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Inside DeepSeek: Reverse Engineering an AI Assistant by Interviewing Itself · manish.sh

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I run manish.sh . I write about AI tools and how LLMs behave when you push them. This is part of the Inside LLMs series — interview a chat model about how it thinks, then check the careful bits against published research. The Kimi K2.6 entry came first; this is the DeepSeek follow-up. My first question sounded simple: What do you actually know about yourself? I expected marketing. Instead, DeepSeek sorted its answer into observation, inference , and guess. That was the moment I realized this interview might actually be interesting. Screenshot from the DeepSeek interview chat used in this post. It drew pipelines like an engineer at a whiteboard. Then it called itself an unreliable witness. Sounded honest. After the chat, I opened the public papers anyway — and that is where the story splits. No weight dumps. No prompt leaks. One long interview, checked against arXiv. Where DeepSeek said “I am guessing,” I keep that label. Scroll to interactive labs at the end. Quick note: One chat, exported 21 July 2026.…

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