Core is a specialized personal AI computer designed to function as an autonomous, privacy-centric proactive agent that operates entirely on-device. Priced at \$3,499 with no subscription fees, the hardware serves as a dedicated platform for executing localized large language models (LLMs) to manage personal workflows, context, and daily decision-making.

Hardware Architecture and Technical Specifications

The system is built upon a high-performance, compact hardware architecture designed for local inference and continuous data processing:

• Graphics Processing Unit (GPU): NVIDIA RTX PRO 4000 Blackwell SFF Edition, featuring 24 GB of GDDR7 ECC memory and a memory bandwidth of $432 \text{ GB/s}$.
• Central Processing Unit (CPU): AMD Ryzen 5 7600, utilizing 6 cores and 12 threads.
• System Memory: 64 GB DDR5 RAM.
• Storage: 1 TB NVMe SSD.

The hardware is optimized to run models such as Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B, and Muse-Glimmer-30B locally, ensuring that sensitive user data—ranging from screen history and file contents to health metrics—remains within the device, precluding external cloud dependency.

Methodology: Contextual Intelligence and Proactive Agency

The core methodology relies on the continuous ingestion and integration of multi-modal data streams to build a longitudinal user profile. The system functions through three primary mechanisms:

1. Continuous Contextual Awareness: Unlike traditional chatbots that operate on a request-response basis, Core maintains a persistent state. It captures data from various integrations, including calendars, email, browser history, smart-home devices, and wearables (e.g., Oura, Whoop, Eight Sleep). By monitoring these inputs 24/7, the system establishes a long-term context window that evolves based on the user's specific routines, recovery scores, and habitual patterns.
2. Autonomous Inference: The system leverages its local GPU and memory resources to perform real-time inference on ingested data. By continuously analyzing the "life context," the AI identifies actionable insights (e.g., predicting grocery needs, optimizing commute times based on weather, or monitoring stress markers) without requiring explicit user prompting.
3. Proactive Assistance: The architecture is designed to trigger proactive interventions. By synthesizing disparate data points—such as correlating "early morning workouts" with "higher recovery scores"—the model generates suggestions that align with the user's stated goals, such as dietary preferences or reminder prompts for daily tasks. This proactive capability allows the system to function as an autonomous agent that manages domestic, health, and professional digital environments.