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grit above all else
:octocat:
grit above all else
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mso-docs/README.md

👋 Hello World, I'm Mackenzie O'Brien!

AI systems engineer focused on building, evaluating, and integrating intelligent tools into real developer workflows.

I design and prototype AI-powered systems including RAG pipelines, MCP-based agents, model evaluation workflows, and API-driven automation. My background in documentation and developer experience gives me a strong edge in building systems that are not just functional—but usable, maintainable, and scalable.

🚀 Core Focus Areas

  • 🧠 Model evaluation & applied ML (HF evals, LoRA, experimentation pipelines)
  • 🛠️ AI tooling (MCP, agents, structured prompting, tool use)
  • ⚙️ Backend automation (API orchestration, JWT flows, data pipelines)
  • 📦 Developer experience & docs-as-code systems

🧩 What I Do

  • Build and evaluate AI systems (RAG, agents, small models)
  • Design developer-facing AI tools and workflows (MCP, API integrations)
  • Implement backend automation pipelines (JWT flows, data pipelines, CLI tools)
  • Apply docs-as-code principles to improve AI system usability and DX

🚧 Flagship Projects

🔹 AI Research & Tracking Agent (Local-First)

  • Ingests RSS feeds, saved URLs, and local notes
  • Generates structured summaries with embeddings
  • Stores memory for semantic retrieval and trend tracking
  • Supports queries like: “What changed in my tracked topics this week?”

🔹 API Automation Pipeline (JWT → Bearer → API)

  • Fully automated authentication + request flow
  • Designed for integration with tools like Bruno
  • Reduces manual API testing overhead

🔹 Tiny Transformer Experiments

  • Implementing transformer architectures from scratch in PyTorch
  • Exploring tokenization, attention mechanisms, and training loops

🟣 AI Tooling & MCP Systems

  • Designing MCP-based tools for document navigation and code-aware assistants
  • Building structured prompt systems for tool-using agents
  • Developing end-to-end API workflows (JWT → bearer → service integration)

🧬 AI & ML Engineering

  • Running and analyzing model evaluations (HF evals and alternatives)
  • Training and experimenting with small models & LoRAs
  • Studying transformer internals (attention, RoPE, tokenization)
  • Building experimental training loops and evaluation pipelines

💻 Software Development

  • Automating end-to-end API workflows in Python
  • Building CLI tools and internal utilities
  • Creating backend services to support AI experimentation

🛠️ Tech Stack

🧠 AI / LLM Systems

Python PyTorch Hugging Face Transformers LoRA Model Evaluation RAG Embeddings Agents MCP Prompt Engineering OpenAI Amazon Bedrock Amazon SageMaker Ollama OpenClaw

⚙️ Backend, Data & Automation

FastAPI Node.js JavaScript Pandas Matplotlib API Design JWT OAuth CLI Tools Automation SQLite

📦 Infrastructure, Cloud & Data Pipelines

Docker Linux WSL AWS PySpark Apache Spark Data Pipelines Local LLMs

🧪 Developer Workflow & Tooling

Git GitHub GitHub Actions VS Code Postman Bruno

📚 Docs-as-Code & DX

OpenAPI Swagger MkDocs YAML Docs-as-Code

📊 GitHub Stats

Image Image Image

🌐 Connect With Me

LinkedIn Portfolio Hugging Face

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  1. Docs-Navigator-MCP-SUSE-Edition Docs-Navigator-MCP-SUSE-Edition Public

    AI-Assisted Documentation Explorer Powered by Model Context Protocol!

    JavaScript 1

  2. Docs-Navigator Docs-Navigator Public

    An MCP server that navigates through documentation via an assistant!

    Python

  3. ml-sensei-lora ml-sensei-lora Public

    A tiny LoRA that helps you level up your AI/ML skills!

    Python

  4. Ollama-Web-Summarizer Ollama-Web-Summarizer Public

    A Chrome extension that uses local Ollama to summarize web content and chat about it.

    JavaScript

  5. Luckie-Runner Luckie-Runner Public

    In Luckie Runner, you play as Luckie Puppie, a barista dog who is running late to her shift at Beachside Boba Cafe! Can she make it in time? Or will RNG screw her over? Find out by playing the game!

    JavaScript