Unsupervised Learning

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

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

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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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A welcome back and early entry into 2026. 

Sponsored by: Knocknoc!

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

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

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How going from exploration to exploitation can help you as both a consumer and creator of everything.

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Some thoughts on how novelty and attention magnify the time that we have. 

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➡ Stay Ahead of Cyber Threats with AI-Driven Vulnerability Management with Maze:
https://mazehq.com/

In this conversation, I speak with Harry about how AI is transforming vulnerability management and application security. We explore how modern approaches can move beyond endless reports and generic fixes, toward real context-aware workflows that actually empower developers and security teams.

We talk about:

The Real Problem in Vulnerability Management
Why remediation—not just prioritization—remains the toughest challenge, and how AI can help bridge the gap between vulnerabilities and the developers who need to fix them.

Context, Ownership, and Velocity
How linking vulnerabilities to the right applications and teams inside their daily tools (like GitHub) reduces friction, speeds up patching, and improves security without slowing developers down.

AI Agents and the Future of Security
Why we should think of AI agents as “extra eyes and hands,” and how they’re reshaping everything from threat detection to system design, phishing campaigns, and organizational defense models.

Attackers Move First
How attackers are already building unified world models of their targets using AI, and why defenders need to match (or exceed) this intelligence to stay ahead.

From Days to Minutes
Why the tolerance for vulnerability windows is shrinking fast, and how automation and AI are pushing us toward a future where hours—or even minutes—make the difference.

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Chapters:

00:00 – Welcome and Harry’s Background
01:07 – The Real Problem: Remediation vs. Prioritization
04:31 – Breaking Down Vulnerability Context and Threat Intel
05:46 – Connecting Vulnerabilities to Developers and Workflows
08:01 – Why Traditional Vulnerability Management Fails
10:29 – Startup Lessons and The State of AI Agents
13:26 – DARPA’s AI Cybersecurity Competition
14:29 – System Design: Deterministic Code vs. AI
16:05 – How the Product Works and Data Sources
18:01 – AI as “Extra Eyes and Hands” in Security
20:20 – Breaking Barriers: Rethinking Scale with AI
23:22 – Building World Models for Defense (and Attack)
25:22 – Attackers Move Faster: Why Context Matters
27:04 – Phishing at Scale with AI Agents
31:24 – Shrinking Windows of Vulnerability: From Days to Minutes
32:47 – What’s Next for Harry’s Work
34:13 – Closing Thoughts

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