I’ve started narrating my own thought process out loud to myself. I look like a bit of a loon, but I tell myself I’m just a streamer with an audience of one. And to my surprise, it’s working really well. Not only does it make the game more lively and energetic, but also I find I’m more effective in solving puzzles.

I was initially baffled by this. Common sense says that I can’t articulate my thoughts at remotely the same speed or accuracy as thinking them. Particularly for a puzzle game where the majority of reasoning is geometric, difficult to describe in speech. We know LLMs benefit from this sort of reasoning, but that is due to an architectural limitation – they literally cannot iterate on previous thoughts; they must output tokens to get any true recursion.

But then I remembered rubber duck debugging. It’s often useful in software engineering to explain your problem to someone else. But who you explain to isn’t really an important part of the process – it could be a rubber duck. The value is in forcing you to evaluate all your unstated assumptions, linearly sort your thoughts by priority and dependency, and find good abstractions.

These are also useful skills for real-time problem solving. I think there are some further benefits for this particular use case:

• It forces you to establish named formal rules and concepts, instead of relying purely on building intuition. In other words, engaging System II, which the puzzles are explicitly designed to require.
• It forces you to acknowledge when you are making an arbitrary choice. Useful for backtracking or exhaustively searching a possibility space.
• I think it lets you use echoic memory to slightly augment your working memory.
• I may well have accidentally triggered the Hawthorne effect

It turns out there’s good evidence that this technique has a moderate benefit, for example this meta-study (n=6000 participants).

— Adam Newgas, Chain of Thought For Humans, Boris The Brave, 2026