LINAYY

API Integration
OpenLedger provides a streamlined framework for interacting with custom-trained large language models through secure API endpoints and an adaptable chat interface. Developers can easily authenticate, manage, and deploy AI agents using the OpenLedger proxy infrastructure.
Python Integration
This example demonstrates how developers can connect to OpenLedger-hosted models using the OpenAI Python SDK.
Key Usage
Configure the base_url with your OpenLedger proxy endpoint.Authenticate requests using your api_key.Define the complete model path, including adapter details and versioning.
This approach is ideal for backend systems, automation pipelines, and Python-based AI applications.
Curl Integration
A lightweight HTTP-based integration method for environments where SDKs are unnecessary or unavailable.
Key Usage
Send a POST request with the required Content-Type and Authorization headers.Include the model path and message payload directly within the request body.
Best suited for quick testing, command-line workflows, and automated scripting environments.
JavaScript / Node.js Integration
Integrate OpenLedger-hosted models seamlessly into frontend applications or Node.js services using the OpenAI JavaScript client.
Key Usage
Initialize the client with your API credentials and baseURL.Use the chat.completions.create() method to interact with deployed models.Fully supports modern async/await workflows.
Recommended for web apps, AI assistants, bots, and browser-compatible services.
Completion
OpenLedger enables users to participate across the full decentralized AI lifecycle by allowing them to:
Create and contribute to DatanetsTrain, fine-tune, and deploy AI modelsInteract with AI systems while earning through tokenized engagementParticipate in governance and ecosystem decision-making
Every interaction and contribution is recorded on-chain, ensuring transparency, verifiable ownership, and decentralized collaboration throughout the AI ecosystem.
#OpenLedger @OpenLedger
$OPEN

Bridging
@OpenLedger leverages the OP Stack Standard Bridge, deployed through AltLayer, its Rollup-as-a-Service (RaaS) infrastructure partner. This integration enables secure and standardized interoperability with Ethereum while maintaining full alignment with the wider OP Stack ecosystem.
Key Features
Open-Source & Standardized Infrastructure
The OP Stack bridge framework, built on the Bedrock architecture, is fully open source, extensively audited, and widely adopted across multiple OP Stack rollups. Core components such as OptimismPortal, L1StandardBridge, L2StandardBridge, and CrossDomainMessenger serve as canonical contracts reused by networks including Base, Mode, and Zora.
OPEN Token as Native Gas Asset
OpenLedger utilizes the OPEN ERC-20 token on Ethereum as the native gas token for its Layer 2 network. During the bridging process on testnet, OPEN tokens are first locked within the OptimismPortal contract on Sepolia and then minted on L2 through the standard OP Stack deposit mechanism. Although OptimismPortal is commonly associated with ETH deposits, OpenLedger securely extends its functionality for OPEN token bridging without modifying the underlying canonical bridge design.
Secure Mint-and-Burn Bridging Model
Asset transfers follow the proven OP Stack mint-and-burn architecture. Tokens deposited on L1 are securely escrowed through OptimismPortal, while equivalent OPEN tokens are minted on L2 after deposit confirmation. For withdrawals, OPEN tokens are burned on L2 and subsequently released back on L1.
Seamless Ethereum Ecosystem Compatibility
OpenLedger maintains full compatibility with widely used Ethereum tools and wallets, including MetaMask, Ledger, Hardhat, and viem, ensuring a familiar developer and user experience consistent with other OP Stack-based rollups.
Audited Security Framework
The bridge infrastructure — including Ethereum token contracts, Layer 2 representations, and canonical bridge components — has undergone multiple security audits by leading firms such as OpenZeppelin and Trail of Bits. Since OpenLedger introduces no custom changes to the core bridge architecture, it inherits the established security assurances of the OP Stack ecosystem.
#OpenLedger
#TrumpSaysIranDealLargelyNegotiated #RussiaExpandsMinerInfoRequirements #StablRDepegsAfterAttack
$OPEN $BTC

ModelFactory is a user-friendly fine-tuning platform for Large Language Models (LLMs) within the OpenLedger ecosystem. Unlike conventional fine-tuning solutions that rely on APIs or command-line setups, ModelFactory offers a fully GUI-based experience, making model customization accessible to everyone. It enables users to fine-tune AI models using permissioned and verified datasets approved through @OpenLedger
Core Concepts: How ModelFactory Works
ModelFactory combines secure dataset management with AI model fine-tuning in a streamlined and user-friendly workflow, ensuring both data protection and ownership transparency. The platform operates through several key components:
Model Selection & Setup
Users can choose from multiple leading LLMs such as LLaMA, Mistral, and DeepSeek. Training parameters including learning rate, batch size, and training epochs can be adjusted directly through the graphical interface without requiring coding knowledge.
Fine-Tuning Engine
ModelFactory supports efficient fine-tuning techniques like LoRA and QLoRA, enabling faster and more resource-efficient model customization. Integrated real-time dashboards allow users to monitor training performance, progress, and optimization metrics throughout the process.
Interactive Chat Interface
The platform includes a built-in chat environment where users can test and interact with their fine-tuned models instantly through the GUI or via API access. This supports live Q&A experiences, workflow automation, and specialized task interactions in real time. #OpenLedger #SECHaltsInnovationExemption #ECBOpposesEuroStablecoinExpansion #USDCCirculationUp400MWeekly
$OPEN $BTC $AI