Getting Started with OpenClaw: Build Trust Before You Build Agents
If I were building OpenClaw again today, I would not start with faster hardware or a newer model. I would start with one real problem, ChatGPT Plus, Docker, Git, and a system that earns trust slowly.
Click for more / Podcast Player>Phil Hawthorne on Smarter Home Assistant Automations, ESPHome, E-Ink and AI – HGG686
OpenClaw Had the Data. It Still Misread the System.
OpenClaw had accurate data, but its Solar Manager still raised the wrong status. Here is what that defect, a workspace reconstruction, and runtime model verification taught me about operational intelligence.
Click for more / Podcast Player>T.J. Huddleston on Smart Gardens, Solar-Powered Sheds and Home Assistant AI – HGG685
The Model Is Part of the Permission Boundary: Routing AI Work by Risk and Trust
I originally treated AI model routing as a cost and capacity decision: use a smaller local model for routine work and reserve GPT for harder tasks. A scheduled security audit showed me why that was incomplete. Even read-only models can make consequential mistakes when their judgment influences what happens next. In this eighth OpenClaw post, I explain how permission, model trust, deterministic checks and guarded execution now work together.
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