Introduction: Why spatial computing is revolutionizing AI assistants
The answer to the most common question is straightforward:
- How can spatial computing be leveraged with modern AI assistants? By combining spatial sensors (LiDAR, depth cameras, GPS), a secure sandbox-runtime like Anthropic’s, and optimized models such as Underdog Saluki 27B.
What makes spatial computing unique in 2026
1. Increasingly integrated sensors and middleware
Newer generations of IoT devices integrate perception pipelines based on TensorRT directly into the operating system, eliminating the need for separate cloud services for each frame. This enables AI assistants to receive depth data, images, and inertial measurement unit (IMU) data with latency under a millisecond.
2. OS-level sandbox-runtime
As described in Anthropic’s October 24, 2026 post, their sandbox-runtime (srt) provides kernel-level isolation for AI agents without relying on traditional containers. This is critical when an assistant needs to manipulate map files or access peripheral devices: the runtime restricts access to only explicitly granted resources, safeguarding the host system.
3. Models optimized for spatial computing
Underdog Saluki 27B, based on the 2-bit quantization of Qwen3.8-27B Tool-Calling, delivers exceptional performance on edge hardware with minimal power consumption. A guide published on October 25, 2026, demonstrates how to load the model into a development environment and integrate it with a custom spatial recognition module.
Building a spatial assistant: a practical example
Below is a complete workflow that combines the three trends mentioned above. The goal is to create an AI assistant capable of mapping a room, identifying objects, and suggesting context-based actions.
Step 1: Set up the sandbox-runtime
# Install Anthropic's sandbox-runtime (srt)
pip install srt-sdk
# Initialize a new agent with limited permissions
srt run --policy spatial_agent \
--allow read:/tmp/maps \
--allow network:localhost:8080Step 2: Load a compact spatial model
# Download the quantized version of Saluki 27B (gguf)
wget https://huggingface.co/underdog/saluki-27b-2bit-gguf/resolve/main/saluki-27b-2bit.gguf
# Export the model to a .torch file for fine-tuning
python export_model.py --model saluki-27b-2bit.gguf --output spatial_model.ptStep 3: Integrate the perception module
Create a simple service that combines LiDAR and depth camera data:
import cv2, numpy as np
from srt_sdk import secure_call
@secure_call
def process_spatial_input(image, depth):
# A minimal example of 3D object recognition
objects = detect_objects(image, depth)
return objectsStep 4: Write the assistant prompt
Prompt engineering is essential for guiding the model toward meaningful spatial actions:
SYSTEM_PROMPT = """
You are an AI assistant specialized in spatial interaction.
- Use depth and image data to identify the location of objects.
- Return a list of relevant objects with their coordinates in meters.
- Suggest an action only if you are certain the object is reachable.
"""
USER_PROMPT = "Map the room and indicate all electronic devices that are to the right of the entrance."Security and plugins: Cognition’s contribution
On October 25, 2026, Cognition open-sourced an agent plugin designed to protect systems from emerging threats. The plugin, called Devin Vulnerability Fixes, includes three MIT-licensed capabilities for:
- Inventorizing PHP/Node.js dependencies.
- Automatically patching known vulnerabilities.
- Testing self-hosted GitLab repositories.
Integrating this plugin into the sandbox-runtime ensures that any spatial assistant performs security checks before accessing sensitive network resources.
Best practices and takeaways
- Leverage the sandbox-runtime
- Use quantized models
- Write specific spatial prompts
- Add security controls