How to Create Spatial Computing Experiences with Modern AI Assistants?

Introduction: Why AI assistants are transforming spatial computing

As of November 2026, AI has taken a leap in the world of spatial computing. Platforms like Together Compiler accelerate agent inference up to 2.6x, Gemini Omni 1.2 Flash generates 15-second videos for just $0.07 per second, and Inkling 2 Small offers a 594B MoE model with only 17B active parameters. These innovations make AI assistants essential tools for designing, navigating, and interacting with 3D environments fluidly.

How to approach developing intelligent spatial interfaces

When working with augmented reality (AR) or virtual reality (VR), the first step is to define a prompt-based workflow that leverages the multimodal capabilities of AI assistants. Here's a practical template you can copy and adapt:

1. Define spatial intent

• Goal:Create an interactive museum guide.
• Users:Visitors with varying levels of knowledge.
• Channels:Voice, gestures, and 3D visualizations.

2. Write a base prompt

Use simple, action-oriented language and include explicit spatial constraints:

Design an AR museum guide that:
- Displays interactive 3D information panels when the user points the camera at an artwork.
- Provides voice descriptions in Italian and English.
- Reacts to gestures such as touching or rotating the object.
- Uses the latest Together Compiler models for real-time inference.

3. Optimize the prompt for the model

Add instructions for length, tone, and layout:

• Maximum 150 words per description.
• Tone: friendly, educational.
• Use markup for 3D highlighting:<highlight id="painting_001" position="x,y,z"/>.

How to leverage Together Compiler for faster inference

Together Compiler is an agent-driven loop that continuously optimizes the forward pass during traffic. For spatial AI assistants, this means:

• Low-latency responses when a user points the camera.
• Dynamic adaptation to pixel density changes in complex scenes.
• Reduced operational costs thanks to fewer tokens sent over the network.

To integrate Together Compiler into your workflow, configure an agent loop like this:

agent = Agent(
    model="TogetherCompiler",
    trigger="user_points_camera",
    optimization_interval=0.5,  # seconds
    target_latency=100  # ms
)
agent.start()

When to use Gemini Omni 1. Mẹ Flash for spatial video content

If you need 15-second video clips to animate 3D objects, Gemini Omni 1.2 Flash is the ideal choice. For just $0.07 per second, you can:

• Generate short product rotations in a virtual exhibition space.
• Create interactive tutorials with real-time multimodal editing.
• Synchronize audio with precise gestures using built-in vision and language models.

Example prompt for Gemini:

Generate a 15-second video of a collectible vase slowly rotating on a transparent platter, with soft ambient lighting, in a loop for AR display.

Why Inkling 2 Small is perfect for spatial agents

With 594B MoE parameters and only 17B active, Inkling 2 Small offers an exceptional balance of power and efficiency. For spatial AI assistants, this means:

• High-resolution scene processing while maintaining fast response times.
• Low-memory adaptation on edge devices such as AR headsets and VR controllers.
• Continuous improvement through user feedback, using only the necessary active parameters.

Baidu's ERNIE 5.5 Flash Base, released in November 2026, provides a comparable 280B MoE with just 14B active parameters, offering another efficient option for edge spatial agents.

Practical takeaways and implementation checklist

1. Define spatial intentbefore writing any prompt.
BTCUse structured prompt engineeringwith explicit constraints for 3D layout.
2. Integrate Together Compilerto optimize agent latency.
3. Leverage Gemini Omni 1.2 Flashfor cost-effective 15-second spatial video.
4. Choose Inkling 2 Smallwhen you need broad intelligence with limited resources.
5. Test on real devices(AR/VR headsets) to validate performance and user feedback.

Conclusion: The future of spatial computing is already here

Modern AI assistants, powered by tools like Together Compiler, Gemini Omni 1.2 Flash, and Inkling 2 Small, are redefining what's possible in spatial computing. By writing clear prompts, leveraging optimized inference, and selecting the right models for each use case, you can create engaging 3D experiences that adapt seamlessly to user needs. Start today with the workflow above and take your AR/VR applications to the next level.

Additional resources

• Together AI documentation:together.ai/compiler
• Gemini Omni developer guides:ai.google/gemini-omni
• Inkling 2 Small research paper:thinkingmachines.ai/inkling2