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.
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
So the new implementation always operates at the line level, replacing one or more lines. That's not ideal for some refactorings like rename where search and replace is faster.
Edit
Checking ohmypi The model has access to str replace too so this is just a edit till
How? You don't know what the llm was trained on and don't know if it has any bias.
Imo llms are a disaster for knowledge work because they act like a black box.
Agreed it probably contributes to the model improving for all agents but crucially it is verifiably better against their own agent. So they get a good feedback loop to improve both
Yeah that's one example, but I suspect they train the model on entire sequences of tool calls, so unless you prompt the model exactly as them you won't get the same results.
There's a reason they won the agent race, their models are trained to use their own tools.
The most imp part is editing code, to do that reliably Claude models are trained on their own str replace tool schema I think. Models find it hard to modify existing code, they also can't just rewrite whole files bcz that's expensive and doesn't scale.