Experimental OpenCode-first orchestration plugin inspired by the Tang Dynasty's Three Departments and Six Ministries: draft, review, dispatch, execute, and audit.

Proof of Human Intent (PoHI) - Cryptographically verifiable human approval for AI-driven development

A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

Sifaka is an open-source framework that adds reflection and reliability to large language model (LLM) applications.

Lean orchestration platform for enterprise AI — where each decision costs hundreds. State machine core, HITL as a first-class state, corrections that accumulate. First use-case being Coding agent. Open research, early stage.

This project integrates Hyperledger Fabric with machine learning to enhance transparency and trust in data-driven workflows. It outlines a blockchain-based strategy for data traceability, model auditability, and secure ML deployment across consortium networks.

ADOS Turn AI into a repeatable, auditable SDLC: ticket → spec → plan → PR → quality gates → release. Agents, templates, skills, scripts, and reference workflows.

Stop Claude Code from doing irreversible damage. Policy-gated execution + receipts so you can ship agents without sweating production.

Governance layer for human–AI collaboration: evidence boundaries, audit artifacts, and change admissibility.

Auditable transparency pack for OddsFlow: verification rules, schemas, sample logs, and versioned notes (not betting tips).

Determinism: Bit-identical outputs under identical inputs, configuration, and execution environment.

Governance beneath the model. Custody before trust. Open for audit. Constitutional Grammar for Multi-Model AI Federations, Firmware Specification • Zero-Touch Alignment • Public Release v1.0

Methodology defining structured workflow topology and reproducibility guarantees for governed research.

SMALL (Schema, Manifest, Artifact, Lineage, Lifecycle) is a formal execution state protocol that makes AI-assisted work legible, deterministic, and resumable by separating durable state from ephemeral execution.

AI that tries to show its work. Transparent, private, and easy to run yourself

Open, verifiable AI-driven football market analytics project for detecting mispriced bookmaker odds.

A neutral protocol for coordinating intent across humans and agents. Goal lifecycle, state, auditability.

🔥 Emergent intelligence in autonomous trading agents through evolutionary algorithms. Testing zero-knowledge learning in cryptocurrency markets. Where intelligence emerges, not designed.

Reference implementation of the Spiral–HDAG–Coupling architecture. It combines a verifiable ledger, a tensor-based Hyperdimensional DAG, and Time Information Crystals to provide a new kind of memory layer for Machine Learning. With integrated Zero-Knowledge ML, the system enables trustworthy, auditable, and privacy-preserving AI pipelines.

Not new AI, but accountable and auditable AI