

Coding in Collaboration with AI with Sourcegraph CTO Beyang Liu
41 snips Jan 18, 2024
Beyang Liu, Co-founder and CTO of Sourcegraph, shares his insights on the intersection of AI and code development. He discusses how AI can alleviate the tedium in software engineering, enabling teams to produce more accurate work. Their new AI assistant, Cody, aims to enhance coding accuracy and efficiency by providing robust context. Beyang explores the future of software development, pondering the balance between human input and AI-generated code, and emphasizes the need for evolving skills in the face of advancing technology.
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Beyang's AI Background
- Beyang Liu's first CS love was AI/ML, focusing on computer vision at Stanford with Daphne Koller.
- Back then, neural nets were considered a failed experiment, and graphical models were dominant.
Context is King
- Choosing and structuring large repo context is key for effective AI code generation.
- Sourcegraph augments LLMs with context from code search and "graph context" to improve accuracy.
Code Search Architecture
- Code search within AI coding tools functions like a generalized search problem, similar to web search.
- It uses a two-layered architecture with retrievers (keyword, embedding) and a reranking layer for precision.