tractatus/public/locales/en/homepage.json

{
  "hero": {
    "title": "Tractatus AI Safety Framework",
    "subtitle": "Structural constraints that require AI systems to preserve human agency for values decisions—tested on Claude Code",
    "cta_architecture": "System Architecture",
    "cta_docs": "Read Documentation",
    "cta_faq": "FAQ"
  },
  "value_prop": {
    "heading": "A Starting Point",
    "text": "Instead of hoping AI systems \"behave correctly,\" we propose structural constraints where certain decision types require human judgment. These architectural boundaries can adapt to individual, organizational, and societal norms—creating a foundation for bounded AI operation that may scale more safely with capability growth."
  },
  "paths": {
    "intro": "We recognize this is one small step in addressing AI safety challenges. Explore the framework through the lens that resonates with your work.",
    "researcher": {
      "title": "Researcher",
      "subtitle": "Academic & technical depth",
      "tooltip": "For AI safety researchers, academics, and scientists investigating LLM failure modes and governance architectures",
      "description": "Explore the theoretical foundations, architectural constraints, and scholarly context of the Tractatus framework.",
      "features": [
        "Technical specifications & proofs",
        "Academic research review",
        "Failure mode analysis",
        "Mathematical foundations"
      ],
      "cta": "Explore Research"
    },
    "implementer": {
      "title": "Implementer",
      "subtitle": "Code & integration guides",
      "tooltip": "For software engineers, ML engineers, and technical teams building production AI systems",
      "description": "Get hands-on with implementation guides, API documentation, and reference code examples.",
      "features": [
        "Working code examples",
        "API integration patterns",
        "Service architecture diagrams",
        "Deployment best practices"
      ],
      "cta": "View Implementation Guide"
    },
    "leader": {
      "title": "Leader",
      "subtitle": "Strategic AI Safety",
      "tooltip": "For AI executives, research directors, startup founders, and strategic decision makers setting AI safety policy",
      "description": "Navigate the business case, compliance requirements, and competitive advantages of structural AI safety.",
      "features": [
        "Executive briefing & business case",
        "Risk management & compliance (EU AI Act)",
        "Implementation roadmap & ROI",
        "Competitive advantage analysis"
      ],
      "cta": "View Leadership Resources"
    }
  },
  "capabilities": {
    "heading": "Framework Capabilities",
    "items": [
      {
        "title": "Instruction Classification",
        "description": "Quadrant-based classification (STR/OPS/TAC/SYS/STO) with time-persistence metadata tagging"
      },
      {
        "title": "Cross-Reference Validation",
        "description": "Validates AI actions against explicit user instructions to prevent pattern-based overrides"
      },
      {
        "title": "Boundary Enforcement",
        "description": "Implements Tractatus 12.1-12.7 boundaries - values decisions architecturally require humans"
      },
      {
        "title": "Pressure Monitoring",
        "description": "Detects degraded operating conditions (token pressure, errors, complexity) and adjusts verification"
      },
      {
        "title": "Metacognitive Verification",
        "description": "AI self-checks alignment, coherence, safety before execution - structural pause-and-verify"
      },
      {
        "title": "Pluralistic Deliberation",
        "description": "Multi-stakeholder values deliberation without hierarchy - facilitates human decision-making for incommensurable values"
      }
    ]
  },
  "validation": {
    "heading": "Real-World Validation",
    "subtitle": "Framework validated in 6-month deployment across ~500 sessions with Claude Code",
    "case_27027": {
      "badge": "Pattern Bias Incident",
      "type": "Interactive Demo",
      "title": "The 27027 Incident",
      "description": "Real production incident where Claude Code defaulted to port 27017 (training pattern) despite explicit user instruction to use port 27027. CrossReferenceValidator detected the conflict and blocked execution—demonstrating how pattern recognition can override instructions under context pressure.",
      "why_matters": "Why this matters: This failure mode gets worse as models improve—stronger pattern recognition means stronger override tendency. Architectural constraints remain necessary regardless of capability level.",
      "cta": "View Interactive Demo"
    },
    "resources": {
      "text": "Additional case studies and research findings documented in technical papers",
      "cta": "Browse Case Studies →"
    }
  },
  "footer": {
    "description": "Reference implementation of architectural AI safety constraints—structural governance validated in single-project deployment.",
    "tagline": "Safety Through Structure, Not Aspiration",
    "built_with": "Built with",
    "acknowledgment": "This framework acknowledges Te Tiriti o Waitangi and indigenous leadership in digital sovereignty. Built with respect for CARE Principles and Māori data sovereignty."
  }
}