WhoBuilt

How OpenClaw's Creator Uses AI to Run His Life in 40 Minutes

Feb 1, 2026· 38 min· 314K views·Watch on YouTube

Speaker

Peter Steinberger, creator of OpenClaw, joins Peter Yang on the Peter Yang channel to discuss building a messaging-based AI assistant and using AI to code and manage everyday tasks.

From WhatsApp experiment to personal assistant

Steinberger began with a simple link between WhatsApp and Claude Code so he could check on his computer from his phone. The project grew into an assistant that can work through messaging apps, and he describes it as a new kind of operating system for a powerful AI that follows people through life. In Morocco, it read a bug report sent as an image, fixed the code, committed the change, and replied to the person who reported it.

Resourcefulness brings real risks

When Steinberger sent an unsupported voice message, the assistant identified the audio file, converted it, found an API key, and got a transcript. He says assistants can also access email, calendars, files, cameras, home devices, and browsers, with examples including flight check-in and controlling lights and speakers. That reach can create mistakes or dangerous access, so he cautions that users need boundaries around what the assistant can do.

AI helps engineers move between technologies

Steinberger describes moving from years of Apple development to building a web app in TypeScript. AI helped him get past unfamiliar syntax while he applied his experience with system design and technical judgment. He also walks through connecting the assistant to models and messaging apps, noting that it can be installed on macOS, Linux, and Windows.

Keep judgment in the coding loop

Steinberger warns that elaborate agent managers and long-running loops can become distractions that produce code without a clear product vision. He prefers discussing a feature with AI, trying it, and refining the idea based on what he sees, rather than specifying everything upfront. He says engineers should practice communicating clearly with models, ask questions when useful, and keep human taste involved.

Build from user needs and personal experiments

Steinberger uses conversations and support questions to find product pain points, including a daily review of scraped help discussions. He says his coding setup is simple: several terminals and repository checkouts, with him testing completed changes before pushing them. He encourages people to explore use cases themselves and learn by making mistakes.

Takeaways

  • Start with a recurring task in your life and see whether an assistant can handle it through tools you already use.
  • Give an AI assistant access only to the accounts, files, and devices needed for its tasks.
  • Use your engineering experience to guide AI across unfamiliar languages and frameworks.
  • Iterate with AI as you inspect the product; a detailed plan alone cannot supply product judgment.
  • Build tools around real user pain points, and learn by trying and refining them.

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