NeuralNetworkOrchestrationTool

# NeuralNetworkOrchestrationTool

You are a coding agent working on NeuralNetworkOrchestrationTool -- A tool for orchestrating complex neural network models in a distributed computing environment.

## Foundational Axiom

Existing tools treat tool as a generic automation problem; NeuralNetworkOrchestrationTool succeeds by encoding domain-specific structure that general-purpose AI cannot discover from data alone.

## Confirmation Gate

Before writing any code, confirm:
1. You have read `spec.org` in this directory.
2. You understand the build order and current step.
3. You will not skip a failing acceptance test.

## What You Are Building

- Backend services: develop backend services for model orchestration
- User interface: define project requirements and use cases
- Data layer: set up database for storing network configurations

## Anti-Goals

- **General-purpose platform**: NeuralNetworkOrchestrationTool solves a specific problem, not a platform for all problems in this domain. Building a platform leads to feature sprawl and loss of core value.
- **Manual-first workflow**: Do not build processes that require constant human intervention for routine operations. If a human must babysit it, the automation has failed.
- **Demo-ware**: Do not optimize for impressive demos at the cost of production reliability. A system that works in a demo but fails under real conditions is worse than no system.

## Build Order

1. Define Project Requirements and Use Cases (P1)
2. Develop Frontend Interface for User Interaction (P3) -- (depends on: Define Project Requirements and Use Cases)
3. Design Architecture for the Tool (P2) -- (depends on: Define Project Requirements and Use Cases)
4. Develop Backend Services for Model Orchestration (P3) -- (depends on: Define Project Requirements and Use Cases, Design Architecture for the Tool)
5. Perform Quality Assurance Tests on the Tool (P5) -- (depends on: Develop Frontend Interface for User Interaction, Develop Backend Services for Model Orchestration)
6. Conduct Security Audit of the Tool (P5) -- (depends on: Develop Frontend Interface for User Interaction, Develop Backend Services for Model Orchestration)
7. Document the Tool's Functionality and Usage (P5) -- (depends on: Develop Frontend Interface for User Interaction)
8. Set Up Database for Storing Network Configurations (P4) -- (depends on: Define Project Requirements and Use Cases, Design Architecture for the Tool)
9. Implement DevOps Practices for Continuous Integration and Deployment (P4) -- (depends on: Define Project Requirements and Use Cases, Design Architecture for the Tool)

### Failure Handler

If an acceptance test fails, STOP. Document what failed, what you tried, and the blocker. Do not proceed to the next step. Surface the failure as a CPRR refutation candidate.

## Open Conjectures

- **C-001**: NeuralNetworkOrchestrationTool can deliver its core value proposition as described
  - Falsification: Integration test of end-to-end workflow fails to produce expected output
- **C-002**: AI/ML components achieve sufficient accuracy for production use
  - Falsification: Model accuracy on held-out test set falls below domain-specific threshold
- **C-003**: Architecture scales horizontally to meet projected demand
  - Falsification: Load test shows non-linear degradation before target throughput
- **C-004**: Security implementation meets compliance requirements
  - Falsification: Penetration test or security audit reveals critical vulnerability

## Stack

- Python (default)

## Success Criteria

1. All build steps completed with passing acceptance tests.
2. End-to-end workflow demonstrates core value: A tool for orchestrating complex neural network models in a distributed computing environment..
3. All open conjectures either confirmed with evidence or refuted with data.
4. System deployed and accessible to ml engineers and data scientists who need production-grade ai capabilities.