> The article is confusing the architectural layers of AI coding agents. It's easy to add "cut/copy/paste" tools to the AI system if that shows improvement. This has nothing to do with LLM, it's in the layer on top.
I think we can't trivialize adding good cut/copy/paste tools though. It's not like we can just slap those tools on the topmost layer (ex, on Claude Code, Codex, or Roo) and it'll just work.
I think that a lot of reinforcement learning that LLM providers do on their coding models barely (if at all) steer towards that kind of tool use, so even if we implemented those tools on top of coding LLMs they probably would just splash and do nothing.
Adding cut/copy/paste probably requires a ton of very specific (and/or specialized) fine tuning with not a ton of data to train on -- think recordings of how humans use IDEs, keystrokes, commands issued, etc etc.
I'm guessing Cursor's Autocomplete model is the closest thing that can do something like this if they chose to, based on how they're training it.
I think we can't trivialize adding good cut/copy/paste tools though. It's not like we can just slap those tools on the topmost layer (ex, on Claude Code, Codex, or Roo) and it'll just work.
I think that a lot of reinforcement learning that LLM providers do on their coding models barely (if at all) steer towards that kind of tool use, so even if we implemented those tools on top of coding LLMs they probably would just splash and do nothing.
Adding cut/copy/paste probably requires a ton of very specific (and/or specialized) fine tuning with not a ton of data to train on -- think recordings of how humans use IDEs, keystrokes, commands issued, etc etc.
I'm guessing Cursor's Autocomplete model is the closest thing that can do something like this if they chose to, based on how they're training it.