Sourcegraph have built the most popular open source AI coding tool in both the dev community and the Fortune 500. I sat down with Beyang Liu their CTO and cofounder to find out how they did it.

We dive into the technical details of Cody's architecture, discussing how Sourcegraph handles the challenges of limited context windows in LLMs, why they don't use embeddings in their RAG system, and the importance of starting with the simplest approach before adding complexity.

We also touch on the future of software engineering, open-source vs closed LLM models and what areas of AI are overhyped vs underhyped

I hope you enjoy the conversation!

Chapters
- 00:00:00 - Introduction
- 00:02:30 - What is Cody, and how does it help developers?
- 00:04:15 - Challenges of building AI for large, legacy codebases
- 00:07:30 - The importance of starting with the simplest approach
- 00:11:00 - Sourcegraph's multi-layered context retrieval architecture using RAG
- 00:15:30 - Adapting to the evolving landscape of LLMs and model selection
- 00:19:00 - The future of software engineering in the age of AI
- [00:23:00 - Advice for individuals navigating the AI wave
- 00:26:00 - Predictions for the future of AI in software development
- 00:30:00 - Is AI overhyped, underhyped, or both?
- 00:33:00 - Exciting AI startups to watch
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Humanloop is an Integrated Development Environment for Large Language Models. It enables product teams to develop LLM-based applications that are reliable and scalable. To find out more go to https://hubs.ly/Q02yV72D0

Building an AI coding assistant with Beyang Liu CTO of Sourcegraph