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fr3kchy/README.md

Michael Insch — fr3k

Independent AI systems architect · programmer · operator
North Queensland, Australia

I build autonomous systems where models, tools, infrastructure, hardware, safety and operations have to agree.

My work spans local-first agent infrastructure, resilient communications, distributed control planes, model evaluation and optimisation, RAG, multimodal generation, embodied AI and evidence-gated field systems. I do not count scaffolds as finished systems and I do not report simulated passes as execution evidence.

Current engineering surface

  • Sovereign agent infrastructure: Hermes Agent, OpenClaw and Paperclip systems running as independently supervised services.
  • Resilient communications: Reticulum/LXMF transport with a fail-closed optional Hermes policy gateway.
  • Distributed agent operations: authenticated fleet enrolment, durable task dispatch, replayable events and bounded workers.
  • FR3K infrastructure: community-owned communications research for North Queensland, controlled by explicit regulatory, safety and evidence gates.
  • Local model engineering: 88-model Ollama inventory, quantisation, context/VRAM sweeps and task-specific evaluation across an RTX 3060 and constrained GPUs.
  • Embodied and multimodal AI: Reachy Mini physical I/O, speech/vision/action routing, ComfyUI, FLUX, Wan and local video pipelines.
  • Agent evaluation: golden datasets, drift detection, trajectory replay, cost-aware routing and local/cloud comparative harnesses.

Flagship systems

Repository What it demonstrates Executed evidence
hermes-reticulum-platform Independent Reticulum/LXMF transport plus a bounded Hermes policy gateway 38 tests; signed virtual delivery; gateway E2E; restart persistence; transport survives gateway failure
hermes-fleet-control Secure local-first control plane for distributed Hermes nodes 16 tests; HMAC heartbeats; durable task queue; authenticated events and metrics
fr3k-infrastructure Executable governance and evidence gates for community communications infrastructure Pack checksum verification; automated G0 decision gate; explicit blocked-state handling
qwen36-bench Reproducible Ollama optimisation on constrained hardware 35B MoE model on 4 GB VRAM; 5.71 tok/s peak; all short-suite checks passed
trajectory-replay-demo Record and replay agent tool trajectories for model/prompt A/B testing SQLite-backed replay and comparative evaluation
agent-cost-router-demo Route tasks to the cheapest viable model instead of defaulting to frontier inference Measured cost-routing baseline
mcp-rag-pgvector-demo Production-shaped RAG with pgvector, embeddings, MMR and evaluation Reproducible pipeline and evaluation harness
ollama-local-rag-demo Fully local RAG with Ollama and FAISS Zero-cloud retrieval and generation path
agent-eval-harness-demo Golden-dataset agent evaluation and drift detection 25-question baseline with local and hosted backends
divine-arsenal Domain-specific computation joining ephemeris data, planetary hours and operational rules Deterministic Python implementation rather than symbolic prose
openclaw-workshop Two-day hands-on curriculum for agents, MCP and AI coding tools Structured labs and deployment material

How I engineer

  1. Operational truth outranks mythology. Read the running system, not just its design document.
  2. Transport and control are separate failure domains. Optional AI must not weaken the underlying system.
  3. Evidence is graded. Observation, unit tests, integration tests and live acceptance are not interchangeable.
  4. Security boundaries are explicit. Inputs are untrusted, queues are bounded, privileged audit fails closed and cloud egress is gated.
  5. Hardware claims require hardware. Virtual/LAN acceptance is never relabelled as RF, robotics or field acceptance.
  6. Useful failure reports beat polished fiction. A blocked gate is a valid engineering result.

What I am building now

  • governed FR3K bench and field architecture for Magnetic Island and North Queensland
  • Reticulum/LXMF identity, routing and offline messaging systems
  • a distributed Hermes fleet with durable orchestration and independent node operation
  • local multimodal and embodied-agent pipelines with measurable latency, memory and reliability constraints
  • benchmark-gated self-improvement loops for agents, prompts, skills and local models
  • private trading and operational systems where risk controls remain separate from model output

Reviewer path

If you want the fastest high-signal inspection:

  1. Read the engineering dossier.
  2. Inspect the acceptance matrix in hermes-reticulum-platform.
  3. Inspect the security boundaries and tests in hermes-fleet-control.
  4. Inspect the blocked-state discipline in fr3k-infrastructure.
  5. Reproduce the measured sweep in qwen36-bench.

Contact: fr3k@mcpintelligence.com.au

Pinned Loading

  1. fr3kchy fr3kchy Public

    Michael Insch (fr3k) — AI systems architecture, resilient infrastructure and evidence-driven engineering

  2. hermes-fleet-control hermes-fleet-control Public

    Secure local-first control plane for distributed Hermes Agent nodes

    Python

  3. hermes-fr3k-mobile hermes-fr3k-mobile Public

    fr3k mobile agent config - Android Termux

    Python

  4. hermes-reticulum-platform hermes-reticulum-platform Public

    Fail-closed Hermes policy gateway over independent Reticulum/LXMF transport

    Python

  5. qwen36-bench qwen36-bench Public

    Reproducible Qwen3.6 35B-A3B Ollama optimisation on a 4 GB GPU

    Python

  6. vibe-coding-site vibe-coding-site Public

    Experimental AI-assisted web development and interface design

    HTML