Episode Details
EPISODE INFO
- Released
- January 18, 2024
- Duration
- 46m
- Channel
- No Priors
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Coding in collaboration with AI can reduce human toil in the software development process and lead to more accurate and less tedious work for coding teams. This week on No Priors, Sarah talked with Beyang Liu, the cofounder and CTO of Sourcegraph, which builds tools that help developers innovate faster. Their most recent launch was an AI coding assistant called Cody. Beyang has spent his entire career thinking about how humans can work in conjunction with AI to write better code. Sarah and Beyang talk about how Sourcegraph is thinking about augmenting the coding process in a way that ensures accuracy and efficiency starting with robust and high-quality context. They also think about what the future of software development could look like in a world where AI can generate high-quality code on its own and where that leaves humans in the coding process. 0:00 Beyang Liu’s experience 0:52 Sourcegraph premise 2:20 AI and finding flow 4:18 Developing LLMs in code 6:46 Cody explanation 7:56 Unlocking AI code generation 11:00 search architecture in LLMs 16:02 Quality-assurance in data set 18:03 Future of Cody 22:48 Constraints in AI code generation 30:28 Lessons from Beyang’s research days 33:17 Benefits of small models 35:49 Future of software development 42:14 What skills will be valued down the line
SPEAKERS
Sarah Guo
hostBeyang Liu
guest
EPISODE SUMMARY
In this episode of No Priors, featuring Sarah Guo and Beyang Liu, No Priors Ep. 47 | With Sourcegraph CTO Beyang Liu explores sourcegraph CTO Beyang Liu on AI’s Real Impact on Coding Workflows Sourcegraph CTO and co-founder Beyang Liu explains how their AI assistant Cody builds on a decade of work in code search and code understanding to make developers dramatically more productive. He details how retrieval-augmented generation (RAG), graph-based code context, and search-style pipelines are as critical as the underlying large language models. The conversation explores near-term “inner loop” tools like completions and targeted commands versus the longer-term goal of AI engineers that can go from issue description to production-ready pull requests. Liu also shares a forward-looking view of software development where AI compresses boilerplate work, magnifies the importance of CS fundamentals and domain expertise, and helps teams operate with far greater cohesion and visibility.
RELATED EPISODES
