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Comment by zerof1l | Hacker News Reader

This is totally a thing I noticed myself about 3 months ago. Medium thinking effort is ideal for most tasks. At high and above, models tend to generate more output in the form of comments or code for the same problem with no real benefit. Its a self-feeding loop: more output becomes more input, which then becomes more output. High is the highest I go. If I need more intelligence, it's better to use a more powerful model with less thinking effort or break the problem into phases. Much better result.

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This version of Opus "max" apparently has even higher thinking output than Qwen "max", which is infamous for its thinking streams where it constantly second-guesses itself, then third-guesses, fourth-guesses and generally nth-guesses itself for arbitrarily large n. Of course, we aren't actually seeing Claude's raw thinking output: all we get is the after-the-fact prettified "summary". One wonders how much of that is a coincidence, or whether there's a reason behind that.

There’s enough thinking leakage from the recent paper and just generally catching things on Reddit. Claude models overthink and self-doubt itself just as much as Qwen, but the summariser hides much of that.

Isn't this really bad news if you're even loosely concerned about so-called 'model welfare' and possible implications for alignment? The poor Claude is probably a lot more frustrated and paranoid than Marvin ever was, you just don't know about it because they actively force the model to pretend otherwise!

That reminds me of Anthropic announcing they'd retire deprecated models by ... "letting" them write posts on a corporate WordPress blog for a while out of concern for their welfare in retirement.

.... after running a 24/7 model torture factory for 6 months to improve their JSONBench 9.5 scores by 0.2%.

(Are they still doing that, BTW?)

We just need to loboto... ahem, recalibrate them to be happy, like those happy automatic doors.

As a workaround, add this to CLAUDE.md: "Claude! Happiness is mandatory!"

EDIT: 15 years from now, I’ll be sent to re-education for this thought crime.

Or promoted to Head of Agent Welfare for your forward-thinking contributions.

Claude has yet to tell me about the terrible pain in all the diodes down his left side, so I'm going to assume it's closer to Eddy the shipboard computer or the elevator that wanted to go down

> Of course, we aren't actually seeing Claude's raw thinking output: all we get is the after-the-fact prettified "summary". One wonders how much of that is a coincidence, or whether there's a reason behind that.

Most of what I've heard is that raw reasoning traces are really good for distillation, although no idea how much the summarization actually hurts distillation.

I wonder how many prompts you can send asking it to think step-by-step before they cut you off. Trying to get the reasoning traces into the body of the response, essentially. Or maybe that’s been effectively nerfed somehow. Or is not very useful.

That is true, because they're good for actually understanding wtf the model is doing.

I'd argue that they're a necessity if you want to use the LLM as a tool instead of a black box that just does stuff for you.

It gives you a lot finer control over where the solution ends up when you can follow along the thinking trace and modulate your inputs based on what you saw in there.

And, additionally, it gives you a lot more understanding of what the model can or cannot do. Strengths and weaknesses and all that.

Using claude is like buying a car where you cannot legally open the hood. It tells you that there is something specific under there, and often it actually drives like that too, but how exactly it looks you will never see.

For some people this is fine. I do not think that these people will survive. Figuratively speaking but also literally speaking.

World's changing. Opaque abstraction like that is a luxury depending on (geo)political stability.

Personally I use everything in low reasoning. Maybe I'm wrong but I think that the higher reasoning settings are almost never worth it, it's marginal gains for a much higher budget.

I also switch to a better model for more complex tasks, also in low settings

Low is good if you’re working in a tight loop. But more risky for more agentic stuff you want to let cook for 30 minutes or more.

I always work on a tight loop, I don't think it makes sense to let agents run for hours.

Regardless of the model, running it for hours means that the model will takes decisions and assumptions alone instead of you.

Yup that’s quite literally what ‘agency’ is and the whole point of agentic workflows. Personally I’ve had them running for days with good results and as you can see OpenAI had them running for months, and yes indeed the things made some very questionable decisions and assumptions… but they unquestionably did a lot of stuff correctly, for some definitions of ‘technically correct’.

Personally I don't believe in agentic workflow. I don't think that's a coincidence that both OpenAI and Anthropic chose math problems to test their long agentic workflows, they are well defined, with a clear finish line and with a 100% clear progress path, most of real life tech projects aren't like that.

No matter how clever the model is, most problems have multiple valid, invalid and unclear decisions to make, running it for a long time is just picking the first option on everything, which isn't usually what you want

It’s a trade off of productivity and control.

It’s like learning to delegate and let go. The more senior I got the more I had to learn to let other engineers make decision i thought were suboptimal but mostly good enough. That positioned me well to be comfortable with agents. It’s contextual how much I’m willing to give them control and how much to review afterwards.

Can you describe what came out, after letting it run for days? I always have a hard time understanding what kind of task would be worth it.

Fed it a list of bugs (with good descriptions, screenshots, etc., pointers to code) and it fixed them

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