Building Gemini's Coding Capabilities

Google AI: Release Notes

Connie Fan, Product Lead for Gemini's coding capabilities, and Danny Tarlow, Research Lead for Gemini's coding capabilities, join host Logan Kilpatrick for an in-depth discussion on how the team built one of the world's leading AI coding models. Learn more about the early goals that shaped Gemini's approach to code, the rise of 'vibe coding' and its impact on development, strategies for tackling large codebases with long context and agents, and the future of programming languages in the age of AI.

Watch on YouTube: ⁠https://www.youtube.com/watch?v=jwbG_m-X-gE⁠

Chapters:

0:00 - Intro
1:10 - Defining Early Coding Goals
6:23 - Ingredients of a Great Coding Model
9:28 - Adapting to Developer Workflows
11:40 - The Rise of Vibe Coding
14:43 - Code as a Reasoning Tool
17:20 - Code as a Universal Solver
20:47 - Evaluating Coding Models
24:30 - Leveraging Internal Googler Feedback
26:52 - Winning Over AI Skeptics
28:04 - Performance Across Programming Languages
33:05 - The Future of Programming Languages
36:16 - Strategies for Large Codebases
41:06 - Hill Climbing New Benchmarks
42:46 - Short-Term Improvements
44:42 - Model Style and Taste
47:43 - 2.5 Pro’s Breakthrough
51:06 - Early AI Coding Experiences
56:19 - Specialist vs. Generalist Models

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