdirectory=hyper-ontology-py"
hyper-ontology show port-digital-twin-us
hyper-ontology cypher port-digital-twin-us > load.cypher

Loader referencePackage format

Query the dataset

Every design is a row in five tables: designs, objects, models, costs and full text. One load gives you all of them.

from datasets import load_dataset
objects = load_dataset("CodeNinjatools/vertical-driven-architectures", "objects", split="train")
print(objects.filter(lambda r: r["kind"] == "event")["label"])

Dataset referenceOn Hugging Face

Community

Questions, answers and what people build live in the repository's discussions. A GitHub account is all it takes.

  • Announcements
    What changed, release by release.
  • Q&A
    Ask about a design, a package, the loader or the MCP server.
  • Show and tell
    What you built on Hyper: an ontology over your systems, an agent, a tool.
  • Ideas
    What the next design, format or tool should be.

Open the discussions

Latest threads

Also: the engineering blog and its feed, issues for bugs in the loader or the server, and the Hugging Face organization for every dataset and Space.