I build distinct systems for cloud and network operations, GPU/model economics, agent security and identity, commerce operations, physical AI, data/decision systems, and marketing measurement. Each domain has its own technical objective, benchmark units, and evidence boundary.

Current maintenance focus: GRC Claw · Multi-Cloud Infrastructure Control Loop · Network Change Intelligence Twin. This names a bounded stewardship focus; it does not collapse the other domains or claim every repository is maintained at the same cadence.

Sponsor the work · Maintenance record · Sponsorship program and ten levels · Browse every public original repository · Benchmark protocols · LinkedIn

The cards below refresh daily from GitHub's public repository API. Counts exclude forks and private repositories. A push is repository activity, not a deployment or customer adoption. Stars and forks are GitHub attention, not revenue or independent validation. Inspect the machine-readable snapshot and definitions.

The live GitHub Sponsors page offers nine monthly tiers from $10 to $8,000, each with a tier-specific welcome message. Sponsorship funds public maintenance, reproducible tests, documentation, issue triage, and independently reported limitations for the three focus projects. The dated record shows the starting state and will record subsequent work. The program terms describe a tenth, $20,000/month strategic level arranged under a separate agreement because GitHub caps a monthly tier at $12,000. Funding never buys benchmark results, a reference-list position, or control of technical conclusions.

These are separate domains, not one blended product category. Each card links to that domain's original projects. Its two benchmark labels specify what to measure; they are not claimed results. The protocol defines the numerator, denominator, workload, and evidence needed before publishing a score.

Commerce systems reconcile product, order, payment, and customer-operation states. Their tests should measure incident precision and human review effort, not equate detected mismatches with recovered revenue. Commerce Incident Network · Merchant Profit OS.

Model-serving work is about latency, throughput, capacity, and unit cost under a declared workload. LLM Inference Benchmark · GPU Cloud Cost Calculator.

Cloud and network engineering is evaluated on change safety, blast radius, rollback, and operational reliability. Multi-Cloud Infrastructure Control Loop · Network Change Intelligence Twin.

Marketing systems require causal measurement and decision quality, not impressions or synthetic engagement as a substitute for business outcomes. AttentionOS Bench · Audience Swarm Lab.

Physical AI is evaluated on unsafe-action misses, recorder completeness, reproducibility, and safe failure under defined conditions. Physical AI Governor · Robot Black Box.

Agent systems are evaluated on authorized execution, denied-action escape, false denial, and evidence integrity. GRC Claw · Agent Trust Fabric.

Data and decision systems are evaluated on freshness, correctness, and decision improvement against a fixed baseline. Decision World · Outcome Fabric.

The remaining profile repository is listed separately. Domain counts are classification metadata, not a ranking of technical maturity.

The control loop consumes normalized operational evidence from three independently testable cloud adapters:

• Azure Compliance Automation — Azure Policy and Checkov/Terraform evidence.
• AWS Compliance Automation — Security Hub, AWS Config and Checkov/Terraform evidence.
• GCP Compliance Automation — Security Command Center, Cloud Asset Inventory and Checkov/Terraform evidence.

Each adapter produces normalized control observations, SHA-256 integrity digests and review-gated CISO Assistant synchronization plans. CISO Assistant remains the GRC system of record; the adapters and control loop provide the technical collection, architecture decision and verification layers.

• Commerce: adjudicated incident precision, review effort, correction observation, and contribution economics.
• GPU/model serving: latency and throughput at fixed quality, concurrency, model revision, and fully allocated cost.
• Cloud/network: unsafe-change escapes, blast-radius prediction, rollback verification, and recovery time.
• Marketing: incrementality and uncertainty under a declared experimental design.
• Physical AI: missed hazards, decision timing, recorder completeness, and safe failure in a specified environment.
• Agent security/identity: prohibited-action escapes, false denials, policy scope, and replayable traces.
• Data/decisions: data freshness, correctness, baseline utility, and outcome observation.

See exact benchmark definitions and evidence requirements.

I treat governance as an engineering feedback loop derived from deployed systems—not a spreadsheet layer separated from operations:

Cloud, network, identity, application and SOC telemetry
                         ↓
           Normalized technical evidence
                         ↓
     Controls, risks, findings and audit workflows
                         ↓
   Terraform / OpenTofu / Bicep / Ansible proposal
                         ↓
         Human approval and controlled rollout
                         ↓
       Recollection and remediation verification

Relevant capabilities include:

• CISO Assistant integration and multi-cloud evidence collection;
• ISO 27001, ISO 42001, SOC 2, NIST, CIS, NIS2 and DORA mapping workflows;
• Microsoft Sentinel, Wazuh, OpenSearch and cloud-native SOC architectures;
• identity, segmentation, logging, detection engineering and incident evidence;
• audit readiness, evidence lifecycle, third-party risk and corrective-action tracking;
• agent authorization, MCP security and human-governed remediation.

Framework mappings and modeled outcomes are never presented as certification, legal advice or customer results without the corresponding review and evidence.

Every flagship separates four evidence classes:

• Implemented — executable code and automated tests exist.
• Deployed — retained evidence comes from an authorized cloud or infrastructure environment.
• Simulated — deterministic fixtures or synthetic telemetry exercise declared scenarios.
• Contract — an integration boundary is designed but has not called the real provider.

Modeled revenue, savings, latency, capacity and risk reduction are not presented as customer outcomes. SHA-256 receipts demonstrate integrity of serialized decisions; they do not provide non-repudiation without authenticated signing and evidence custody.

• Agentic DevOps & SRE Skill Registry — evaluated reusable skills for CloudOps, SRE, Kubernetes, networking and FinOps.
• AI Factory Revenue Twin — GPU, fabric, capacity and hybrid-cloud unit economics.
• Enterprise AI Integration Platform — durable orchestration across CRM, ERP, payments, logistics and billing boundaries.
• AI-Native Internal Developer Platform — Kubernetes, GitOps, golden paths and platform delivery economics.
• AIOps Observability Platform — OpenTelemetry, root-cause analysis and incident automation.
• Cloud Resilience & Disaster Recovery — RTO/RPO, ransomware recovery and multi-region scenarios.

Post-quantum, healthcare, biometric, robotics, and domain-specific systems retain their own scope and evidence limits in the full original-project directory.

Cloud, network, Kubernetes, GPU serving and data topology; failure modes, capacity, rollback, security boundaries, operating KPIs, and unit economics.

Authorization boundaries, agent-tool evaluation, SOC integration, control evidence, and reviewable remediation workflows.

Product and order-state diagnostics, causal measurement, customer-operation reliability, data freshness, and benchmark design tied to accepted business outcomes.

Recorder completeness, missed-hazard evaluation, simulation-to-lab evidence boundaries, and safety-oriented test protocols.

For a scoped technical review, contact me through A2Z SOC. A2Z SOC is a separate services site; the repositories and benchmark specifications above are the open-source work.