aszen

Because subscription plans are cheaper, only enterprises paying per tok pricing should be freaking out

About jev being a black box and the potential for bias, I think it boils down to what questions you are asking the model.

Broad questions like Is this resume good / score this city will ofcourse be biased but I think jev encourages more granular focused questions like Score this candidates Python experience / Rate this city for its food which then allows you to introduce your own biases in which questions you ask and how you combine their answers.

In this way I think jev like models can be easier to reason about for critical decisions.

Their zdr is not a concrete promise though, there's no way to verify that the provider is not storing the logs

I was thinking the same, obvious suspicion is they benchmaxed it on older bench.

Because stronger models are harder to jailbreak from the paper it says haiku was easily fooled into giving us thinking contents

Won't surprise me if the llm just calls sleep after it's convinced it knows all

And does it actually use it

Is your plugin just skills or does it have custom agents?

My own experience is that Claude will use your skills but will ignore your agents or custom search tools.

Claude code already fans out and sandboxes context by calling sub agents so I'm not sure this approach brings much benefit there. A complex search strategy only makes sense if the search is slow and compute intensive.

Coding agents prefer to do iterative search, I have yet to see them create a complex search script. They try different search cmds in parallel, evaluate their results and then refine or dive deeper.

This approach usually works great but I can see many use cases where a smarter search strategy may make sense especially to optimize context.

By slowing down engineers with ai agents adding multiple code reviews on top. Also encouraging engineers to engage in manual testing themselves to better understand the product.

Claude code is not infra, the model is the infra. They changed settings to make their models faster and probably cheaper to run too. Honestly with adaptive thinking it no longer matters what model it is if you can dynamically make it do less or more work.

Same here. Reviewing gets harder too and multi tasking kills any kind of productivity if you need to review the code then.

My approach these days is to do one change at a time, until I can fully merge it with confidence.

This is quite interesting, will try it. I kind of expect this to be done continuously as the code base changes.

This article doesn't mention the moat of data gathering, frontier AI labs have a huge advantage in curating proprietary datasets from actual usage of their platforms.

This in turn allows them to optimize their models for the long tail of tasks that open weight models can't compete with.

Another factor is that pure intelligence isn't enough, how the model communicates is a huge plus. An enterprise used to talking to Claude all day won't be easy to switch to another model