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.
I should not trust their "vibe-coded productivity/token cost saving hacks" but I should trust yours?
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Why should I trust that what you're peddling isn't snakeoil?
Jetbrains IDEs are a perfect solution for this. They expose IDE actions (e.g, search, see occurrences, go to implementation) in their MCP server, which the harnesses can then call directly instead of figuring out the code themselves.
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:
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.
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.
I don't understand how this or all these magic skill bundles and methodologies get traction and why they are so popular. It's either plain worse or has serious trade offs.
I don't think they're comparable. RTK just modifies the output of CLI tools to reduce the number of tokens, a Roslyn MCP gives the agent a fundamentally superior way of interacting with a C# codebase.
Just another instance of the bitter lesson. The model itself knows how to be clever and conserve tokens in command output by using shell primitives and as the models get smarter they get better at anticipating large output and defensively adapting the input commands.
What if the next-gen models are trained on RTK output as well? Then you will actually have less tokens in the context window, and the model won't become confused (which would require more turns, wasting tokens)
doesn't change the fact that it doesn't do what it claims to now. I just don't care about vague promises and "trust us bro" vibes that tech is sold for nowadays. It claims x, it doesn't deliver x. Maybe it could in the future, or maybe not.
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
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...
It was so slow that the roslyn results would be lagged well behind any edits it was making, which would just leave it confused.