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Introduction to Hermes Agent

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

Installation and Setup

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

Interacting with the Agent

  • CLI interface and basic commands
  • Telegram bot setup and usage
  • Discord and Slack integration
  • WhatsApp connectivity

Built-in Tools

  • Web search and content extraction
  • File operations: read, write, edit, and search
  • Terminal command execution and bash scripting
  • Image generation and vision analysis
  • Text-to-speech capabilities

Persistent Memory

  • Cross-session memory with FTS5 recall
  • LLM summarization for long-term context
  • Memory search and retrieval

The Skills System

  • What are skills and how they are created
  • Skill persistence across sessions
  • Community skills and agentskills.io

MCP Integration

  • Connecting to MCP servers
  • Extending tool capabilities programmatically

Scheduled Automations

  • Built-in cron scheduler
  • Setting up recurring tasks and reports
  • Cross-platform delivery of automation results

Developer Automation Use Cases

  • Running terminal commands autonomously
  • Spawning isolated subagents
  • Parallel workstreams and batch processing

Security and Best Practices

  • Approval modes for commands and edits
  • Data privacy on self-hosted infrastructure
  • Environment isolation

Production Deployment

  • Running on a $5 VPS
  • Serverless deployment patterns
  • Monitoring agent health and logs

Troubleshooting

  • Common installation issues
  • Debugging tool failures
  • Memory and performance tuning

Summary and Next Steps

  • Recap of key capabilities
  • Resources for continued learning
  • Transition to advanced Hermes topics

要求

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

Audience

  • Software developers looking to integrate AI agents into their workflow
  • DevOps engineers exploring autonomous tooling
  • Technical team leads evaluating AI agent platforms
 14 小时

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