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moonrunnerkc/README.md

Brad Kinnard · AI Architect & Systems Engineer

I build offline LLM systems, multi-agent workflows, and cognitive layers that run on real hardware instead of living in slide decks. Most of my work sits between research and practical tools: local first assistants, RL tuned retrieval, belief style memory systems, and code tools that try to behave consistently under real usage.

As the founder of Aftermath Technologies, I take ideas from sketch to working prototype, then push on them until they are stable enough for other people to run without babysitting.


What I work on

  • Offline LLM and agent systems on consumer GPUs and Linux
  • Multi-agent orchestration for code analysis, refactoring, and style enforcement
  • RL driven retrieval and fusion across RAG, cache, and graph sources
  • Belief ecology, contradiction tracing, and anomaly detection for long running sessions
  • WordPress and web infrastructure that actually stays online and pays bills

What you will find here

Most repos fall into one of these buckets:

  • Cognitive layer experiments
    Belief ecologies, tension, memory decay, conflict tracking, and other ways of modeling what a system thinks it knows.

  • RL and orchestration
    Local first orchestrators, agent swarms, and RL tuned fusion layers that sit in front of LLMs instead of treating them like a magic box.

  • Code tools
    Multi-agent code style enforcement, refactoring helpers, and utilities that try to keep human codebases from turning into sludge.

  • Infra and web
    Pieces that support Linux based hosting, WordPress, and the glue that keeps small businesses online.

Most of this is experimental but runnable. If something is rough, I try to at least document what it is supposed to do and how to start it.


Stack in use right now

Core work

  • Python on Linux (Ubuntu)
  • Offline LLM orchestration, multi-agent systems, reinforcement learning pipelines
  • Retrieval and fusion across RAG, cache, and graphs, with evaluation harnesses for drift and failure modes
  • Cognitive style layers: belief ecology, tension, contradiction tracing, long horizon sessions

AI and ML

  • PyTorch
  • LLM tooling and evaluation
  • Prompt and agent design
  • Anomaly detection and basic safety checks
  • Offline RL for policies and fusion weights

Systems and infra

  • FastAPI and async backends
  • Redis, SQL and MySQL
  • Docker, Git, basic observability and benchmarking
  • Local first, privacy focused setups that do not depend on a cloud crutch

Web and WordPress

  • WordPress, WooCommerce, custom themes and plugins
  • PHP, JavaScript, HTML, CSS
  • Performance tuning, migrations, security hardening, DNS and SSL

What I am looking for

  • Work on serious AI systems that have to be stable, explainable, and measurable
  • Roles where Python on Linux, offline LLMs, RL, and multi-agent orchestration are core to the job
  • Chances to turn the cognitive layer and belief ecology work into tools other people can run, inspect, and extend

Contact

If you are working on real AI systems, cognitive layers, or infrastructure that needs to run without constant cloud support, feel free to reach out.

© 2025 Bradley R. Kinnard / Aftermath Technologies

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  1. adaptive-belief-ecology-system adaptive-belief-ecology-system Public

    ABES: Adaptive Belief Ecology System. It is a dynamic AI memory, not static, where beliefs live, decay, conflict, and mutate like organisms. 15 agents and RL run this self-updating cognitive ecosys…

    Python

  2. rlfusion-orchestrator rlfusion-orchestrator Public

    Local RL-driven AI orchestrator fusing RAG, CAG, and graphs - Built offline on consumer hardware.

    Python

  3. code-style-enforcer code-style-enforcer Public

    A cloud-ready, multi-agent code analysis system that runs style, naming, documentation, and security checks in parallel and adapts to your preferences using reinforcement learning. Designed for fas…

    Python

  4. gravitational-data-attractor gravitational-data-attractor Public

    Open-source AI system that uses artificial gravity fields to compress and fuse multimodal data with sub-ms inference. CPU-only, 5k param model. Demo of a novel architecture built for reasoning, not…

    Python 3