440hz

The Problem: To build highly capable, agentic AI, foundation models must move beyond static text prediction and practice in interactive environments using Reinforcement Learning (RL). However, the most valuable training data in the world is locked behind corporate firewalls. Enterprises cannot upload highly sensitive data to centralized LLM providers, and uploading terabytes of high-frequency edge data to AWS/GCP incurs massive ingress/egress fees and unacceptable latency. As AI labs hit the "data wall," they are starved of high-quality, human-generated scenarios.

The Solution: 440hz is the first decentralized, federated RLAIF (Reinforcement Learning from AI Feedback) platform built natively on the 0G ecosystem. Instead of bringing the data to the model, we push the model to the data. By leveraging Parameter-Efficient Fine-Tuning (QLoRA), 440hz allows base models to be trained locally on edge hardware that anyone can provide.

Why It's Needed: We bridge the gap between "knowing" and "doing." 440hz provides a safe, customizable sandbox for agents to learn from the consequences of their actions through trial and error—without risking real company databases or breaking production environments. It completely bypasses centralized cloud egress fees and solves the human bottleneck in RL training by utilizing small "AI Overseer" models to automatically score actions.

The Impact: By ensuring zero IP leakage—raw data never leaves the edge compute container—and guaranteeing computational integrity via ZK verified proofs, 440hz unlocks deep-tech and enterprise proprietary data for frontier AI development. It radically undercuts centralized cloud costs while creating a new decentralized marketplace connecting model tuners, domain-expert environment designers, and edge compute providers.

440hz is built on a highly modular, dual-chain architecture integrating the 0G ecosystem for robust storage and computation orchestration, alongside Base Sepolia for our identity layer.

Core Infrastructure & Architecture:

• Web3 Console (Frontend): A Next.js 16 application (React 19, Tailwind v4, TypeScript) serving as the main command center. It features a streamlined onboarding wizard, dynamic routing, a custom design system, and extensive Web3 wallet integrations.
• Decentralized Edge Compute (Backend): We built a Python training executor and FastAPI provider daemon that runs on a customized fork of 0G compute. This fork is heavily optimized for Reinforcement Learning and heavy AI fine-tuning (GRPO/PPO/DPO + QLoRA). The compute is entirely permissionless—anyone can spin up an edge node to provide compute.
• Smart Contracts: Deployed primarily on 0G Galileo (TrainingEscrow, GymMarketplace, ZKSettlementVerifier) to handle intent matching, escrow, and decentralized settlement in native 0G tokens.

Highlighted Features:

• No-Code Gym Builder: We built a drag-and-drop IDE (using Monaco and ReactFlow) for domain experts to create complex Gymnasium training environments without writing raw Python. Gym containers are compiled and pushed directly to 0G Storage. It includes integrated AI chat (OpenRouter/Groq/0G broker) to assist in environment design.
• Zero-Knowledge Settlement: To ensure trustless training, providers submit ZK verified proofs upon completing a training job. The ZKSettlementVerifier on the 0G Chain guarantees computational integrity before releasing escrowed funds, removing the need for trusted execution environments (TEEs).
• Federated Aggregation: When training across multiple edge nodes, 440hz utilizes Flower.ai to pull the newly trained LoRA adapters and mathematically average them (FedAvg) into a global update before baking them into the base LLM.
• Extensive ENS Integration: 440hz uses ENS (440hz.eth deployed via L2Registrar on Base Sepolia) as its foundational identity layer. The platform automatically registers subnames for all actors and assets: username.440hz.eth (users), *-gym.440hz.eth (training environments), *-model.440hz.eth, and *-weights.440hz.eth.

Niche but Essential Engineering Details:

• 0G Storage-Backed Version Control: The Gym Builder features SHA-256 content-hash change detection, enabling Github-style version history and rollbacks entirely backed by 0G Storage manifests.
• ENS as a Decentralized Database: We heavily utilize ENS text records to store verifiable metadata. For instance, after a model is trained, the ZK proof reference and the 0G Storage root hash of the LoRA adapter are written directly into the ENS text records of the model's subname (zk-proof = <hash>, adapter-ref = <hash>).
• AI-Overseer Loop: We leverage small, lightning-fast models (like Mistral Small or MiMo-v2-flash) acting as the reward function inside the edge container, fully automating the RL process.
• Custom Global Search: We implemented a unified /console/search interface that enumerates all SubnameRegistered events on Base Sepolia, allowing users to filter and seamlessly download Gyms directly to their library using ENS name resolution.