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9 demos of Gemini Omni and Gemini 3.5 in action

These demos provide concrete examples of how the latest Gemini models can be integrated into real-world applications, helping developers quickly assess their potential for automation and enhancement.

Google AI Blog··1 min readrelease
release9 demos of Gemini Omni and Gemini 3.5 in action
blog.google

What happened

Google has released nine demonstration videos showcasing the capabilities of its latest AI models, Gemini Omni and Gemini 3.5, as announced at the I/O 2026 conference. The demos illustrate a range of practical applications, including multimodal reasoning, real-time code generation, and interactive problem-solving. These videos are designed to help developers understand how the models can be integrated into real-world workflows, from automating complex tasks to enhancing user interfaces. Notably, the demos highlight improvements in efficiency and accuracy over previous iterations, offering a glimpse into the models' potential for building more responsive AI-powered tools. For developers and solopreneurs, these demonstrations serve as both inspiration and technical reference, showing concrete ways to leverage Gemini's capabilities in automation, content generation, and interactive experiences. The release underscores Google's continued push to make advanced AI more accessible and practical for everyday development challenges.

Key takeaways

  • Google published 9 demo videos of Gemini Omni and Gemini 3.5 from I/O 2026.
  • Demos cover multimodal reasoning, code generation, and real-time interaction.
  • The models show improved efficiency and accuracy over prior Gemini versions.
  • Videos are intended as practical references for developers building AI workflows.
  • Demonstrations include both automation tasks and user-facing applications.

Why it matters

These demos provide concrete examples of how the latest Gemini models can be integrated into real-world applications, helping developers quickly assess their potential for automation and enhancement.

This is an original editorial digest by AI Workflow Pro. Full reporting at the source:

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