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02 / How it works

One canvas.
A connected foundation.

The proposed architecture connects a shared computing framework, a personal desktop, and an evolving ecosystem of capabilities.

The proposed architecture

Everything working
together.

The original concept calls the underlying framework WebSpinner OS. Inference Desktop™ is the human-facing canvas. Community Skills extend what that canvas can do.

A common foundation. A personal experience.

  1. 01

    The foundation

    WebSpinner OS

    The shared framework for execution, applications, local inference, and safeguards.

  2. 02

    Your canvas

    Inference Desktop

    A place to think, create, and work with AI through natural language.

  3. 03

    The growing ecosystem

    Agents, Spinners & Skills

    Capabilities and community contributions that extend the computing experience.

Built into the framework

Intelligence belongs
inside the experience.

AI should be part of how the computer works.

The concept places Agents, Spinners, and Skills at the framework level. The goal is to make them part of everyday work, rather than a separate destination where people repeatedly copy information, write prompts, and move results back into other tools.

Within this vision, the desktop is the meeting place between a person’s intent and the capabilities available to act on it. Natural language makes those capabilities more accessible; reusable components make the environment more adaptable.

The source concept establishes these building blocks without prescribing a final interface or a complete technical specification. Their detailed behavior remains part of the system’s development.

The intended experience

Begin with what
you want to accomplish.

  1. 01

    Express an intention.

    Describe the work in everyday language: an idea to develop, information to understand, or a process to improve.

  2. 02

    Bring capabilities together.

    The framework is intended to coordinate available Agents, Spinners, and Skills within the desktop environment.

  3. 03

    Shape what comes next.

    Build on the result and add useful capabilities over time, so the canvas evolves with the work.

A local foundation

Bring inference closer to your data.

Local infrastructure and local inference are central to the architecture. The aim is greater ownership over how intelligence is used, with safeguards placed alongside the inference layers.

A continuous evolution

Grow through shared capabilities.

The concept envisions a collaborative marketplace of community Skills and structural components. It describes a direction for contribution and reuse, rather than an already operating store.

The commitments behind the architecture

Capability needs
a foundation of trust.

Read our principles