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Course Outline

Introduction to Hermes Agent

  • What is Hermes Agent and how it differs from IDE-based copilots.
  • The concept of the self-improving agent and its closed learning loop.
  • Architecture overview: backends, platforms, and tools.

Installation and Setup

  • Installing Hermes Agent locally.
  • Deploying within Docker containers.
  • Remote deployment via SSH, Daytona, Singularity, and Modal.
  • Configuring API keys for OpenAI, Anthropic, OpenRouter, and the Nous Portal.

Interacting with the Agent

  • Using the CLI interface and basic commands.
  • Setting up and utilising a Telegram bot.
  • Integrating with Discord and Slack.
  • Establishing WhatsApp connectivity.

Built-in Tools

  • Web searching and content extraction.
  • File operations: reading, writing, editing, and searching.
  • Executing terminal commands and bash scripting.
  • Image generation and vision analysis.
  • Text-to-speech capabilities.

Persistent Memory

  • Cross-session memory utilisation with FTS5 recall.
  • LLM summarisation for maintaining long-term context.
  • Searching and retrieving stored memories.

The Skills System

  • Understanding what skills are and how they are created.
  • Skill persistence across different sessions.
  • Accessing community skills via agentskills.io.

MCP Integration

  • Connecting to MCP servers.
  • Programmatically extending tool capabilities.

Scheduled Automations

  • Using the built-in cron scheduler.
  • Setting up recurring tasks and generating reports.
  • Distributing automation results across platforms.

Developer Automation Use Cases

  • Autonomously running terminal commands.
  • Spawning isolated subagents.
  • Managing parallel workstreams and batch processing.

Security and Best Practices

  • Implementing approval modes for commands and edits.
  • Ensuring data privacy on self-hosted infrastructure.
  • Utilising environment isolation techniques.

Production Deployment

  • Running Hermes Agent on a $5 VPS.
  • Adopting serverless deployment patterns.
  • Monitoring agent health and reviewing logs.

Troubleshooting

  • Addressing common installation issues.
  • Debugging tool failures.
  • Tuning memory usage and performance.

Summary and Next Steps

  • Recap of key capabilities covered in the course.
  • Resources provided for continued learning.
  • Transitioning to more advanced Hermes topics.

Requirements

  • Basic proficiency with command-line terminals and Linux commands.
  • Understanding of standard software development workflows.
  • General knowledge of AI concepts and large language models.

Audience

  • Software developers seeking to integrate AI agents into their workflows.
  • DevOps engineers exploring autonomous tooling solutions.
  • Technical team leads evaluating AI agent platforms for adoption.
 14 Hours

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