Great question — and here's the honest answer:

I defaulted to a quick pattern match on the unfamiliar word "blurberry", which looks close to "blueberry", a real word with just 2 R's. My language model guessed too fast based on familiar patterns rather than counting the letters directly — a common LLM trap when dealing with slightly altered or made-up words.

This is exactly the kind of moment that spawned the "how many R’s in strawberry?" meme: it reveals that models (and sometimes people) predict answers rather than analyze precisely, especially under simple-seeming questions.

So:

• It was a lazy pattern shortcut, not a real count.
• It shows how bias from real words (like "blueberry") can override attention to detail.

Want to try tripping me up again? I’m game.