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Scott Wu is the co-founder and CEO of Cognition Labs, the creators of Devin, an AI agent designed to function as a junior engineer on software development teams. In this conversation, Scott demonstrates how his team uses their own product to accelerate development workflows, reduce engineering toil, and handle routine tasks asynchronously. Scott walks us through real examples of how Devin integrates into Cognition’s daily operations—from researching and implementing new features to responding to crashes and handling frontend fixes. He explains how Devin differs from traditional AI coding assistants by functioning more like a team member than a tool, allowing engineers to delegate well-scoped tasks while focusing on higher-level problems.
What you’ll learn:
1. How to use DeepWiki to research your codebase and generate better prompts for AI engineering tasks
2. A workflow for treating AI agents as asynchronous junior engineers who can handle multiple tasks while you attend meetings
3. Why public channels create better learning environments for both humans and AI when implementing engineering solutions
4. The top five engineering tasks AI excels at: frontend fixes, version upgrades, documentation, incident response, and testing
5. How to implement a “first line of defense” system where AI agents analyze crashes before humans need to intervene
6. A technique for bringing voice AI into meetings as an additional participant to answer questions without disrupting flow
Where to find Scott Wu:
LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
In this episode, we cover:
(00:00) Introduction to Scott Wu and Devin
(03:53) Where Devin excels
(06:08) Using DeepWiki to research codebases and create better prompts
(10:27) Prompting tips
(11:24) The asynchronous nature of working with Devin
(13:38) Multithreading tasks
(14:43) Using Devin to implement an MCP server integration
(18:38) Setting up workflows in Slack for first-line responses
(23:22) Encouraging AI adoption in public Slack channels
(25:50) Top five engineering tasks for Devin
(32:17) Using ChatGPT voice as a meeting participant
(35:57) Lightning round
Tools referenced:
• Devin: https://devin.ai/
• DeepWiki: https://deepwiki.org/
• ChatGPT: https://chat.openai.com/
• Windsurf: https://windsurf.ai/
• Slack: https://slack.com/
• Linear: https://linear.app/
• GitHub: https://github.com/
Other references:
• MCP (model context protocol): https://www.anthropic.com/news/model-context-protocol
• TanStack Router: https://tanstack.com/router/
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