Logic analyzer data dumps are pretty huge. How can you interact with that and now burn a ton of tokens on huge data files?
I guess it would be no different from any other huge data files (very common on any workflow - megabytes of logs, CSV data etc.) - so it would read parts of it, write ad-hoc python analysis scripts and whatever it already does in similar cases.
In fact I think Claude is saving tokens in reading input files pretty aggressively, rarely more than a few hundred lines, maybe 1-2k tokens at once. It's nearly a wonder how well it works with strategic spot reads, and sometimes it indeed doesn't work and it misses something by not reading enough.
It's interesting to see humans work in a very similar way, because our working memory is small and slow. Glancing through, doing spot checks. Writing throwaway tooling is what really good human developers do a lot, and LLMs are good at vomiting that kind of analysis code in split second and somehow get it right the first try, so that's a really good and relatively low-cost pattern for them. This pattern alone seems to make them better than average human developers who are afraid of writing quick tooling.