Interview
summary
A startup applies neural networks to source code using a 3 TB dataset of GitHub to train models that predict bugs, suggest better names, and detect when two pieces should be merged. Pipelines ingest repos, parse files into tokens then ASTs and ASTs and control-flow graphs that become graph inputs for learning. The team prototypes an assistant code review bot that runs before human review and flags likely bugs and refactor opportunities; engineering focuses on scaling data pipelines and model inference.
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