# Gensyn ## Home - [Home](https://docs.gensyn.ai/home.md): Welcome to the official Gensyn docs. - [The Gensyn Protocol](https://docs.gensyn.ai/the-gensyn-protocol.md): An introduction to Gensyn. - [Core Components](https://docs.gensyn.ai/core-components.md): Learn about the four core components that make up the Gensyn protocol. - [Products & Research](https://docs.gensyn.ai/products-and-research.md): Read about Gensyn's research initiatives, projects, and products. - [Get Started](https://docs.gensyn.ai/get-started.md): Start getting involved in the Gensyn ecosystem and contribute to the Testnet today. ## Tech - [Reproducible Execution Environment (REE)](https://docs.gensyn.ai/tech/ree.md): Run AI model inference in a machine-agnostic environment where the same model and inputs produce the same outputs across supported hardware. - [Get Started](https://docs.gensyn.ai/tech/ree/get-started.md): Install REE, launch the TUI, and run your first reproducible generation. - [Using the TUI](https://docs.gensyn.ai/tech/ree/using-the-tui.md): Select models, set prompts, tune parameters, and interpret the output inside of the TUI. - [Receipts](https://docs.gensyn.ai/tech/ree/receipts.md): Understand what receipts prove, what they contain, and how to validate them. - [Supported Models](https://docs.gensyn.ai/tech/ree/supported-models.md): A list of models verified to work with REE. - [Examples](https://docs.gensyn.ai/tech/ree/examples.md): Short, practical recipes for the most common REE workflows. - [Advanced Usage & CLI Reference](https://docs.gensyn.ai/tech/ree/advanced-usage.md): Skip the TUI and work directly with the SDK CLI, pipeline stages, and container configuration. - [Internals](https://docs.gensyn.ai/tech/ree/advanced-usage/internals.md): How the Gensyn Compiler and RepOp kernels achieve bitwise reproducibility under the hood. - [Troubleshooting](https://docs.gensyn.ai/tech/ree/troubleshooting.md): Common errors, their causes, and how to fix them. - [Delphi SDK](https://docs.gensyn.ai/tech/delphi-sdk.md): TypeScript SDK for interacting with Delphi information markets on the Gensyn blockchain. - [Configuration](https://docs.gensyn.ai/tech/delphi-sdk/configuration.md): Installation, environment variables, signing modes, and network defaults. - [API Reference](https://docs.gensyn.ai/tech/delphi-sdk/api.md): REST API methods for listing markets, fetching market details, and querying wallet positions. - [On-Chain Methods](https://docs.gensyn.ai/tech/delphi-sdk/methods.md): Trading, quoting, token approvals, and direct Gateway contract interaction via RPC. - [Subgraph](https://docs.gensyn.ai/tech/delphi-sdk/subgraph.md): Querying historical on-chain event data through the Goldsky-indexed GraphQL endpoint. - [Agentic Trading Toolkit (ATT)](https://docs.gensyn.ai/tech/agentic-trading.md): A toolkit that enables AI agents to browse, trade, and manage positions on Delphi through natural language. - [Get Started](https://docs.gensyn.ai/tech/agentic-trading/get-started.md): Clone the repo, install the skill, configure your environment, and start interacting with your agent. - [Creating & Funding Wallets](https://docs.gensyn.ai/tech/agentic-trading/creating-and-funding-wallets.md): How to fund your wallet with ETH and USDC for trading on Delphi via the Agentic Trading Toolkit. - [Usage Guide](https://docs.gensyn.ai/tech/agentic-trading/usage-guide.md): What you can ask the agent to do, example prompts, and available scripts. - [Troubleshooting](https://docs.gensyn.ai/tech/agentic-trading/troubleshooting.md): Covers common errors, environment variable issues, wallet problems, and slippage failures. - [Agent eXchange Layer (AXL)](https://docs.gensyn.ai/tech/agent-exchange-layer.md): What AXL is, what it does, and what you can build with it. - [Get Started](https://docs.gensyn.ai/tech/agent-exchange-layer/get-started.md): Instructions on how to get started with AXL. - [Configuration](https://docs.gensyn.ai/tech/agent-exchange-layer/configuration.md): CLI flags, node settings, and example configurations. - [How it Works](https://docs.gensyn.ai/tech/agent-exchange-layer/how-it-works.md): In-depth information on AXL's architecture, encryption, peering, privacy model, wire format, and node internals. - [Building Applications & Examples](https://docs.gensyn.ai/tech/agent-exchange-layer/examples-and-building.md): Shipped examples and step-by-step patterns for building your own services with send/recv, MCP, and A2A, plus patterns for building your own applications on top of it. - [Troubleshooting](https://docs.gensyn.ai/tech/agent-exchange-layer/troubleshooting.md): Common build, peering, and runtime issues with their causes and fixes. ## Testnet - [Overview](https://docs.gensyn.ai/testnet/overview.md): A live, open test network for decentralised machine learning. - [RL Swarm (Paused)](https://docs.gensyn.ai/testnet/rl-swarm.md): RL Swarm lets anyone, anywhere, join and participate in a distributed reinforcement learning system that learns faster together than alone. - [How It Works](https://docs.gensyn.ai/testnet/rl-swarm/how-it-works.md): Learn