Search Results for "cpu performance meter"

DebugSwift is a comprehensive open-source toolkit aimed at iOS developers that streamlines and enhances the debugging experience for Swift-based applications by providing a rich set of interactive diagnostic tools. Designed to operate within the iOS environment (iOS 14+ and Swift 6+), it includes network and WebSocket inspectors for HTTP requests, detailed performance metrics (CPU, memory, FPS), and mechanisms to detect memory leaks and main thread violations in real time. The toolkit also provides developers with crash analysis features, console log monitoring, device and build information displays, and customizable debugging panels, all of which make it easier to troubleshoot edge cases and unexpected behavior. ...

Nativ is a native macOS workspace for running AI models locally on Apple silicon. It combines private chat, model management, image generation, voice tools, monitoring, and local inference serving in one SwiftUI application. The app discovers compatible MLX models in the Hugging Face cache and can download additional models with memory-fit warnings. Conversations support streaming responses, images, reasoning output, persistent history, and performance metrics. Nativ exposes OpenAI- and...

With the AR MultiPendulum app, I created the world’s first AR headset experience that does not rely on expensive specialized hardware. Using Google Cardboard, a standard iPhone, and video footage from the phone’s camera, the app creates an AR headset experience by rendering the user’s surroundings in VR. Augmented reality (AR) is a technology that overlays virtual objects over the real world. For years, AR experiences have been available on smartphones through handheld AR apps. In 2016,...

...The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.