I’ve been building Hank — a personal AI assistant running on OpenClaw — that integrates with real-world tools and services. Instead of treating AI as just a chatbot, Hank can execute commands, manage infrastructure, and automate workflows across Google Workspace, GitHub, and more.
OpenClaw is the platform that makes this possible: it gives AI agents persistent memory, tool access, scheduled tasks, and a growing number of skills available through ClawHub — thousands of pre-built capabilities you can drop straight into your agent, or have the agent do it. Hank is my instance of that — my assistant, with my tools and skills, running in my infrastructure — available to act on my behalf: on a fixed schedule or proactively when human intent calls for it.
Like a soldier acting on a commander’s intent, Hank doesn’t wait to be told every step — it acts within the spirit of the standing order.
Talking to Hank
I interact with Hank through Telegram — the same app I use to message friends. No special interface, no OpenClaw app to install, no AI provider portal to log into.
That’s by design. OpenClaw supports WhatsApp, iMessage, Discord, Slack, Teams, and more. You pick the channel you already live in, and your agent meets you there. You can even specify which LLM to use for a given conversation — swap to a more powerful model for a complex task, or a faster one from a different provider for a quick question.
This matters more than it might sound. Most AI tools lock you into their interface — a browser tab, a mobile app, a proprietary chat window. With OpenClaw, you’re not switching contexts to use your assistant. You’re just... messaging it. The same way you’d message a colleague or friend a quick question.
It also means Hank is genuinely mobile. Standing in line, sitting on the couch, walking to the car — if I can send a message, I can put Hank to work.
What I Built
Hank, my OpenClaw-powered AI assistant, can:
Manage Google Workspace: Send emails, check calendar availability, create contacts, and search Drive — all via the gog CLI
Automate GitHub workflows: Clone repos, create branches, make code changes, and submit pull requests on my behalf
Update this website: Hank wrote the About page content of my personal website by reading my resume from Google Drive and creating a PR with professionally-written copy
Search and fetch web content: Research topics, fetch documentation, and summarize findings
This is just scratching the surface of what’s possible — but it gives you a feel for the range.
Why This Matters
Traditional automation requires writing scripts for every task. Even sophisticated tools like Zapier or N8N require you to anticipate every scenario upfront — and the moment your workflow doesn’t fit a predefined template, you’re back to writing code.
With an OpenClaw agent that understands natural language and has access to CLI tools as well as the memory history it has of working with you, the interaction model flips entirely. I can tell Hank “update my About page based on my resume and other details you know about me” and Hank handles:
Reading the resume from Google Drive
Reading your memory files — MEMORY.md, USER.md, daily notes, knowledge base — to pull in context the resume doesn’t capture
Analyzing and reconciling both sources
Cloning the personal website repo
Making appropriate edits to multiple files
Creating a PR with a detailed description
All in one conversational exchange — no template to configure, no workflow to diagram, no brittle automation to maintain.
The deeper implication is that the interface becomes the capability. If you can describe what you want, the agent figures out how to do it. That fundamentally changes what “automation” means.
Technical Architecture
Core Stack
OpenClaw (agentic AI platform), Telegram (chat channel), Claude Sonnet 4.6 (primary model), Claude Opus 4.6 (heavy coding tasks), ChatGPT (fallback).
Integrations
What I’ve connected so far: Google Workspace via gog CLI (Gmail, Calendar, Drive, Contacts); GitHub via gh CLI (repos, PRs, issues, actions); web search, content fetching, web page interactions (Brave Search API, web_fetch, Playwright CLI).
Infrastructure
Runs on Google Cloud Compute Engine (24/7 uptime). Previously ran in a Docker container on my local laptop — moved to GCP so Hank stays available even when my machine is off.
Why I’m Excited About This
It’s not the AI part that excites me. Talking to an LLM? That’s table stakes at this point.
What’s different with OpenClaw is everything around it.
The fact that it meets me in Telegram — the same app I already use — instead of asking me to open another tab or download another tool. The fact that I can swap models mid-conversation, pulling in GPT, Claude, or Grok depending on what I need. The fact that it builds a history with me — it knows how I work, what I’ve done, what I care about — and that context compounds over time.
And then there’s the autonomous piece. Not just answering questions when I ask, but acting on standing orders. Knowing when to do something without me having to trigger it.
But honestly, what might excite me most is the community. OpenClaw has a groundswell around it that is special — thousands building skills on ClawHub, sharing their setups and lessons learned, extending each other’s work. That kind of momentum is rare and it usually means something.
I’m still finding words for all of it. That’s kind of the point — I’m in the middle of it.
What’s Next
This is early-stage experimentation, but the direction is clear. I’m exploring:
More integrations — connecting Hank to more of the tools I use daily
Scheduled automation — having Hank proactively handle recurring tasks without me asking
Autonomous actions — giving Hank standing orders and letting it decide when to act on them, not just when I or a schedule triggers it
Memory and context — how agents retain and use knowledge about you over time
Multi-agent workflows — specialized agents working together on complex tasks
I’ll be sharing more on each of these as the work progresses.