# Arbitrum Orbit Overview

Arbitrum Orbit is an Optimistic rollup-based framework designed to empower web3 businesses by enabling the creation of custom, use case-specific Layer 2 (L2) or Layer 3 (L3) chains in a purely permissionless way. Orbit leverages the Arbitrum Nitro Tech stack, offering unparalleled scalability, advanced compression, full EVM compatibility, and soon-to-be-released cross-chain interoperability. Essentially, Arbitrum Orbit can be thought of as deployable and configurable instances of the Nitro stack, forming an ecosystem of independent chains.

## Key Features of Arbitrum Orbit

1. Customizable Throughput: Orbit chains provide dedicated throughput, ensuring high performance and resource availability tailored to specific dApp requirements.
2. EVM+ Compatibility: Support for multiple programming languages (Rust, C++, C, and Solidity) through Stylus, enabling flexible and cost-effective smart contract development.
3. Predictable Gas Costs: Isolated transaction environments ensure stable and predictable gas fees, crucial for business cost forecasting.
4. Broad Data Availability Options: Flexibility to choose data availability models, including Ethereum Layer 1 or Data Availability Committees (DACs) for off-chain storage.
5. Robust Security: Leveraging Ethereum's security and the Arbitrum Nitro tech stack ensures a high level of security for Orbit chains.

By choosing Arbitrum Orbit, Open Campus leverages a powerful, flexible, and scalable blockchain solution that meets our unique needs. This partnership enables us to build an innovative educational platform that redefines the Learn-to-Earn model, offering unparalleled benefits to our users.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://devdocs.educhain.xyz/educhain/arbitrum-orbit-overview.md?ask=<question>
```

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.
