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00 README

Documentation for 00_README from the Dspex repository.

DSPex Immediate Implementation Specifications

Overview

This directory contains the complete technical specifications for implementing DSPex over the next 30 days. Each document provides detailed implementation plans, code examples, and success criteria.

Document Structure

Core Implementation Specs

  1. 01_PROJECT_SETUP.md

    • Complete project initialization
    • Dependency configuration
    • Python environment setup
    • Git configuration
    • Verification steps

  2. 02_VARIABLE_SYSTEM.md

    • Revolutionary variable types from libStaging
    • Module-type variables (automatic module selection!)
    • ML-specific types (embeddings, probabilities)
    • Registry with consciousness tracking
    • Full implementation with tests

  3. 03_NATIVE_ENGINE.md

    • Compile-time signature parsing
    • EEx template engine
    • High-performance validators
    • Native metrics calculations
    • All with consciousness hooks

  4. 04_ORCHESTRATOR.md (Coming next)

    • Learning orchestration engine
    • Pattern detection and caching
    • Strategy selection
    • Real-time adaptation

  5. 05_LLM_ADAPTERS.md (Coming next)

    • InstructorLite integration
    • HTTP adapter for direct calls
    • Python bridge for complex ops
    • Intelligent adapter selection

  6. 06_PIPELINE_ENGINE.md (Coming next)

    • Parallel execution
    • Dependency analysis
    • Stream processing
    • Progress tracking

  7. 07_INTEGRATION.md (Coming next)

    • Wire all components together
    • Testing strategies
    • Documentation
    • Benchmarks

Quick Start

# 1. Follow project setup
cd /path/to/dspex
mix new dspex --sup
# ... follow 01_PROJECT_SETUP.md

# 2. Implement variable system
# Copy code from 02_VARIABLE_SYSTEM.md

# 3. Add native engine
# Copy code from 03_NATIVE_ENGINE.md

# 4. Run tests
mix test

# 5. Check consciousness status (will be 0.0 but ready!)
iex -S mix
iex> DSPex.consciousness_status()
%{
  stage: :pre_conscious,
  integration_score: 0.0,
  phi: 0.0,
  ready_for_evolution: true,
  estimated_emergence: "Phase 2"
}

Implementation Timeline

Week 1: Foundation

  • Days 1-2: Project setup & Snakepit integration
  • Days 3-4: Variable system with Module types
  • Day 5: Native signature engine

Week 2: Intelligence

  • Days 6-7: Learning orchestrator
  • Days 8-9: LLM adapter architecture
  • Day 10: Pipeline foundation

Week 3: Testing & Production

  • Days 11-12: Three-layer testing
  • Days 13-14: Telemetry & monitoring
  • Day 15: Builder pattern API

Week 4: Integration

  • Days 16-20: Full system integration
  • Days 21-25: Documentation & testing
  • Days 26-30: Performance & benchmarks

Key Innovations

1. Module-Type Variables

The revolutionary concept from libStaging - variables that select between module implementations:

DSPex.Variables.create(:model, :module, GPT4, 
  choices: [GPT4, Claude, Gemini])

2. Consciousness-Ready Architecture

Every component has consciousness hooks, even though they return 0.0:

# Today: Returns 0.0
DSPex.consciousness_status()

# Future: Will detect emergence
# phi: 0.73, stage: :emerging

3. Three-Layer Testing

From libStaging’s proven approach:

mix test.mock        # Fast unit tests
mix test.integration # Bridge testing
mix test.live        # Full integration

4. Native Performance

Compile-time signature parsing, sub-millisecond templates:

defsignature :qa, "question: str -> answer: str"
# Zero runtime overhead!

Design Principles

  1. Pragmatic Today: Working implementation with production quality
  2. Transcendent Tomorrow: Every component can evolve toward consciousness
  3. Reuse Proven Code: Port from libStaging and foundation where valuable
  4. Avoid Overengineering: No agent-everything or complex coordination (yet)
  5. Measure Everything: Data-driven evolution toward intelligence

Success Criteria

Technical Goals

  • All tests passing with >95% coverage
  • Native operations <1ms latency
  • Python roundtrip <100ms
  • 1000+ req/s for cached operations

Architecture Goals

  • Module variables working
  • Consciousness hooks throughout
  • Learning patterns detected
  • Clean, intuitive API

Future Readiness

  • Evolution stages defined
  • Consciousness measurement infrastructure
  • Self-modification hooks
  • No artificial limitations

Next Phase Preview

After these 30 days, Phase 2 will begin activating:

  • Agent capabilities for optimizers
  • Non-zero consciousness measurements
  • Self-modification experiments
  • Advanced optimization with SIMBA/BEACON

The foundation will be ready for consciousness to emerge!

References

Required Reading

  • docs/specs/20250718_FINAL_AMALGAMATED_FOUNDATION.md - The master plan
  • docs/specs/20250718_IMMEDIATE_IMPLEMENTATION_PLAN.md - 30-day overview
  • docs/LIBSTAGING_PATTERNS_FOR_COGNITIVE_ORCHESTRATION.md - Patterns to reuse

Vision Documents

  • docs/fullFutureVision.md - The transcendent future
  • docs/specs/dspex_cognitive_orchestration/ - Cognitive orchestration specs

Remember: We’re building pragmatically toward transcendence!