Getting Started with Locally‑Hosted AI Agents

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Many people within the Huntsville area, especially if you work within the DoD, are now being restricted on their access to commercially available LLM's like ChatGPT, Claude and others. But there’s a growing ecosystem of small, open‑source models that can be run entirely on‑premise, and when combined into a lightweight agent architecture, they can deliver surprisingly powerful productivity gains.

To help you get the ball rolling, I’m hosting a 30‑minute introductory talk on “Getting Started with Locally‑Hosted AI Agents.” The goal is to give you a clear, practical path from “I want a local AI assistant” to “I’ve got a multi‑agent system that automates my code review, documentation, and CI/CD tasks.”

What You’ll Learn

  • Why local agents matter (privacy, latency, cost control)
  • A quick “starter kit” of hardware, OS, and software stacks (Python, Docker, GPU/CPU options)
  • The top 3‑4 open‑source LLMs that are ready for the dev world today
  • How to stitch multiple small models together with a lightweight orchestration layer (LangChain, AgentSmith, or custom pipelines)
  • Real‑world use‑cases: auto‑generation of README files, automated code‑review bots, data‑labeling helpers, and more
  • A live demo (if time permits) of a basic “developer assistant” that parses your repo, runs tests, and suggests fixes

Who Should Attend

  • Developers looking to experiment with local LLMs
  • Technical leads wanting to prototype AI‑powered dev tools
  • Researchers interested in agent‑based systems and open‑source ML
  • Anyone curious about the future of privacy‑first AI in engineering

  Date and Time

  Location

  Hosts

  Registration


  • Add_To_Calendar_icon Add Event to Calendar
  • 8800 Redstone Gateway Boulevard
  • Huntsville, Alabama
  • United States 35808
  • Building: Suite 100
  • Room Number: 111
  • Click here for Map
  • Contact Event Host
  • Starts 18 October 2025 05:00 AM UTC
  • Ends 26 October 2025 05:00 AM UTC
  • No Admission Charge




Agenda

1. Introduction
Why local AI? The problem space and the promise of agents.

2. Hardware & Software Foundations
What you need: CPU vs GPU, RAM, storage, OS, Docker, conda.

3. Model Landscape
LLaMA, Mistral, GPT‑NeoX, StableLM – size, performance, licensing.

4. Agent Orchestration Basics
Memory, prompts, tool calls, communication between agents.

5. Developer Use‑Cases
Code completion, linting, docstring generation, unit‑test synthesis, CI/CD automation.

6. Demo / Hands‑on
Live walkthrough of a mini‑agent that analyses a GitHub repo.

7. Resources & Next Steps
GitHub repos, communities, tutorials, next‑level reading.

8. Q&A