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AIHow We Use AlphaEvolve to Make Complex IDE Algorithms Faster
AlphaEvolve is a Google DeepMind algorithm-discovery system that uses Gemini to generate, test, and refine possible algorithm improvements. Its job is not to answer questions; it searches for faster ways to solve complex algorithmic problems. We tried it on a narrow but important part of IntelliJ-based IDEs: indexing, the background work that makes navigation, search, completion, refactorings, inspections, and other code insight available after a project opens.
That makes indexing speed a simple metric to say out loud and a hard metric to improve. It depends on the language, the framework, the shape of the project, background IDE work, and the storage layer underneath the indexes. Small changes can disappear in noise. Some wins are real in a microbenchmark and invisible in a full IDE run.
We already invest a lot of engineering time here, and that manual performance work continues. The experiment described in this post was not a replacement for engineering judgement, profiling, code review, or product validation. It was a test of an additional search method: could Google DeepMind’s AlphaEvolve help us find useful optimization candidates in code that had already been worked on for years?
