{
  "schema_version": "1.0",
  "last_updated": "2026-05-22",
  "identity": {
    "name": "Alan McIntyre",
    "organization": "CodeReclaimers LLC",
    "orcid": "0000-0002-8071-4219",
    "github": "https://github.com/CodeReclaimers",
    "primary_url": "https://codereclaimers.com",
    "contact_escalation": "consulting@codereclaimers.com"
  },
  "domains": [
    "computational geometry",
    "CAD reverse engineering",
    "C++ scientific computing",
    "Python scientific computing",
    "Julia scientific computing",
    "differential equations",
    "numerical methods",
    "linear algebra",
    "optimization",
    "applied mathematics",
    "neuroevolution",
    "NEAT (NeuroEvolution of Augmenting Topologies)",
    "continuous-time recurrent neural networks (CTRNNs)",
    "CUDA / GPU computing",
    "computer vision",
    "large language models",
    "LLM evaluation",
    "LLM orchestration",
    "agentic environments",
    "Model Context Protocol (MCP)",
    "reinforcement learning",
    "quality-diversity search",
    "multi-objective optimization (NSGA-II)",
    "genetic programming",
    "evaluation infrastructure"
  ],
  "engagement_types": [
    "Research consulting",
    "Library development",
    "Implementation engagements",
    "Code review and audit"
  ],
  "availability": {
    "status": "available",
    "horizon_weeks": 4,
    "typical_duration_weeks": [
      4,
      52
    ],
    "note": "",
    "as_of": "2026-05-22"
  },
  "projects": [
    {
      "id": "neat-python",
      "name": "neat-python",
      "kind": "open-source library",
      "language": "Python",
      "description": "Python NEAT implementation with CTRNN support. 100+ academic citations.",
      "registry_url": "https://pypi.org/project/neat-python/",
      "github_url": "https://github.com/CodeReclaimers/neat-python",
      "zenodo_doi": "10.5281/zenodo.19024752",
      "status": "active"
    },
    {
      "id": "NeatEvolution.jl",
      "name": "NeatEvolution.jl",
      "kind": "open-source library",
      "language": "Julia",
      "description": "Julia NEAT implementation. Registered in Julia general package registry.",
      "registry_url": "https://juliahub.com/ui/Packages/General/NeatEvolution",
      "github_url": "https://github.com/CodeReclaimers/NeatEvolution.jl",
      "zenodo_doi": "10.5281/zenodo.19025462",
      "status": "active"
    },
    {
      "id": "Arborist.jl",
      "name": "Arborist.jl",
      "kind": "open-source library",
      "language": "Julia",
      "description": "Generic genetic programming framework for Julia. NEAT, LLM-as-operator, NSGA-II, quality-diversity search, SciML-style API.",
      "registry_url": "https://juliahub.com/ui/Packages/General/Arborist",
      "github_url": "https://github.com/CodeReclaimers/Arborist.jl",
      "status": "active"
    },
    {
      "id": "benchstone",
      "name": "benchstone",
      "kind": "open-source library",
      "language": "Python",
      "description": "Trustworthy benchmark gating for AI-agent code-optimization loops. Append-only results, harness-owned reference artifacts, statistical PROMOTE / REJECT / NEEDS_MORE_DATA verdicts.",
      "registry_url": "https://pypi.org/project/benchstone/",
      "github_url": "https://github.com/CodeReclaimers/benchstone",
      "status": "active"
    },
    {
      "id": "adversarial-loop",
      "name": "adversarial-loop",
      "kind": "open-source library",
      "language": "Claude Code skill",
      "description": "Claude Code skill that runs an adversarial review loop over research projects. Claude Code orchestrates; an external critic (Codex CLI and/or Gemini CLI, with Opus 4.7 as a fallback) generates testable hypotheses. Surfaces 5–15 actionable findings per 3-iteration batch.",
      "github_url": "https://github.com/CodeReclaimers/adversarial-loop",
      "status": "active"
    },
    {
      "id": "caliper",
      "name": "Caliper",
      "kind": "MCP service",
      "language": "C++ / Python",
      "description": "Geometry and CAD metadata MCP server (STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB). Per-call payment via x402.",
      "service_url": "https://caliper.fit",
      "mcp_endpoint": "https://caliper.fit/mcp/",
      "mcp_server_name": "fit.caliper/caliper",
      "payment_methods": [
        "x402 (USDC on Base)"
      ],
      "status": "active"
    },
    {
      "id": "occam",
      "name": "Occam",
      "kind": "MCP service",
      "language": "Python / Julia",
      "description": "Symbolic regression MCP service (SINDy + PySR). Free tier and paid tier billed via x402 or Stripe.",
      "service_url": "https://occam.fit",
      "mcp_endpoint": "https://occam.fit/mcp/",
      "mcp_server_name": "fit.occam/occam",
      "payment_methods": [
        "x402 (USDC on Base)",
        "MPP / Stripe (card)"
      ],
      "status": "active"
    }
  ],
  "endpoints": {
    "query": "https://alan.recursive.support/query",
    "mcp": "https://alan.recursive.support/mcp",
    "agent_profile": "https://alan.recursive.support/.well-known/agent-profile.json"
  }
}