A phased approach to constructing the Universal Agentic Framework, from foundational research to global deployment.
Building the dataset infrastructure and proving the pipeline works for a constrained domain. This phase focuses on creating the "toy" engine that demonstrates the core concept without requiring massive computational resources.
Using existing LLMs (GPT-4, Claude) as "teachers," we generate millions of (Natural Language → Wasm) training pairs. The goal is to create a high-quality synthetic dataset that captures the relationship between intent and executable logic.
Solving the "training data scarcity" problem by building an industrial-scale data generation pipeline. The AI needs millions of diverse examples to learn the patterns of Universal Hex across different domains.
We define the "instruction set"—what the agent is allowed to do—starting with pure logic (math, strings) and basic I/O (2D canvas). The pipeline runs 24/7, generating prompts, compiling Rust to Wasm, and extracting training pairs.
Fine-tuning the language model that becomes the UAF agent. We start with a pre-trained base model (Llama 3 8B or Mistral 7B) and teach it to speak in hexadecimal bytecode instead of Python or natural language.
Using LoRA (Low-Rank Adaptation) for efficient training on consumer hardware. The model learns to map natural language prompts to WebAssembly instructions, understanding memory layout, type systems, and control flow at the bytecode level.
Building the "Designated Compiler" that runs anywhere and keeps users safe. This is the WASI implementation that gives Wasm modules controlled access to system resources while maintaining security through sandboxing.
Implementing resource limits, capability-based security, and optional Guardian AI for pre-execution bytecode scanning. Creating platform-specific bindings for web, desktop, and mobile deployment.
Building the user-facing interface where anyone can command the UAF agent. A simple chat interface backed by the neural frontend, streaming bytecode generation, and real-time execution feedback.
Public API launch, documentation, developer tools, and community building. Expanding the capability set to include graphics, physics simulation, data processing, and network operations.