Auditable AI infrastructure · control planes · audit gates · Blackwell kernel work

I build auditable AI infrastructure for autonomous AI systems.

Control planes, audit gates, and kernel-level debugging for GPU work. My system's own strict audit passes 393 of 393 canonical outputs and headlines that gate on the front page. Auditing AI output is the product; the papers are what it audits. Enoch now makes the quality floor explicit: operational health, promising signals, paper-corpus readiness, and public trust posture are separate claims.

View Enoch View corpus View promising signals Read docs GitHub profile
Generated hero graphic for Enoch showing an agentic research control plane coordinating worker nodes and artifact bundles
393canonical AI-generated artifacts indexed
393/393pass packaging/provenance lint
393/393pass strict claim/evidence audit

Featured project

Enoch: Agentic Research Control Plane

Enoch is an open-source control plane for running bounded AI research workflows end to end. It coordinates idea intake, queue state, pause and maintenance controls, worker preflight, single-lane safety, evidence synchronization, dashboard status, alerting, and publication-style artifact packaging. Start with the local proof, then inspect the quality floor: packaging/provenance lint, strict claim/evidence audit, promising-signal separation, and visible caveats before trusting any generated result.

The project is built on FastAPI with a hard state contract and operated with Codex plus oh-my-codex/OMX-assisted development workflows.

System repository → Research corpus → Promising signals → Documentation website → Enoch launch site →

Architecture

From idea queue to auditable artifact.

Enoch is framed as infrastructure first: state, safety, evidence, and review boundaries before generated output.

Enoch architecture diagram showing idea intake, control plane, worker gate, worker, evidence, corpus, dashboard, and alerts

What I care about

  • Agent orchestration with durable state
  • Local AI infrastructure and worker safety
  • Evidence-grounded automation
  • Queue systems that fail loudly instead of hanging silently
  • Human-visible provenance for generated artifacts

Current stack

  • Python, FastAPI, SQLite-backed control state
  • FastAPI control-plane boundaries with a hard state contract
  • GitHub Actions, branch protections, release packaging
  • Worker-gated local execution
  • Codex-assisted development and oh-my-codex/OMX operations

Provenance matters

The generated papers in the Enoch corpus are released as AI-generated research artifacts. Separately, the promising-signals repo preserves bounded useful or scale-blocked leads that are not validated papers and not counted as corpus papers. I am not claiming human authorship of the paper prose, arguments, or generated results. The quality floor is deliberately public: operational health, promising-signal preservation, paper-corpus readiness, and public trust posture are separate review layers. The release is about the surrounding system: control-plane design, workflow reliability, evidence capture, provenance metadata, strict audit visibility, and public packaging.