RTK reports token savings, but our cost benchmarks disagree

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• Hacker News
• This makes sense. “Don’t try to penny-pinch your employees” is a lesson most managers learn eventually, and I guess agent-orchestrators will have to learn it too.
  by fwlr
• The technique looked dubious from the start, because LLMs were trained to expect certain outputs from common bash tools. If the output is not what it expects, an LLM may issue more tool calls than before, because it will assume the tool is broken, the arguments passed to it were wrong, or it's a newer/older version of the tool etc => more tokens. Sounds like just adding to the prompt to use `grep` and `tail` extensively will do the trick without any special tooling.
  by kgeist
• I wrote article about it couple months ago that I didn't believe it works. Nice to see numbers now

  https://mroczek.dev/articles/the-token-compression-illusion-...

  by lackoftactics
• Glad to see that more and more people realize these are just snake oils. Without objective metrics like benchmarks, none of the claims mean anything.

  That's also how I feel about skills/plugins. While some provide important context for specific projects/environments, I am very skeptical about (over)generalized skills like "writing JS tests" or "creating a spec". There are dozens of these skills internally at my company, but I haven't seen a single benchmark that shows any of those are better than just plain, single sentence prompts in a meaningful way (aka statistically significant).

  by fg137
• Main takeaway:

    Average cost per attempt, without → with RTK:
    
    Claude/Fable: $1.72 → $1.64 (~5% cheaper)
    DeepSeek: $0.115 → $0.121 (~5% more expensive)
  
    Almost all Claude savings came from a single task.
    Excluding it, savings were under 1%.
  

  It took me a few rereads to parse out the top-line. This article really buries the lede.
  by gillesjacobs
• It 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...

  by ProjectBarks
• It's pretty damn obvious to anyone who ever bothered to look at rtk gain output, no benchmark needed at all. Agent runs

    rtk command-that-prints-100k-tokens | tail -5
  

  costs 5 lines, maybe 100 tokens without rtk, but rtk will report 100k savings. Of course it doesn't know about that tail -5.

  Worse, since rtk defaults to persisting that savings stat, it breaks sandboxing. Prefixing with rtk leads to random auto-mode denials from time to time too (this is independent of disabling savings stat persistence).

  Honestly have no idea why anyone who knows the first thing about CLIs would take rtk gain seriously. I guess clueless vibe coders who has hardly ever worked in a terminal before will look at the stat and feel good about it?

  That said, rtk is still mildly useful for compressing repeated test run outputs and stuff, but you should only ever use it on whitelisted commands; wrapping everything like they suggest you to do is just stupid.

  by oefrha
• All of these "hacks" are snakeoil and I think deep down we all know. Whether it's caveman, RTK, or whatever other vibe-coded productivity/token cost saving hacks/skills/claude.md.

  What I had success with (although benchmarks are older) is to index the codebase with a dedicated local code embedding model. It's a bit expensive on the CPU side but in my benchmarks it reduced token use and wall clock time significantly. Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt.

  Why does it work you may ask? Well, LLMs basically brute force words/phrases and pipe that into find/grep/pgrep/whatever (or as recently discussed here write a python script for it - https://news.ycombinator.com/item?id=49654229). Semantic search looks for similarities so you have to do less brute forcing. Comes of course at the cost of indexing everything first.

  You can find the project here: https://github.com/ory/lumen

  by aeneas_ory