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local-ai

Xybrid: AI on-device

Build AI-powered apps with Xybrid, for startup founders, with on-device AI capabilities.
intermediateโฑ 30 minutes๐Ÿ’ต Free
279 stars28 forksRustUpdated 7/10/2026100% free ยท open source
What it is

Build apps that work with on-device AI, using Xybrid.

What you can make with it

Apps with features like image recognition, text-to-speech, and language translation, e.g., a mobile app that translates foreign signs for a traveler when they show them to their phone, in real-time.

How it helps

Using Xybrid means you can create mobile apps with AI capabilities without worrying about relying on cloud services or dealing with complex AI server setup.

Real use case example

"A founder wants to create a new app that scans restaurant menus and recommends the best dishes, based on user ratings and dietary restrictions. They start by setting up a Rust development environment, create an Xybrid instance, and use its APIs to integrate on-device image recognition. After a few hours of development, they test the app and see that it can accurately identify menu items and provide personalized recommendations. The app is now ready to be released on the App Store."

If you're new

Start with Xybrid when you're new to AI development and want to build mobile apps with simple AI features.

If you're senior

Senior engineers turn to Xybrid when they need to build complex, on-device AI applications for enterprise clients or high-performance scenarios.

Common confusion cleared up

Note that Xybrid is designed for on-device AI, so it won't work for cloud-based AI services or large-scale data processing tasks.

Best inside these AI tools
Claude DesktopAny AI Client
Pairs with
Claude APIStripe webhookNotion database
Why we list it on WorkflowStacks: Xybrid is included here because it offers a free, open-source solution for building on-device AI apps.
What it does

Xybrid allows startup founders to build and deploy AI-powered apps that run directly on-device, enabling faster and more private processing of user data.

When to use it
  • โ€ขWhen you need to build an app that can perform complex AI tasks without requiring a constant internet connection
  • โ€ขWhen user data privacy is a top concern and you want to keep processing on-device
  • โ€ขWhen you're looking to reduce latency and improve the overall user experience of your app
Quick start
  1. 1Install the Xybrid framework using Rust by running the command `cargo add xybrid` in your terminal
  2. 2Import the necessary Xybrid modules into your Rust project and initialize the on-device AI engine
  3. 3Train and integrate your AI model using Xybrid's APIs and tools, then deploy it within your app
  4. 4Test and refine your app's AI-powered features to ensure seamless on-device performance
Ready-to-paste prompt
To get started with a basic image classification model, use the command `xybrid::init_model("image_classification")` in your Rust code
Saves to your device

Topics

ai-games
edge-ai
ios
kotlin
llamacpp
llm
mobile-llm
ollama
on-device-ai
on-device-ml
onnx-runtime
privacy
privacy-tools
rust
swift
unity-ai
unity3d
voice-ai
voice-assistant
What's inside โ€” free to inspect
No purchase needed

Read the entire source before you build โ€” unlike paid marketplaces that hide it behind a buy button.

35
top-level files
16
folders
437.6M
repo size
Apache-2.0
license
Key files
AGENTS.md
MAINTAINERS.md
README.ja-JP.md
README.md
README.zh-CN.md
File tree
.agents/
.cargo/
.claude/
.github/
agents/
bazel/
bindings/
crates/
docs/
examples/
integration-tests/
macros/
spike/
tools/
vendor/
xtask/
.bazelignore
.bazelrc
.bazelversion
.gitignore
.gitmodules
AGENTS.md
BUILD.bazel
Cargo.lock
Quick Actions
Details
Creator
xybrid-ai
Language
Rust
Category
local-ai
Published
12/13/2025

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