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Coffee With Developers • Jun 10, 2026

GitHub Next and the Future of Coding - Idan Gazit

Idan Gazit

Idan Gazit reveals why AI makes typing code obsolete. To survive, engineering teams must shift from writing syntax to curating agent-generated code and mastering architectural judgment.

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#1 about 2 min

Mission and goals of the GitHub Next research team

Exploring second-order impacts of artificial intelligence to make software development faster and more accessible.

#2 about 2 min

Adapting collaboration to zero-cost code creation

When generative AI handles typing, developer alignment must happen before agents draft multiple code variations.

#3 about 2 min

Incorporating human context into agentic software workflows

Providing business context and human preferences helps guide AI agents toward successful organizational outcomes.

#4 about 4 min

Shifting from asynchronous pull requests to real-time collaboration

Real-time communication layers help integrate human intent with agent actions earlier in the development process.

#5 about 3 min

Infrastructure scaling challenges from oversized agent pull requests

Agentic environments generate massive pull requests that overwhelm continuous integration and code hosting infrastructure.

#6 about 6 min

Elevating junior engineers into technical leadership roles

Organizations must invest in junior developers to build the senior engineering talent required to supervise AI models.

#7 about 3 min

Evolving user interface metaphors from files to systems

AI coding tools are moving away from file-level text selection toward system-level refactoring instructions.

#8 about 4 min

Measuring developer productivity with artificial intelligence adoption

Justifying AI expenditure requires observing overall deployment speed rather than relying on flawed granular metrics.

#9 about 2 min

Prioritizing cognitive orientation over raw code generation

Generative AI provides the highest value by helping engineers understand existing systems rather than simply typing code.

#10 about 7 min

The hardware accessibility gap for local AI processing

Running capable local language models requires expensive hardware, forcing developers to rely on cloud subscription services.

#11 about 2 min

Evaluating distributed computation networks for AI model execution

Leveraging peer-to-peer networks for model computation remains impractical due to severe container security vulnerabilities.

#12 about 3 min

Developing AI tooling intuition through continuous practice

Engineers must actively practice prompting and context engineering to build a fingertip feel for AI capabilities.

Matching moments

4:04 min

Introduction to GitHub Next and AI prototyping

Krzystof Czieslak · LIVE

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann Chris Heilmann +2 · LIVE

5:16 min

Motivations for adopting AI to enhance developer productivity

51 sec

Impact of AI tools on developer collaboration

Brooke Gazdag Brooke Gazdag · Europe 2026 Virtual

2:38 min

Focusing on developer happiness at GitHub Next

Krzysztof Cieślak Krzysztof Cieślak · World Congress 2026 Europe

2:11 min

Understanding GitHub Copilot and core developer benefits

lgonta lgonta +1 · World Congress 2024

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