https://arxiv.org/abs/2606.00206

In this paper they nerf an LLMs ability to emit waffling thinking tokens like "wait", "but", "alternatively", and the models (they're old, small models in the paper) terminate reasoning faster and perform better. I bet Anthropic is tuning this on their backend.

Seriously. Whenever I read the thinking output I get mad and turn down effort to medium or low.

Just output the code and we’ll work through it!

I feel similarly about having codex review claude’s plans. I don’t think I’ve ever seen it catch a major issue. It just points out things that would have inevitably been addressed during implementation anyway.

I usually have Claude build a plan first, then I put it into an XML file it updates with phases, usually we talk about some of those tasks, and then once its good and I like it, I have Claude implement the plan.

Another thing I tell Claude to do is to not guess, but look at documentation, it messes up a lot less, might use some tokens reading docs, but at least it has a higher success rate code wise.

I've been having success with Opus but you REALLY have to tame it. Long prompts that list what files to look at, relationships between entities, etc... I went from regularly hitting my daily limit to almost never hitting it. Oh, and also I was being lazy with small changes and stopping that helped a lot too. As you said, it gets in these loops where it's just churning and if you don't stop it it can go on for way too long.

Fable was 20 times worse on that.

It's clear it was the vibe coding model, as like no other model before, fully turned you into his assistant instead of the other way around.