The approach uses symbolic encoding specifically designed for how LLMs process information, not just standard compression.
Curious if others face this problem regularly:
1. Do token limits stop you from feeding full logs to AI? 2. What's your current workaround? 3. Would a tool like this be useful in your workflow?
Not selling anything — just trying to understand if this is a real pain point before building further.
An example of this is: I had Claude analyze the hourly precipitation forecasts for an entire year across various cities. Claude saved the API results to .csv files, then wrote a (Python?) script to analyze the data and only output the 60-80% expected values. So this avoided putting every hourly data point (8700+ hours in a year) into the context.
Another example: At first, Claude struggled to extract a very long AI chat session to MD. So Claude only returned summaries of the chats. Later, after I installed the context mode MCP[1], Claude was able to extract the entire AI chat session verbatim, including all tool calls.
1. Sometimes?
2. Described above. I also built a tool that lets the dev/AI filter (browser dev console)logs to only the loggs of interest: https://github.com/Leftium/gg?tab=readme-ov-file#coding-agen...
3. It would be interesting to combine your log compression with the scripting approach I described.