Absolutely. GitHub Code Search is by far the most valuable online development tool I have used the past year. It is so much more useful than Copilot or any of the AI LLMs in my experience.
With Code Search, I have:
* Rewritten a CMake build system, which would have been practically impossible without access to real-world examples because of how poorly designed and documented it is;
* Validated machine-generated translations by looking up language strings from projects that used human translators;
* Tracked down bugs in unfamiliar codebases, using symbol-based navigation, without the downtime of fetching a bunch of files and waiting for a language server to process them locally;
* Reviewed how projects were using the APIs of a library I work on to determine whether high-maintenance features were actually used, and whether tricky features were being used correctly or needed redesigning to reduce programmer error
Kudos to the team at GitHub. Genuinely stellar work.
For CMake stuff I would search like `<function name> path:/CMakeLists\.txt$|cmake/`.
For translations I would search a phrase using `<phrase> lang:"Gettext Catalog" path:/<language iso code>/`.
For reviewing library consumers I would just search for `<my library name> (<function name> OR <other function name> OR <third function name>) NOT path:<my library name>/**`.
There may be better queries for some of these, but I really didn’t have to do any magic or weird tricks. Everything more or less just works.
With Code Search, I have:
* Rewritten a CMake build system, which would have been practically impossible without access to real-world examples because of how poorly designed and documented it is;
* Validated machine-generated translations by looking up language strings from projects that used human translators;
* Tracked down bugs in unfamiliar codebases, using symbol-based navigation, without the downtime of fetching a bunch of files and waiting for a language server to process them locally;
* Reviewed how projects were using the APIs of a library I work on to determine whether high-maintenance features were actually used, and whether tricky features were being used correctly or needed redesigning to reduce programmer error
Kudos to the team at GitHub. Genuinely stellar work.