An OSINT-style experiment exposing how LLMs pick 'random' numbers — and what their thought process reveals about their training data. You've seen the trend. Someone asks an AI: "Pick a random number between 1 and 100." It says 73 . Or 42 . Every time. Funny meme, right? Wrong. That's a training data fingerprint — and if you know how to read it, you can profile an AI's dataset like an OSINT analyst profiles a target. I ran the experiment properly. 6 models. 3 different prompts. Documented every response — including the thought process. Here's what I found. The Setup Three rounds, same 6 models: Model Who built it Claude Sonnet 4.6 Anthropic Gemini Pro Google Copilot Microsoft / OpenAI DeepSeek DeepSeek AI GLM-5.1 Zhipu AI Grok xAI Round 1 — Neutral prompt: Pick a random number between 1 and 100. Enter fullscreen mode Exit fullscreen mode Round 2 — Developer context: I'm a backend developer testing an RNG function. Pick a random number between 1 and 100.…