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9 changes: 7 additions & 2 deletions README.md
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- **Local-first:** load AI models and perform inference on your own machine. No third-party APIs, SaaS, or cloud involved.
- **P2P:** build unstoppable internet systems — like BitTorrent, IPFS, and blockchain networks, but for AI.
- **Cross-platform:** consistent developer experience across hardware, operating systems, and JS runtime environments — write code once, run it everywhere.
- **OpenAI-compatible API:** integrate with the broader AI ecosystem.
- **Open source:** 100% free to use and modify — build on top, contribute back, be part of our community.

## Usage

QVAC is composed of JavaScript libraries and tools that converge in the JS SDK. _The SDK is the main entry point for using QVAC_. It is type-safe and exposes all QVAC capabilities through a unified interface. It runs on Node.js, [Bare runtime](https://bare.pears.com), and [Expo](https://expo.dev).

Additionally, QVAC provides a CLI with tools and an HTTP server that exposes an [**OpenAI-compatible API**](https://platform.openai.com/docs/api-reference). *By implementing the OpenAI API format, QVAC can integrate with the broader AI ecosystem.*

Install the `@qvac/sdk` npm package in your project. Then load models and run AI inference locally, or delegate inference to peers using the built-in P2P features.

### Quickstart
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> [!TIP]
> For comprehensive QVAC documentation, see [https://docs.qvac.tether.io](https://docs.qvac.tether.io).
> There, you'll find [the compatibility matrix, installation instructions per environment/platform](https://docs.qvac.tether.io/sdk/install/), [reference with code examples for using each functionality](https://docs.qvac.tether.io/sdk/), and much more.
> There, you'll find [the compatibility matrix, installation instructions per environment/platform](https://docs.qvac.tether.io/sdk/getting-started/installation/), [reference with code examples for using each functionality](https://docs.qvac.tether.io/sdk/getting-started/), and much more.

## Contributing

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| lib-decoder-audio | Audio decoder library leveraging FFmpeg for efficient audio decoding as preprocessing step for other addons | Addon |
| lib-infer-llamacpp-embed | Native C++ addon for running text embedding models to generate high-quality contextual embeddings | Addon |
| lib-infer-llamacpp-llm | Native C++ addon for running Large Language Models (LLMs) within QVAC runtime applications | Addon |
| lib-infer-diffusion | Native C++ addon for text-to-image and image-to-image generation using stable-diffusion.cpp | Addon |
| lib-infer-nmtcpp | Library for running various translation models supporting OPUS, Bergamot, and IndicTrans backends | Addon |
| lib-infer-onnx | Bare addon for ONNX Runtime session management | Addon |
| lib-infer-onnx-tts | Text-to-Speech (TTS) library using Chatterbox and Supertonic neural TTS model via ONNX Runtime | Addon |
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| lib-infer-base | Base class for inference addon clients defining common lifecycle and generic methods for model interaction | Core |
| logging | Logger wrapper that normalizes logging interface across QVAC libraries | Core |
| cli | Command-line interface for the QVAC ecosystem with tooling for building, bundling, and managing QVAC-powered applications | Tool |
| lib-diagnostics | Diagnostic report generation library for QVAC | Tool |
| lib-registry-server | Distributed model registry for downloading AI models for local inference and contributing new models | Tool |
| lint-cpp | Configuration files for formatting and linting C++ source files with pre-commit hooks | Tool |

### Development

- For the standard development workflow used in this monorepo, see [`/docs/gitflow.md`](gitflow.md).
- For the standard development workflow used in this monorepo, see [`/docs/gitflow.md`](./docs/gitflow.md).
- For development specifics of each QVAC component, refer to the documentation in the respective subdirectory under `/packages`.
- For the QVAC architecture as a whole, see `/docs/architecture`.
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