Have you benchmarked it in real condition to test if it truly reduces costs ? I feel like many of those type of tools often end up making the llm more lost about the logs and make it more spend than just feeding the raw logs or letting it code to discover the content itself
A few days ago I made something to use in addition to head/tail: Venetianblinds.js shows equally spaced chunks of a file https://github.com/firasd/venetianblinds
So I can kinda see these as being part of the same workflow like VBlinds for 'what does this file even look like' before calling the decompose
Finally, a tool that tackles this problem without forcing you to adopt an entire ecosystem. I appreciate how this leans into the Unix philosophy by just focusing on the decomposition step and doing it well. Really appreciate the modular architecture here.
Thank you and we do really appreciate it. The Unix philosophy was certainly one of the influences upon us, we wanted CtrlB Decompose to carry out one task well, converting noisy logs into structured and compact representations which are easier for both humans and LLMs to handle.
Have you benchmarked it in real condition to test if it truly reduces costs ? I feel like many of those type of tools often end up making the llm more lost about the logs and make it more spend than just feeding the raw logs or letting it code to discover the content itself
Very interesting.
A few days ago I made something to use in addition to head/tail: Venetianblinds.js shows equally spaced chunks of a file https://github.com/firasd/venetianblinds
So I can kinda see these as being part of the same workflow like VBlinds for 'what does this file even look like' before calling the decompose
Finally, a tool that tackles this problem without forcing you to adopt an entire ecosystem. I appreciate how this leans into the Unix philosophy by just focusing on the decomposition step and doing it well. Really appreciate the modular architecture here.
Thank you and we do really appreciate it. The Unix philosophy was certainly one of the influences upon us, we wanted CtrlB Decompose to carry out one task well, converting noisy logs into structured and compact representations which are easier for both humans and LLMs to handle.