Yep this is a glimpse into the future of 500+ t/s, which is in my opinion the next big thing that validates Jevon's paradox (the models are already smart enough)
Using fd/rg sounds interesting, honestly it would require little tweaks to the bash tool lua plugin, either add to the description to prefer these binaries instead or something like that.
In general though I much prefer "advising" and encouraging the LLM to use the native tools like grep l/glob, they are implemented to be super fast, and you will get better parser output.
As much as I hate to admit, T
the tools you provide, the descriptions, and prompts, all amount to pretty big changes in experience, even using the same models.
The biggest ones are: using tree-sitter to index code files as a tool, code_execution tool running a workflow of tools inside a python interpreter (monty), and not being a harness developed by the company profiting from selling you the shovels (and introducing "dynamic workflows" aka spawning 50 agents).
Totally wrong, you underestimate the frontier's incompetence in anything other than building LLM models (ehm ehm flickering TUI for a year "written like a game engine").
I ran a bunch of benchmarks and there are proven ways to reduce tokens while achieving the same results (finding the same CVEs / finding the same bugs in CRs, etc...).
Yep exactly my thoughts, went and looked at the code for the deepseek provider in my coding agent. and basically all of what the author wrote there is implemented... http://github.com/tontinton/maki for the curios
Currently the subagent chat windows don't allow to inject user messages like the main window, I want to change that soon though.
Regarding tiered models, it currently caps the model use to the current tier you're on, so no it can't upgrade from haiku to opus suddenly. The reasoning for that is that if you selected haiku, you probably don't want to pay for opus by accident.
Yeah we all converge to the same workflow, in my ai coding agent I'm working on now, I've added an "index" tool that uses tree-sitter to compress and show the AI a skeleton of a code file.