alt Hacker NewsIt seems like most of these tools are mostly vaporware. Benchmarks done on Headroom and RTK show that neither result in real savings. If it were possible to have such a simple pre-process step why wouldn’t the AI Labs upstream the optimizations themselves? My guess is they mostly don’t work or make the behavior much more confusing for the model. I really think there needs to be some kind of independent benchmark.
Here are other cases demonstrating the exact same issues with these kinds of tools:
https://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-... https://brandonbarker.me/writing/headroom-fewer-tokens-bigge...
Replies
> If it were possible to have such a simple pre-process step why wouldn’t the AI Labs upstream the optimizations themselves?
Not defending these tools, but one reason these might not be upstreamed is because it would negatively impact vendor margins, and they have no incentive to save their users money
Naively, I think some optimizations would require access to the whole codebase and that would make people nervous (plus incur more cost).
Eg absurd idea, but you could write something that minifies a codebase (by token, rather than byte) and then translates edits back into the expanded code. Probably an insane use of fuse lol. Partially minifying on each tool call sounds like a huge pain with a lot of state to track.
There’s also a lot of common situations where humans prefer solutions that take more tokens because it’s easier for us to read (eg for loop vs map vs list comprehension), which may have some gains.
I strongly suspect there is some form of token compression that works, but I don’t think it will be as simple as “pipe arbitrary text with no context into this tool”.
Jetbrains feels like a place this might come from. “Take this code, parse it to an AST, find the fewest token representation of it” feels like something they’d do, or maybe Astral (specifically in Python land, type checkers feel sort of adjacent as well).