do you necessarily need to see whole codebase? or like ancillary tools: run this linter for me, show me your API footprint across the dependencies you use, give me a sense of your cloud stack. test coverage. total lines or at least ratios between programming languages.
show me a random file that exists in every codebase but isn't sensitive, like the entrypoint
show me your jenkins / CI, how long do they take, how often do tests flap
screenshot of datadog / sentry
all that said, in the case of ML, I'm guessing even the dependency graph is giving away some hard-earned information like 'this implementation of this algorithm works best for our domain'
show me a random file that exists in every codebase but isn't sensitive, like the entrypoint
show me your jenkins / CI, how long do they take, how often do tests flap
screenshot of datadog / sentry
all that said, in the case of ML, I'm guessing even the dependency graph is giving away some hard-earned information like 'this implementation of this algorithm works best for our domain'