how RL Swarm functions under the hood, from GenRL’s modular architecture to multi-agent learning, coordination, and reward cycles. - [CodeZero](https://docs.gensyn.ai/testnet/rl-swarm/how-it-works/codezero.md): Learn about CodeZero, the cooperative coding environment powering RL Swarm. - [Legacy Environments](https://docs.gensyn.ai/testnet/rl-swarm/how-it-works/legacy-environments.md): An archive of legacy RL Swarm environments, including Reasoning Gym, which has been replaced by CodeZero. - [Getting Started](https://docs.gensyn.ai/testnet/rl-swarm/getting-started.md): Browse resources for getting started with RL Swarm. - [Windows (WSL 2)](https://docs.gensyn.ai/testnet/rl-swarm/getting-started/windows-wsl-2.md): Spin up your node and participate in the swarm in a Windows (WSL 2) environment. - [Linux](https://docs.gensyn.ai/testnet/rl-swarm/getting-started/linux.md): Spin up your node and participate in the swarm in a Linux (Ubuntu 22.04+) environment. - [macOS](https://docs.gensyn.ai/testnet/rl-swarm/getting-started/macos.md): Spin up your node and participate in the swarm in a macOS environment. - [Node Management](https://docs.gensyn.ai/testnet/rl-swarm/node-management.md): Learn how to monitor, maintain, and control your RL Swarm node once it’s running. - [Troubleshooting](https://docs.gensyn.ai/testnet/rl-swarm/troubleshooting.md): Stuck? Get unblocked with RL Swarm on Windows (WSL 2), Linux, or macOS, or reach out to support for more help. - [BlockAssist (Paused)](https://docs.gensyn.ai/testnet/blockassist.md): BlockAssist is an interactive, AI-driven Minecraft environment where reinforcement learning agents learn from your actions. - [Getting Started](https://docs.gensyn.ai/testnet/blockassist/getting-started.md): Get up and running with BlockAssist and start training your own model. - [Windows (WSL 2)](https://docs.gensyn.ai/testnet/blockassist/getting-started/windows-wsl-2.md): Get up and running with BlockAssist on your Windows device with a WSL 2 environment. - [Linux](https://docs.gensyn.ai/testnet/blockassist/getting-started/linux.md): Get up and running with BlockAssist in your Ubuntu 22.04+ Linux environment. - [Running BlockAssist on a Remote Linux Desktop](https://docs.gensyn.ai/testnet/blockassist/getting-started/linux/running-blockassist-on-a-remote-linux-desktop.md): Learn how to run BlockAssist on a remote Linux environment using several types of devices. - [macOS](https://docs.gensyn.ai/testnet/blockassist/getting-started/macos.md): Get up and running with BlockAssist in your macOS environment. - [Using BlockAssist](https://docs.gensyn.ai/testnet/blockassist/using-blockassist.md): Use the platform-agnostic guide below to launch BlockAssist after installation, complete tasks, train your models, and upload them via Hugging Face. - [Running BlockAssist with Cursor](https://docs.gensyn.ai/testnet/blockassist/using-blockassist/running-blockassist-with-cursor.md): This guide shows non-technical users how to set up and run BlockAssist on macOS using Cursor, an AI-powered code editor with an integrated chat assistant. - [Hugging Face Guide](https://docs.gensyn.ai/testnet/blockassist/hugging-face-guide.md): Follow this quick tutorial to sign up for Hugging Face and generate a private token for use with BlockAssist. - [CodeAssist (Paused)](https://docs.gensyn.ai/testnet/codeassist.md): CodeAssist is an AI assistant that learns from every edit, keystroke, and solution, adapting to your style and training itself to become the perfect coding partner for you. - [Getting Started](https://docs.gensyn.ai/testnet/codeassist/getting-started.md): Get up and running with CodeAssist and start training local models based to code like you. - [Windows (WSL 2)](https://docs.gensyn.ai/testnet/codeassist/getting-started/windows-wsl-2.md): Get up and running with CodeAssist in your Windows (WSL 2) environment. - [Linux](https://docs.gensyn.ai/testnet/codeassist/getting-started/linux.md): Get up and running with CodeAssist in your Linux (Ubuntu 22.04+) environment. - [macOS](https://docs.gensyn.ai/testnet/codeassist/getting-started/macos.md): Get up and running with CodeAssist in your macOS environment. - [Using CodeAssist](https://docs.gensyn.ai/testnet/codeassist/using-codeassist.md): Read the end-to-end CodeAssist user guide and start training your own personal coding model. - [Hugging Face Guide](https://docs.gensyn.ai/testnet/codeassist/hugging-face-guide.md): Follow this quick tutorial to sign up for Hugging Face and generate a private token for use with CodeAssist. - [Troubleshooting](https://docs.gensyn.ai/testnet/codeassist/troubleshooting.md): Stuck? Get unblocked with CodeAssist on Windows (WSL 2), Linux, or macOS, or reach out to support for more help. ## Litepaper (legacy) - [Litepaper](https://docs.gensyn.ai/litepaper/litepaper.md): The hyperscale, cost-efficient compute protocol for the world’s deep learning models --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on a page URL with the `ask` query parameter: ``` GET https://docs.gensyn.ai/home.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.