With LLMs, you're still mostly read things "off the tip of the tongue". A better comparison is observing a smart person talking to themselves while working on a tough problem.
replyEDIT: also there's a reason the dial is called "effort", not "smarts".
I don't think smart people generally solve problems by talking through reasoning steps at a mile a minute. They clear their mind and let the solution come.
replyOf course I don't know if there's really a way for this to be molded in current LLM's (sounds more like diffusion)
> They clear their mind and let the solution come.
replyThey work on a problem until their brain is full of problem-related concepts. Then something comes. After validation it might be a solution.
Yes, but that should apply to the CoT "thinking", not the final output.
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replyIt's so bad I've made myself a Pi extension that rewrites responses in side by side view using models on Cerebras (insanely fast tps)
replyI just go over the comments with Gemini 3.1 Pro at the end which has a much more normal "voice" and it doesn't lose nuance as a cheap model would. I don't care so much about what Claude writes during the debugging as I just do all the cleanup at the end instead of at every commit.
replyThe higher the effort the more things Claude checks, and it's eager to tell you about all of them
replySee, this insight it had early on looked like a red hering for a while, but then turned out to be load-bearing. And that's not just a difference in semantics, it changed the whole conclusion (spoiler: it didn't). And Claude is very eager to tell you about this exciting journey
“I have made this letter longer only because I didn’t have the time to make it shorter.”
- Blaise Pascal
replyOpenAI has separate dials for verbosity and reasoning_effort (but could still do a better job).
replyI hate this too, I had to switch to Codex, because the skill to force Claude Code not to think too much about very, very basic things no longer worked
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