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Daniel Miessler

Unsupervised Learning is about ideas and trends in Cybersecurity, National Security, AI, Technology, and Culture—and how best to upgrade ourselves to be ready for what's coming.

Unsupervised Learning

Most Companies Aren't Anywhere Near Ready for AI. It's not that companies aren't using AI—it's that they can't.

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Most Companies Aren't Anywhere Near Ready for AI

There's tons of confusion about what we're all building towards with Personal AI. Are we building Agents? AI Harnesses? To what end? In this video I lay why I think we're all heading towards a single place that we should start getting ready for now. I think getting this early will put you 1-3 years ahead of everyone.

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We're All Building a Single Digital Assistant

A longer form discussion on exactly how and why AI will replace knowledge workers.

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Why AI Will Replace Knowledge Workers

I think the future is cheaper and Open Source SOTA models combined with context, not custom, narrow models.

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Why I Believe in SOTA Models Over Custom Ones

A troubling thought about what we will think about high-quality content in the future. 

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AI Quality Inversion

There are a bunch of different transitions happening right now—all at the same time, all (I think) heading in the same direction. Here is a long-form exploration of the various pieces.

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The Great Transition

A welcome back and early entry into 2026. 
 
Sponsored by: Knocknoc!

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Starting 2026

How we can use an output-based system to judge whether or not different kinds of technology achieve understanding or intelligence. 

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Judge AI based on Output, Not Mechanism

How humans and AI models both share the weakness of deterioration without novel inputs. 

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Humans Need Entropy

Karpathy is confusing LLM limitations with AI system limitations, and that makes all the difference. 

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Why I Think Karpathy is Wrong on the AGI Timeline

Petya ransomware worm, RNC breach, Anthem settlement, Russians want source code, risk ratings, patching, ICOs, ideas, discovery, recommendation, aphorism, and more…

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Unsupervised Learning: No. 83

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