robryan

You get no better result from Opus 5.5 than Opus 4.1 (frontier this time last year)?

Probably because it is in flux, there is a large scale reorganisation of software around agents going on.

Any basic LLM told to evaluate the traffic against the actual goal would have been able to flag this.

GLM 5.3 flash seems to get more excited the longer it has been trying to hunt down a problem. Complete with caps, many exclamation marks and emoji.

It is funny sometimes because the actual issue it traced down was mostly inconsequential.

> Algorithmic mitigation is being explored, but scientists describe it as “embryonic” — potentially demanding computing power that rivals the science processing itself.

It sounds like this would work? Just be more costly, at least in the short term.

> Engineering fixes are the real answer

What is the fix and tradeoff? The article doesn't seem to cover it at all.

You can still do all that. The only thing going is that in some cases the human isn't going to be able to claim that they made the key insights that first solved the problem.

A lot of this would be public funding. If AI is reducing the need for mathematicians in an area it makes sense to fund it less and fund something else that isn't solved.

Surely it is. Ask it to keep a list of all the promising sub paths, reprompt the collections of agents again on these after the main problem has been addressed. Or even release a list of them and let others investigate.

I feel like most of the latest big Chinese lab models are good enough now. If I had to pick a difference it feels like sol goes further towards 1 shotting things and needs less babysitting. But if I am actively prompting and reading the results can get just as far on a lesser model.

They really can't in the open model space. Look at any current open model you would want to use on open router, there will be 20 or more options.

I think that is fine, certain things are becoming less important to learn and certain things more important. With the pace of change it is likely to continue shifting.

Nothing stopping kids from asking AI to give you responses like this, it is more than capable. There is always going to be the temptation to take short cuts though.

Can just pass it a context, cancel the context, and handle for it in the goroutine.

Some of the pre-ai decline in questions might just be that they had filled out of alot of the question space. What might be more interesting is the traffic graph as it would be possible to have a decline in questions but still have traffic rising to the existing ones.

Today I'd expect even when someone is googling and a question is relevant that most people will just read the AI overview.

I think it was different pre-AI. Someone might come in and spend days getting some understanding of the codebase before they contribute some minor fix. Over time they might stick around a make some more of these, progressively gaining trust so when they do take on something bigger the maintainers will know they aren't wasting their time reviewing it.

Now they can drop a multi thousand line poorly understood PR day 1.