GitHub Code Search is the real MVP
Published 2025-08-11 on Farid Zakaria's Blog
There is endless hype about the productivity boon that LLMs will usher in.
While I am amazed at the utility offered by these superintelligent LLMs, at the moment (August 2025) I remain bearish on the utilization of these tools to have any meaningful impact on productivity especially for production-grade codebases where correctness, maintainability, and security are paramount.
They are clearly helpful for exploring ideas or any goal where the code produced may be discarded at the end.
Thinking about how much promise of productivity we might gain from this tool had me reflecting on what other changes in the past 5 years had already benefited me and a clear winner stands out: GitHub’s code search via cs.github.com.
Pre-2020, code search in the open-source domain never really had a good solution, given the diaspora of various hosting platforms. If you’ve worked in any large corporate environment (Amazon, Google, Meta etc…) you might have already had exposure to the powers of an incredible code search. The lack of such a tool for public codebases was a limitation we simply worked This is partly why third-party libraries were consolidated into well-known projects like Apache or established companies such as Google’s Guava.
An upside to the consolidation of code on GitHub’s platform was capitalized on with the release of their revamped code search. Made generally available in May 2023, the new engine added powerful features like symbol search and the ability to follow references.
The productivity win is clear to me, even with the introduction of LLMs. I visit cs.github.com daily, more frequently and with more interaction than any of the LLMs available to me.
Why?
Finding code written by other humans is fun, and for some reason, more joyful to read. There is a certain level of joy to finding solutions to problems you may be facing that were authored and written by another human. This psychological effect may diminish as the code I’m wading through begins to tilt toward AI-generated content. But for now, the majority of the code I’m viewing still subjectively looks like that authored by a human.
I also tend to work in niche areas such as NixOS or Bazel that don’t have a large corpus of material online so the results from the LLM tend to be more disappointing.
If given a Sophie’s choice between GitHub code search and LLMs, strictly for the purpose of code authorship, I would pick code search as of today.
Humans easily adapt to their environment, a phenomenon known as the hedonic treadmill. As we all get excited for the incoming technology of generative AI, let’s take a moment to reflect on the already amazing contribution to engineering we have become accustomed to due to a wonderful code search.
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