I compressed a 600MB log file to 10MB and the AI still understood it all

I've been dealing with a frustrating problem:
I needed to analyze large log files using AI,
but they were way too big to fit in any context window.

Standard compression (gzip, zip) didn't help —
the AI couldn't read the compressed output.

So I built a different approach: symbolic encoding
designed specifically for how LLMs process tokens.

The results surprised me:

• 600MB log file → 10MB output
• AI comprehension: 97% (tested against original)
• Works on repetitive logs even better, sometimes higher than 60x compression

The AI could still identify errors, trace request flows,
and answer specific questions about the log content.

I'm curious:

1. Is this a real pain point for you day-to-day?
2. What do you currently do when logs are too large for AI?
3. Would you use a tool like this in your workflow?

Not pitching anything — genuinely want to understand
if others hit this wall before I build further. you can try for free