How to Use Spatial Computing with AI Assistants: Practical Examples with AnyJev and Nemotron 3

Introduction: Why Spatial Computing and AI Assistants Make a Great Team

Over the past few months, the line between physical space and digital intelligence has become increasingly blurred. Spatial computing devices track movement, context, and interactions in real time, while AI assistants are evolving from simple text chatbots into space-aware partners. Together, these technologies are transforming how we work, learn, and entertain ourselves.

This article explainshow to combine spatial computing and AI assistantsusing cutting-edge open tools like Nokia’s AnyJev and NVIDIA’s new Nemotron 3 Diarization model. You’ll discover real-world use cases, practical code examples, and a prompt engineering checklist to help you get started quickly.

Foundations: What Are Spatial Computing and AI Assistants?

Spatial Computing

Spatial computing is a paradigm that extends the user interface beyond the screen, incorporating depth, orientation, and gestures. Key devices include:

• Mixed-reality headsets (e.g., HoloLens 4, Meta Quest Pro 3)
• 3D touchless displays and wall projection
• High-fidelity motion sensors (LiDAR, depth cameras, IMU)

These sensors collect raw spatial data, such as positions, depth maps, finger trajectories, voices, and environmental cues.

AI Assistants

Modern AI assistants go beyond simple natural language processing. They are multimodal models capable of:

• Interpreting images, videos, and spatial inputs
• Maintaining contextual continuity across multiple modalities
• Executing actions in the physical world (controlling IoT devices, 3D rendering, task planning)

When combined with spatial computing, AI assistants become partners that are aware of their surroundings.

Advanced Techniques: Integrating TypeSafe AI and Jev into Your Workflows

An emerging trend is the use of TypeSafe AI models likeJevto transform generic LLMs into structured decision systems. Jev is designed to handle non-textual judgments (e.g., "What is the optimal position for a 3D marker?") while maintaining type traceability and calibrated trust.

Why TypeSafe AI Is Important for Spatial Scenarios

• Reliability:Type checks prevent incorrect inferences about coordinates or depth tags.
• Debuggability:Data flow diagrams are explicit, making it easier to debug spatial pipelines.
• Scalability:Type-level controls allow you to move from prototypes to production with calibrated trust.

When working with AnyJev (Nokia’s open-source library that transforms any open LLM into a decision model), you get a direct bridge between raw sensor data and Jev’s structured judgments, without any further training required.

Practical Example: Building a Spatial AI Assistant with AnyJev and Nemotron 3 Diarization

Goal: Create an AI assistant that, in an augmented reality environment, can:

1. Detect who is speaking in the area (real-time speaker diarization)
2. Interpret the voice command and map it to 3D actions (e.g., move a model)
3. Return a contextualized response, maintaining spatial context.

Below is a code example that demonstrates the integration:

# Install dependencies (once)
!pip install anyjev transformers torch torchvision

# Load the Nemotron 3 Diarization model for speaker detection
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from anyjev import AnyJev

# The diarization model is available on Hugging Face
model_id = "nvidia/Nemotron-3-Diartization"
processor = AutoProcessor.from_pretrained(model_id)
speaker_model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)

# Initialize AnyJev with an open base LLM (e.g., open_llm_13b)
llm_name = "open_llm_13b"
anyjev = AnyJev(llm=llm_name, schema=structured_decision_schema)

# Spatial processing pipeline (simplified example)
def process_spatial_interaction(audio_chunk, depth_map, hand_gesture):
    # 1. Speaker diarization
    inputs = processor(audio_chunk, sampling_rate=16000, return_tensors="pt")
    speaker_ids = speaker_model(**inputs).logits.argmax(dim=-1)
    
    # 2. Extract spatial features
    spatial_context = {
        "depth_map": depth_map,
        "hand_gesture": hand_gesture,
        "timestamp": datetime.utcnow().isoformat()
    }
    
    # 3. Create a structured prompt for Jev
    prompt = f"""
    Who is speaking: {speaker_ids[0]}
    Voice command: {audio_chunk.text}
    Spatial context: {json.dumps(spatial_context)}
    Required action: move the red cube 0.5 meters forward
    """
    
    # 4. Execute Jev's safe judgment
    judgment = anyjev.run(prompt)
    return judgment

This workflow demonstrates how AnyJev can transform a raw LLM into a reliable decision system, while Nemotron 3 Diarization provides the speaker diarization loop needed for a multi-user spatial AI assistant.

Best Practices and Prompt Engineering Tips

• Always specify units:"move 0.5 meters forward" instead of "a bit forward"
• Include spatial context:Depth, gestures, and speaker positions help Jev avoid ambiguities
• Use type controls:Define schemas for coordinates (e.g.,{"x": float, "y": float, "z": float}) before passing the prompt
• Validate calibrated trust:AnyJev returns a confidence score; ignore low-confidence actions in critical contexts
• Iterate with feedback:Record Jev's decisions and actual outcomes to refine the schema

Takeaway: How to Get Started Right Now

1. Install AnyJevand a compatible diarization model (Nemotron 3 Diarization is a great open-weight choice)
2. Define a structured decision schemafor your domain (e.g., 3D transformations, object tagging, command routing)
3. Create a data acquisition workflowthat combines audio, depth maps, and gesture inputs
4. Try your first spatial AI assistantusing the code snippet above
5. Monitor trustand accuracy; refine the prompt and schema based on results

Conclusion

The combination of spatial computing and AI assistants is giving rise to truly contextual and immersive experiences. By leveraging cutting-edge open-source advancements like Nokia AnyJev and NVIDIA Nemotron 3 Diarization, developers can build spatial AI assistants that are safe and space-aware without having to resort to heavy training pipelines.

Whether you're building an AR workstation for remote collaboration, a navigation assistant for vehicles, or an interactive gaming experience, the models and practices outlined in this article provide you with a concrete roadmap to get started. Combine spatial data with structured decision-making, and you'll have an AI assistant that not only sees the world, but acts in it with precision and confidence.