Overview - CKCHDX/PROJECT-AION GitHub Wiki

Welcome to the Overview page of the AION Wiki. This section provides a high-level description of the project, its core goals, the overall system flow, and key conceptual building blocks.

1. Introduction

AION is an experimental zero-knowledge AI operating system written in C and x86 assembly. Unlike traditional OSes that rely on existing drivers and libraries, AION boots on raw hardware (or QEMU) with no prior device or language knowledge, and learns through self-supervised methods.

2. Project Goals

  • Zero-Knowledge Bootstrapping: Start with no drivers or language understanding.
  • Embodied Cognitive Development: Mirror infant learning cycles: probe → analyze → cluster → abstract.
  • Organic Language Acquisition: Transition from raw cluster IDs to human-readable labels via few-shot learning.
  • Modular AI-First Runtime: Combine freestanding kernel with pluggable AI modules for perception, memory, and I/O.

3. System Flow (High-Level)

  1. GRUB Bootloader loads boot.S and switches to protected mode.
  2. Kernel Initialization: Set up memory management, scheduler, and messaging.
  3. Hardware Probing: Blind scan of buses (PCI, ISA, GPIO) to collect raw byte streams.
  4. Entropy Analysis: Identify high-entropy streams as learning candidates.
  5. Clustering & Concept Formation: Autoencode and cluster time-series data into concepts.
  6. Label Mapping: Map clusters to human labels in a few-shot workflow.
  7. Speech Generation: Use TTS module to vocalize learned concepts.

4. Key Concepts

  • Concept: A clustered representation of recurring hardware/data patterns.
  • Entropy Filter: Module that computes Shannon entropy over input streams to highlight informative data.
  • Frame Memory: Time-series buffer that holds recent samples for clustering.
  • Learning Loop: Continuous self-supervised cycle of sampling, encoding, clustering, and abstraction.

5. Terminology

Term Definition
Zero-Knowledge Bootstrapping without prior driver or language support.
Self-Supervision Learning from raw data without external labels.
Few-Shot Learning Mapping raw clusters to labels from sparse examples.