Lessons Learned from Building GitHub Copilot(s)
summary
Built original GitHub Copilot, Copilot for Docs, and Copilot Workspace and report concrete outcomes: 1.8 million paying users and ~50% of some users’ code written with Copilot, a homegrown vector DB that fails an A/B test for private code tuning but later becomes the core of neural retrieval→Copilot for Docs, and a simple neighboring tabs context injection that boosts quality as much as a model generation upgrade. Multiple UI rewrites for Copilot Workspace land a brainstorming-first flow that users prefer.
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chapters (16)
- 0:00GitHub Copilot lessons
- 1:27AI developer tools for software
- 3:55AI innovation lessons
- 6:22Delivering results frequently
- 8:53Urgency via real deadlines
- 10:18Project selection discipline
- 12:46Avoid long-term ownership
- 14:11Why UX matters in tools
- 19:05Prototypes for validation
- 20:53Simplest solutions first
- 22:41Evaluation suites for quality
- 24:26Cost versus user value
- 25:52Monitoring ML literature
- 26:56Dogfooding and feedback
- 28:40Three Copilot takeaways
- 29:45Q&A on business and velocity
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