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Course Outline
Introduction to Hermes Agent
- Understanding Hermes Agent and its differentiation from IDE copilots.
- The concept of self-improving agents and closed learning loops.
- Overview of architecture: backends, platforms, and tools.
Installation and Setup
- Installing Hermes Agent locally.
- Deploying via Docker containers.
- Remote deployment using SSH, Daytona, Singularity, and Modal.
- Configuring API keys for OpenAI, Anthropic, OpenRouter, and Nous Portal.
Interacting with the Agent
- CLI interface and fundamental commands.
- Setting up and using the Telegram bot.
- Integrating with Discord and Slack.
- Establishing WhatsApp connectivity.
Built-in Tools
- Web search 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 using FTS5 recall.
- LLM summarization for maintaining long-term context.
- Searching and retrieving stored memory.
The Skills System
- Understanding skills and their creation process.
- Ensuring skill persistence across sessions.
- Accessing community skills and agentskills.io.
MCP Integration
- Connecting to MCP servers.
- Programmatically extending tool capabilities.
Scheduled Automations
- Utilizing the built-in cron scheduler.
- Setting up recurring tasks and generating reports.
- Distributing automation results across platforms.
Developer Automation Use Cases
- Autonomous execution of 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.
- Maintaining environment isolation.
Production Deployment
- Running the agent on a $5 VPS.
- Implementing serverless deployment patterns.
- Monitoring agent health and reviewing logs.
Troubleshooting
- Addressing common installation issues.
- Debugging tool failures.
- Tuning memory and performance.
Summary and Next Steps
- Recap of key capabilities.
- Resources for continued learning.
- Transitioning to advanced Hermes topics.
Requirements
- Basic proficiency with command-line terminals and Linux commands.
- Familiarity with software development workflows.
- General understanding of AI and large language models.
Audience
- Software developers seeking to integrate AI agents into their daily workflows.
- DevOps engineers exploring autonomous tooling solutions.
- Technical team leads assessing AI agent platforms for organizational adoption.
14 Hours