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Showing 8 open source projects for "tools"
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1
Osaurus
AI edge infrastructure for macOS. Run local or cloud models
...Developers can extend the system through plugins that expose additional capabilities, tools, or services to the runtime using a structured plugin architecture. Osaurus also supports the Model Context Protocol, allowing tools and AI services to share context and interact with multiple applications simultaneously. -
2
Quotio
Stop juggling AI accounts. Quotio is a beautiful native macOS menu bar
...Quotio simplifies setup with one-click agent configuration, menu bar access to server status, and notifications for low quotas or connection issues. While targeted at developers with CLI-based AI tools, its visually clear UI and quota tracking make it a useful utility for anyone working with multiple AI APIs on macOS. -
3
CodexBar
Show usage stats for OpenAI Codex and Claude Code
CodexBar is a lightweight macOS utility that displays real-time usage statistics for AI coding tools such as OpenAI Codex and Claude Code directly from the system menu bar. The application is designed to give developers quick visibility into token consumption and activity without requiring them to open web dashboards or log into provider portals. Built in Swift with a native macOS interface, it integrates seamlessly into the desktop environment and emphasizes minimal overhead. ... -
4
Palmier Pro
macOS video editor built for AI
...The app is written from scratch in Swift and takes inspiration from professional editors like Premiere Pro while rethinking the workflow around AI. Users can generate videos and images inside the editor with models such as Seedance, Kling, and Nano Banana Pro. It also exposes an MCP server, so tools like Claude, Codex, and Cursor can interact with the project. The core editor, MCP server, and agent chat are open source, while generative AI processing remains closed source. It is designed specifically for macOS 26 on Apple Silicon. -
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5
apfel
Apple Intelligence from the command line
apfel is a lightweight and likely experimental development project focused on building efficient and minimal tools or frameworks, typically emphasizing simplicity, performance, and clean abstractions. The project appears to follow a philosophy of reducing unnecessary complexity while still enabling practical functionality for developers who prefer lean systems over heavy frameworks. It is designed to be adaptable, allowing developers to extend or modify its behavior depending on their specific use case. ... -
6
Swift for TensorFlow
Swift for TensorFlow
...By embedding machine learning functionality into the Swift compiler and language design, the project enables developers to write high-performance machine learning models while maintaining the readability and safety of modern programming practices. Swift for TensorFlow also introduces tools that allow developers to compute gradients automatically, which is essential for training neural networks through gradient-based optimization. -
7
Bender
Easily craft fast Neural Networks on iOS
...Bender suports the most common ML nodes and layers but it is also extensible so you can write your own custom functions. With Core ML, you can integrate trained machine learning models into your app, it supports Caffe and Keras 1.2.2+ at the moment. Apple released conversion tools to create CoreML models which then can be run easily. Finally, there is no easy way to add additional pre or post-processing layers to run on the GPU. -
8
Swift AI
The Swift machine learning library
...We recommend that you read the docs carefully for detailed instructions on using the various components of Swift AI. The example projects are another great resource for seeing real-world usage of these tools. Swift AI currently depends on Apple's Accelerate framework for vector/matrix calculations and digital signal processing.
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