caveman

Neither, just be neutral and use caveman speak. It saves tokens. Being polite consumes extra tokens, and being rude can risk you getting banned. So use caveman speak, save tokens. The best part is, you'll get curt responses as well, without any cringy flowery language that LLMs are known for. In fact, there's even a skill for it: https://github.com/juliusbrussee/caveman

caveman mode good https://github.com/JuliusBrussee/caveman

Same. I use this to fix claudespeak. Any hypothetical token savings are just gravy. https://github.com/JuliusBrussee/caveman/blob/main/skills/ca...

There is also Caveman[1] skill which rewrite prompts. 1. https://github.com/JuliusBrussee/caveman/

Along similar lines, a coworker told me about Caveman and it has made my Claude experience vastly better. I'm already a programmer, Claude shouldn't need to tell me like I'm 5. https://github.com/JuliusBrussee/caveman

how about stuff likes https://github.com/JuliusBrussee/caveman is it also make the model less capable?

There is a reason Caveman (https://github.com/JuliusBrussee/caveman) is as popular as it is

This is what you want: https://github.com/juliusbrussee/caveman

Have you tried using the caveman skill ? :D Might be worth a try if you dont like long prose https://github.com/JuliusBrussee/caveman

Neat! But I still lean towards https://github.com/juliusbrussee/caveman.

I recently started using caveman, and it’s been great. It doesn’t just cut down on overuse of specific terms; it cuts down on time spent digesting slop in general. https://github.com/JuliusBrussee/caveman

Tell the llm to answer like a cavemen, if llm talk like cavemen, the answers become shorter and more compressed. https://github.com/JuliusBrussee/caveman It's for getting it to output shorter answers, but also could help with your burnout.

My criteria is "do they measure performance, or at least even try to?". Caveman [1], RTK [2] and more recently ponytail [3] don't or use a few trivial tests. Those projects don't measure performance on widely used benchmarks (like SWE Pro and stuff), that have their issues but at least it would give some indication. They also don't measure "big model + caveman vs smaller model". I've had a few times where removing all custom instructions that I started using with model N-2 made model N perform way better, so I'm very suspicious of everything that changes how the model works, it's easy to get degraded performance silently and suddenly you're paying latest Opus costs for 6 months old Sonnet performance. [1]: https://github.com/JuliusBrussee/caveman [2]: https://github.com/rtk-ai/rtk [3]: https://github.com/DietrichGebert/ponytail