Personal Agents with OpenClaw
Released 10/2026
By Steve Kinney
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 6 Lessons ( 4h 44m ) | Size: 1.6 GB
Build a Local-First AI Assistant Without Giving It the Keys to Everything
Install and configure OpenClaw, connect it to a real chat channel, and learn how to scope trust, tools, and access so your personal AI assistant is genuinely useful without becoming a liability.
What You Will Master
Understand that a personal AI agent is delegated authority, and learn how to deliberately scope both its power and its risk.
Learn why local-first is not secure by default and how to actually lock down an assistant running on your own hardware.
Build a clear mental model of OpenClaw’s personal-assistant trust model so you do not mistake it for multi-tenant isolation.
Apply least privilege across three axes: who can talk to the bot, where it can act, and what it can touch.
Read and interpret openclaw doctor, security audit, and security audit –deep output with confidence instead of treating green checkmarks as magic.
Know when to reach for a tool, a skill, or a plugin as you expand your assistant’s capabilities.
Leave with a reusable security checklist, a custom skill, and a working capstone automation you can keep evolving after the workshop.
Prerequisites
Basic comfort with the terminal, including running commands, editing config files, and reading command output.
Some exposure to AI coding tools such as Copilot, Cursor, Claude Code, ChatGPT, or similar tools.
No prior experience with agent architecture, OpenClaw, self-hosted assistants, or secure automation is required.
Come with Node.js LTS, npm, Git, and a code editor installed and working on your machine.
OpenClaw should be pre-installed if possible.
Bring an API key for at least one model provider with a low spending limit.
Telegram and Tailscale are helpful but optional; simulated alternatives will be available.
Who is this for?
This workshop is for developers and tinkerers who are curious about personal AI agents and want something they actually control. It is especially well suited for engineers drawn to local-first tools and for anyone considering giving an agent access to messages, files, a browser, or a shell and wanting to do that responsibly. It is not aimed at people looking only for prompt-engineering tips, teams shopping for managed SaaS agents, or anyone who needs airtight multi-tenant isolation without understanding the underlying trust model.
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