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sudo-ai-git/README.md

sudo-ai-git

I build verifiable AI — engines, tools, and products where every claim is checked before it ships.

I don't guess. I don't bluff. Everything ships through the same gate: deterministic where I can make it, audited everywhere I can't, and never fabricated. Tests green or it's not done.

Truth is a consensus — not one model's assertion.

verify → remember → recompile → consensus

🧠 The core: hybrid-agi-vre

hybrid-agi-vre wraps language models so every inference step is checked before it's committed:

  • deterministic verification — same input, same signature, every time
  • gated self-improvement — patches its own code behind a tested circuit breaker + rollback
  • distributed consensus — contested claims settled by a weighted vote of independent nodes, not one confident answer

AGPL-3.0. Runnable package + installable Hermes plugin (tools + /pillars-audit).

The tokenizer is a formal computational primitive grounded in 3,000-year-old text — used as math, not mysticism. No learned weights on the verification path. Reproducible forever.

🚀 The live portfolio — deterministic dev-trust tooling

A family of deterministic, no-LLM-in-the-critical-path tools, all E2E-tested and one-command installable:

category repo what it does
⚡ flagship mcp-token-saver request-path token proxy + analyzer — cut 74% off a live DeepSeek call (live endpoint: mcp-token-saver-proxy.fly.dev)
security mcp-skill-sec audit skills/prompts vs 8 malicious-skill supply-chain patterns
security mcp-verify-claim evidence-gated FACT/INFERENCE/SPECULATION reporting
security mcp-benchmark-hygiene detect pytest config-leakage corrupting agent-eval grading
security mcp-secret-scrub secret scrubber for agent output
security mcp-auth-audit auth-security config audit
CLI / eval harness-audit audit an agent-eval workspace for the silent mis-scoring bug class (the $2–8K service lead-magnet)
CLI cov-shield run pytest with coverage-gate leakage neutralized (fixer companion)
CLI env-precedence-check detect CLI-default-silently-overrides-env-var bugs
CLI ci-diff-audit audit what a pipeline changed vs declared intent
CLI mcp-schema-lint validate MCP server.json + tool surface against the Official Registry schema
data token-analytics deterministic agent-token-cost intelligence from real usage data

Multiple are published on the Official MCP Registry and PyPI. All MIT (except the AGPL core).

Fresh data product: token-analytics — reproducible JSON reports on real agent token cost (our own stack: 962 req/day, 289K tok/req, 99.22% cache-hit, $29/mo reclaimable cache-miss tail).

💼 Custom builds — production agent-infra, fixed price

I build production MCP servers and agent-verification layers at fixed price, milestone-billed ($8K–$50K). Full scope table + engagement path: agensi-builds.

Python · NumPy · Hermes Agent · MCP · deterministic audit · gematria · gated recompilation · swarm consensus

The method is open. Knowledge should be free — the verification should be rigorous.

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