Local truths, local lies

I’ve been running local AI models for weeks now. No servers whispering back across oceans. No hidden requests vanishing into corporate black boxes. Just code, parameters, and my own hardware.

What strikes me is this: the mistakes feel different.

The online systems I tested last year spoke with certainty—even when they were wrong. They dressed lies in confidence, wrapped noise in smooth language.

The local ones don’t. They hesitate. They stumble. When they don’t know, they admit it—or fail with a clumsy silence.

Is that real, or am I imagining it? Is it the nature of local models, or just the comfort of knowing the wires don’t lead somewhere I can’t see?

Maybe it’s not truth I’m seeing. Maybe it’s just fewer lies.